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  • Digital Humanities and Cultural Heritage
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  • DRYAD

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Gandon, Enora; Nonaka, Tetsushi; Endler, John; Coyle, Thelma; +1 Authors

    Potters from three communities were instructed to faithfully reproduce four different model shapes with the thinnest walls possible using two different quantities of clay (0.75 kg or 2.25 kg), giving a total of eight experimental conditions. The four shapes (referred to as Cylinder, Bowl, Sphere, and Vase, respectively) were presented as pictures without providing any indication of the absolute dimensions to be produced. The four shapes were not part of any of the potters’ daily repertoire. Potters produced five specimens for each of the eight vessel types. The experimental sessions were video-recorded under standardized conditions using a Panasonic NV-GS320 camcorder. The camera was fixed on a tripod with lens orientation centered on the vertical rotation axis of the wheel. The camera was positioned at a height of 30 cm above the level of the wheel at a horizontal distance of 4-6 m. The lower edge of the video scene was aligned with the center of the wheel. The zoom was adapted to fully cover a 36-cm high by 42-cm wide calibration object (inverted T-shape) placed on the wheel at the start of each recording. For each trial, the images of the clay body profile after each fashioning gesture were extracted from the video frames (image resolution: 720 x 576 pixels; video sampling frequency: 25 fps). The first image captured the profile immediately following the (centering and opening) pre-forming phase and the last image captured the final profile; the intervening images captured the intermediate profiles during form development. This succession of profiles captured the vessel’s morphogenesis. The overall duration of the forming process was also analyzed. From the images, we extracted the 2D coordinates of the right half of the cross-sectional profiles by tracing them out on a Cintiq 21UX Wacom (Kazo, Japan) tablet with an integrated screen. The profile coordinates were converted from pixels to centimeters using a calibration factor obtained from the digitized dimensions of the calibration object. The profiles were re-sampled to generate an equal number of points at regular height intervals along the vertical (Y) axis and the resulting coordinates were smoothed with a low-pass filter. Because wheel-thrown vessels are typically axisymmetric, profiles were subsequently converted to full pot outlines by multiplying the horizontal (X) coordinates by -1 to create the corresponding left edge. Of the total 840 vessels thrown (twenty-one potters, each throwing five specimens of eight different vessel types), 12 vessels were not included due to problems with the video recording. # Data from: cultural attraction in pottery practice: group-specific shape transformations by potters from three communities --- Experimental data used in "Cultural attraction in pottery practice: group-specific shape transformations by potters from three communities". Methodological Information: see the manuscript methods section or the methods section of the Dryad dataset (DOI:10.5061/dryad.9ghx3ffpv) for details. Briefly, the dataset contains the two-dimensional outlines of the clay form as a function of time extracted from the video recordings of a cross-cultural experiment with 21 potters from three different cultural backgrounds (French and two Indian communities--Prajapati and Multani Kumhar). ## Description of the Data and file structure ## Summary Metrics * File count: 1 * Total file size: 713 MB * File formats: .mat ## Table of Contents * Pottery_Morphogenesis_Data.mat ## Setup * Unpacking instructions: n/a * Recommended software/tools: Matlab version 2023b. --- ## File/Folder Details ## Details for: Pottery\_Morphogenesis\_Data.mat * Description: Data of the two-dimensional outlines of the clay form as a function of time extracted from the video recordings of a cross-cultural experiment with 21 potters from 3 different cultural backgrounds (French and two Indian communities--Prajapati and Multani Kumhar) replicating 5 specimens of 4 model vessel types (cylinder, bowl, sphere, and vase) with 2 different quantities of clay (0.75 kg and 2.25 kg). * Format(s): .mat * Size(s): 713 MB * The table array "Pottery_Data" stores the following variables for each trial: * Potter: Potter ID and community (e.g., 'French9', 'Prajapati6'). * Type: The name of the model vessel type replicated and mass of clay used (e.g., 'Cylinder0.75', 'Vase2.25'). * Trial: Trial number (1-5). * Outline: Cell arrays that store multiple outlines (first column: the onset of the forming phase (t0), last column: the end of the forming phase) from each trial. Each cell contains 2D outline data extracted from video images of the clay body profile following each fashioning gesture. * Time: Cell arrays that store time stamps corresponding to multiple outlines stored in the column "Outline" (first column: the onset of the forming phase (t0), last column: the end of the forming phase) from each trial. Pottery is a quintessential indicator of human cultural dynamics. Cultural alignment of behavioral repertoires and artifacts has been considered to rest upon two distinct dynamics: selective transmission of information and culture-specific biased transformation. In a cross-cultural field experiment, we tested whether community-specific morphological features of ceramic vessels would arise when the same unfamiliar shapes were reproduced by professional potters from three different communities who threw vessels using wheels. We analyzed the details of the underlying morphogenesis development of vessels in wheel throwing. When expert potters from three different communities of practice were instructed to faithfully reproduce common unfamiliar model shapes that were not parts of the daily repertoires, the morphometric variation in the final shape was not random; rather, different potters produced vessels with more morphometric variation among than within communities, indicating the presence of community-specific deviations of morphological features of vessels. Furthermore, this was found both in the final shape and in the underlying process of morphogenesis; there was more variation in the morphogenetic path among than within communities. These results suggest that the morphological features of ceramic vessels produced by potters reliably and nonrandomly diverge among different communities.

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    DRYAD; ZENODO
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Liebovitch, Larry; Powers, William; Shi, Lin; Chen-Carrel, Allegra; +2 Authors

    # NLP and Machine Learning to Measure Peace from News Media [Dataset]. Dryad. https://doi.org/10.5061/dryad.2v6wwpzv6 PLOS ONE https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0292604 Authors * Larry S. Liebovitch; Department of Physics, Queens College, City University of New York, New York, New York, United States of America and Advanced Consortium on Cooperation, Conflict, and Complexity, Columbia University, New York, New York, United States of America * William Powers; Department of Physics, Queens College, City University of New York, New York, New York, United States of America * Lin Shi; Department of Physics, Queens College, City University of New York, New York, New York, United States of America * Allegra Chen-Carrel; School of Management, University of San Francisco, San Francisco, California, United States of America * Philippe Loustaunau; Vista Consulting, LLC, Arlington, Virginia, United States of America. * Peter T. Coleman; Advanced Consortium on Cooperation, Conflict, and Complexity, Columbia University, New York, New York, United States of America and Teachers College, Columbia University, New York, New York, United States of America Abstract "Hate speech" can mobilize violence and destruction. What are the characteristics of “peace speech” that reflect and support the social processes that maintain peace? In this study we used a data driven, machine learning approach to identify the words most associated with lower-peace versus higher-peace countries. Logistic regression and random forest classifiers were trained using five respected, traditional peace indices: Global Peace Index, Positive Peace Index, World Happiness Index, Fragile States Index, and Human Development Index. The feature inputs into the machine learning model were the word frequencies from the news media in each country and the output classifications were the level of peace in that country. The machine learning model was successful in properly classifying the level of peace from the news media in a country (both accuracy and F1: 96% - 100%). We also used that trained machine model to create a machine learning peace index that measured the level of peace in countries, including countries not in the training set, which correlated with the average of those five traditional peace indices (r-squared = 0.8349). Using the random forest feature importance method we found that the words in news media in lower-peace countries were characterized by words related to government, order, control and fear (such as government, state, law, security and court), while higher-peace countries were characterized by an increased prevalence of words related to optimism for the future and fun (such as time, like, home, believe and game). The detailed analysis and results based on this data set are published in the article in PLOS ONE https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0292604 ## Description of the data and file structure The starting point of our work used the NOW, News on the Web corpus https://www.english-corpora.org/now/ because it has a large amount of news media data on a large range of different topics, including on-line newspaper and magazine articles about accidents, business, crime, education, the arts, government, healthcare, law, literature, medicine, politics, real estate, religion, sports, war, as well as book, music, and movie reviews. A small sample of these sources in the United States include: AlterNet, Austin American-Statesman, Business Insider, Business Wire (press release), Chicago Tribune, FOX43.com, Jerusalem Post, Israel News, KCCI Des Moines, Kentwired, KOKI FOX 23, POWER magazine, Press of Atlantic City, The Jewish Press, USA TODAY, and Vulture. We analyzed data over the time period January 2010 through September 2020. In order to optimize this data for machine learning required natural language processing to substantially transform the NOW data so that the training algorithms would be focused on the most important elements and less sensitive to extraneous elements in the data. The programs to do this were developed as part of a Capstone project by MS students in Data Science at the Columbia Data Science Institute: Jinwoo Jung, Hyuk Joon Kwon, Hojin Lee, Tae Yoon Lim, and Matt MacKenzie as advised by Peter T. Coleman, Allegra Chen-Carrel, and Larry S. Liebovitch and are posted at [https://github.com/mbmackenzie/power-of-peace-speech](https://github.com/mbmackenzie/power-of-peace-speech). This processing consisted of four steps: 1. General text pre-processing: Removing non-word data such as: html tags like p and h and symbols such as {}, <>, , \\n and @. 2. Removing phrases not related to the article's content, such as inducing readers to subscribe and suggested links to other articles which were identified by 5-gram and cosine similarity to find those repeated phrases from each publisher. 3. Removing words (called "stop words" in nlp) such as "a", "the", "and", likely to be similar to both lower-peace and higher-peace countries so that the machine learning algorithms would be more focused on the differences between lower-peace and higher-peace countries. Removing words (called "named entities" in nlp) such as proper names of people, places, and companies, that could be confounding variables that correlate with levels of peace, independent of the language itself. 4. Lemmatizing the words, reducing all forms of words to their stem roots, such as collapsing "walk", "walking", "walked" to one word, so that all forms of each word would count equally towards the total count of that word. The final data set, transformed by these methods, consisted of a total of 723,574 media articles having a total of 57,819,434 words. Usage notes:The data analyzed in our article is available in the data set file. 1. Each text csv file is from the news media in one country. Countries are identified by their 2 letter Alpha-2 country codes: https://www.iban.com/country-codes 2. Each row is one article from an on-line news media source in that country. 3. The first columns respectively identify the: * line number * article\_id * article\_title * publisher, year * article\_text (as modified by step #1 above) * country\_mention * domestic (TRUE=local publisher) 4. The following columns respectively have * article\_text\_Ngram (as additionally modified by step #2 above) * article\_text\_Ngram\_stopword (as additionally modified by step #3 above) * article\_text\_Ngram\_stopword\_lemmatize (as additionally modified by step #4 above) ## Sharing/Access information ArXiv preprint: https://arxiv.org/abs/2305.12537 PLOS ONE article: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0292604 ## Code/Software The Python programs used to do the four natural language processing steps described above and analyze that data in the article are available at * [https://github.com/mbmackenzie/power-of-peace-speech](https://github.com/mbmackenzie/power-of-peace-speech) * [https://github.com/wpqc21/ArticleClassifier/tree/main/ArticleClassifierHSSC](https://github.com/wpqc21/ArticleClassifier/tree/main/ArticleClassifierHSSC) * [https://github.com/smilelinnn/Article-Classification](https://github.com/smilelinnn/Article-Classification) The starting point of our work used the NOW, News on the Web corpus <https://www.english-corpora.org/now/> because it has a large amount of news media data on a large range of different topics, including on-line newspaper and magazine articles about accidents, business, crime, education, the arts, government, healthcare, law, literature, medicine, politics, real estate, religion, sports, war, as well as book, music, and movie reviews. A small sample of these sources in the United States include: AlterNet, Austin American-Statesman, Business Insider, Business Wire (press release), Chicago Tribune, FOX43.com, Jerusalem Post, Israel News, KCCI Des Moines, Kentwired, KOKI FOX 23, POWER magazine, Press of Atlantic City, The Jewish Press, USA TODAY, and Vulture. We analyzed data over the time period January 2010 through September 2020. In order to optimize this data for machine learning required natural language processing to substantially transform the NOW data so that the training algorithms would be focused on the most important elements and less sensitive to extraneous elements in the data. The programs to do this were developed as part of a Capstone project by MS students in Data Science at the Columbia Data Science Institute: Jinwoo Jung, Hyuk Joon Kwon, Hojin Lee, Tae Yoon Lim, and Matt MacKenzie as advised by Peter T. Coleman, Allegra Chen-Carrel, and Larry S. Liebovitch and are posted at <https://github.com/mbmackenzie/power-of-peace-speech>. This processing consisted of four steps: 1. General text pre-processing: Removing non-word data such as: html tags like <p> and <h> and symbols such as {}, <>, \, \n and @. 2. Removing phrases not related to the article's content, such as inducing readers to subscribe and suggested links to other articles which were identified by 5-gram and cosine similarity to find those repeated phrases from each publisher. 3. Removing words (called "stop words" in nlp) such as "a", "the", "and", likely to be similar to both lower-peace and higher-peace countries so that the machine learning algorithms would be more focused on the differences between lower-peace and higher-peace countries. Removing words (called "named entities" in nlp) such as proper names of people, places, and companies, that could be confounding variables that correlate with levels of peace, independent of the language itself. 4. Lemmatizing the words, reducing all forms of words to their stem roots, such as collapsing "walk", "walking", "walked" to one word, so that all forms of each word would count equally towards the total count of that word. The final data set, transformed by these methods, consisted of a total of 723,574 media articles having a total of 57,819,434 words. USAGE NOTES The data analyzed in our article is available in the data set file. 1. Each text csv file is from the news media in one country. Countries are identified by their 2 letter Alpha-2 country codes: https://www.iban.com/country-codes 2. Each row is one article from an on-line news media source in that country. 3. The first columns respectively identify the: line number article_id article_title publisher, year article_text (as modified by step #1 in the Methods) country_mention domestic (TRUE=local publisher) 4. The following columns respectively have article_text_Ngram (as additionally modified by step #2 in the Methods) article_text_Ngram_stopword (as additionally modified by step #3 in the Methods) article_text_Ngram_stopword_lemmatize (as additionally modified by step #4 in the Methods) “Hate speech” can mobilize violence and destruction. What are the characteristics of “peace speech” that reflect and support the social processes that maintain peace? In this study we used a data driven, machine learning approach to identify the words most associated with lower-peace versus higher-peace countries. Logistic regression and random forest classifiers were trained using five respected, traditional peace indices: Global Peace Index, Positive Peace Index, World Happiness Index, Fragile States Index, and Human Development Index. The feature inputs into the machine learning model were the word frequencies from the news media in each country and the output classifications were the level of peace in that country. The machine learning model was successful in properly classifying the level of peace from the news media in a country (both accuracy and F1: 96% - 100%). We also used that trained machine model to create a machine learning peace index that measured the level of peace in countries, including countries not in the training set, which correlated with the average of those five traditional peace indices (r-squared = 0.8349). Using the random forest feature importance method we found that the words in news media in lower-peace countries were characterized by words related to government, order, control and fear (such as government, state, law, security and court), while higher-peace countries were characterized by an increased prevalence of words related to optimism for the future and fun (such as time, like, home, believe and game).

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ DRYAD; ZENODOarrow_drop_down
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    DRYAD; ZENODO
    Dataset . 2023
    License: CC 0
    Data sources: Datacite; ZENODO
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      DRYAD; ZENODO
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    Authors: Segessenman, Daniel; Peters, Shanan;

    # Supplementary Data for Transgression-regression cycles drive correlations in Ediacaran-Cambrian rock and fossil records [https://doi.org/10.5061/dryad.xwdbrv1k9](https://doi.org/10.5061/dryad.xwdbrv1k9) All supplementary data files for this study, including R-scripts for analyses, an animation of fossil and stratigraphic column locations through time, tables of rock units matched to fossil occurrences, tables of fossil occurrence assigned ages, and correlations for Ediacaran and Cambrian rock and fossil quantities as separate time periods are included in this Dryad repository. ## Description of the data and file structure Supplementary Figure, Tables, and captions. Highlighting of cells in Table S3, S4, and S5 are to indicate statistical significance at different confidence levels. Green highlighting indicates a correlation that is significant at the 95% level, and yellow indicates a correlation that is significant at the 90% level. No coloring indicates correlations that are not statistically significant. ***Non-Code Supplementary Materials Include***: 2. **Figure S1 (Figure S1 - Animated E-Cm rock & fossil distributions.gif)**: .gif animated map (5 myr bins) of Ediacaran-Cambrian rock and fossil locations on North America 3. **Table S1 (Supplementary Table S1 - Matched Macrostrat units and occurrence counts.csv)**: Macrostrat unit\_id's, strat names, counts of matched PBDB occurrences, and the modeled top and bottom ages of strat units 4. **Table S2 (Supplementary Table S2 – PBDB matched occurrence ages)**: table of Ediacaran-Cambrian PBDB occurrences their PBDB formations/ages and their matched Macrostrat formations/ages. ***NOTE***: cells with 'null' values in this table are cells that were empty (had no value recorded) when pulled from the Paleobiology Database's API. 5. **Table S3 (Supplementary Table S3 Ediacaran-Cambrian Rock-Fossil 1st Difference Correlations.xlsx)**: Correlation coefficients and p-values of 1st differences for Ediacaran-cambrian rock and fossil quantities. 6. **Table S4 (Supplementary Table S4 Cambrian Rock-Fossil Correlations.xlsx)**: Correlation coefficients and p-values of raw rock and fossil quantities for only the Cambrian 7. **Table S5 (Supplementary Table S5 Ediacaran Rock-Fossil Correlations.xlsx)**: Correlation coefficients and p-values for rock and fossil quantities from only the Ediacaran 8. **Supplementary Figure and Table Captions** ## Sharing/Access information API calls used to collect data from the following publicly accessible data platforms are contained within the R-script files found within this supplementary data repository. ***Links to other publicly accessible locations of the data***: * [https://macrostrat.org](https://macrostrat.org) * [https://paleobiodb.org](https://paleobiodb.org) ## Code/Software 1. Supplement S1a-h: Data files and R scripts that produce the panels for all figures and tables - Note that all .csv files included should be ***File-names are***: - S1a\_PBDB\_Collections\_and\_Macrostrat\_column\_Map\_plotter\_(Figure\_1).R - S1b\_PBDB\_occurrences\_and\_Macrostrat\_rock\_quantity\_time\_series\_(Figures\_2-5).R - S1c\_PBDB\_fossils\_and\_Macrostrat\_rock\_correlations\_astrochron\_(Table\_1).R - S1d\_NA Ediacaran Polygons 5-6-2021 v4.csv \- S1e\_pbdb\_data\_ed\_matched\_v7\.csv - S1f\_pbdb\_data\_cm\_matched\_v7.csv - S1g\_pbdb\_ed\_all\_non\_NA\_v1\_8-21-23.csv - S1h\_pbdb\_cm\_all\_non\_NA\_v1\_8-21-23.csv Strata of the Ediacaran Period (635-538.8 Ma) yield the oldest known fossils of complex, macroscopic organisms in the geologic record. These “Ediacaran-type” macrofossils (known as the Ediacaran biota) first appear in mid-Ediacaran strata, experience an apparent decline through the terminal Ediacaran, and directly precede the Cambrian (538.8-485.4 Ma) radiation of animals. Existing hypotheses for the origin and demise of the Ediacaran biota include: changing oceanic redox states, biotic replacement by succeeding Cambrian-type fauna, and mass extinction driven by environmental change. Few studies frame trends in Ediacaran and Cambrian macroevolution from the perspective of the sedimentary rock record, despite well-documented Phanerozoic covariation of macroevolutionary patterns and sedimentary rock quantity. Here we present a quantitative analysis of North American Ediacaran–Cambrian rock and fossil records from Macrostrat and the Paleobiology Database. Marine sedimentary rock quantity increases nearly monotonically and by over a factor of five from the latest Ediacaran to the late Cambrian. Ediacaran–Cambrian fossil quantities exhibit a comparable trajectory and have strong (rs > 0.8) positive correlations with marine sedimentary area and volume flux at multiple temporal resolutions. Even so, Ediacaran fossil quantities are dramatically reduced in comparison to the Cambrian when normalized by the quantity of preserved marine rock. Although aspects of these results are consistent with the expectations of a simple fossil-preservation induced sampling bias, together they suggest that transgression-regression and a large expansion of marine shelf environments coincided with the diversification of animals during a dramatic transition that is starkly evident in both the sedimentary rock and fossil records. Two existing datasets, Macrostrat's database of rocks/stratigraphy and a subset of Paleobiology Database (PBDB) fossil occurrence data, were merged on the basis of their shared rock unit name field for Ediacaran-Cambrian age (635-485.4 Ma) rocks/fossils of North America. Once PBDB fossil occurrences were matched to Macrostrat rock units in time and space (and checked), the fossil occurrence age ranges were modified based on the Macrostrat provided age model of a given fossil occurrences' host rock. Time series of fossil occurrences were generated from this updated data. Correlation coefficients were calculated from the generated time series of fossil occurrences and rock quantities through the Ediacaran-Cambrian geologic time Periods.

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    Dataset . 2023
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    Authors: Muller, Antoine; Barsky, Deborah; Sala-Ramos, Robert; Sharon, Gonen; +3 Authors

    Spheroids are one of the least understood lithic items yet are one of the most enduring, spanning from the Oldowan to the Middle Palaeolithic. Why and how they were made remains highly debated. We seek to address whether spheroids represent unintentional by-products of percussive tasks or if they were intentionally knapped tools with specific manufacturing goals. We apply novel 3D analysis methods, including spherical harmonics and surface curvature, to 150 limestone spheroids from 'Ubeidiya (c.1.4Ma), presently the earliest Acheulean occurrence outside of Africa, to bring a new perspective to these enigmatic artefacts. We reconstruct the spheroid reduction sequence based on trends in their scar facets and geometry, finding that the spheroid makers at 'Ubeidiya followed a premeditated reduction strategy. During their manufacture, the spheroids do not become smoother, but they become markedly more spherical. They approach an ideal sphere, a feat that likely required a mental template and skilful knapping. Acheulean bifaces are currently thought to represent the earliest evidence of hominins imposing a premeditated, symmetrical shape on stone. With evidence of spheroids occurring before the Acheulean, the intentional production of a sphere-like object now represents the oldest evidence of hominins desiring and achieving intentional geometry and symmetry in stone.

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    DRYAD; ZENODO
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      DRYAD; ZENODO
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    Authors: Ebel, Erika; LeMoine, Genevieve; Darwent, Christyann; Darwent, John; +1 Authors

    One hundred and thirty specimens were chosen for peptide mass fingerprinting (PMF) to identify previously unknown osseous materials and their taxonomic origin. Identification of these specimens promotes a better understanding of osseous tool production. PMF involves the enzymatic digestion of proteins followed by Matrix Assisted Laser Desorption-Ionization Time of Flight Mass Spectrometry (MALDI) analysis of the resultant peptide mixture. In the case of bone and ivory, Type 1 collagen is the major constitutive protein, and for each mammalian source, the amino acid sequence of Type 1 collagen, albeit highly conserved, may have one or more amino acid substitutions. These substitutions change the mass of some peptides detected in the MALDI spectrum of the protein digest, which are then used as markers for specific mammalian sources. Markers from known reference materials are compared with those from unknown samples for identification. The ZooMS (PMF) method was a simplified procedure based on that described in Kirby (2013, 2019). Since there are no cystines in collagen, the cystine reduction and end capping steps were omitted. In addition, spectral quality was sufficient to provide confident identifications in all cases where collagen was detected, so no purification or fractionation was required. Some spectra (~15%) had weak or absent A and/or G markers, but those markers were not required for accurate identifications of these samples. In all cases, only a single sample was digested and analyzed. The first author sampled the specimens at the UC Davis Zooarchaeology Lab with sample sticks—polystyrene strips with fiber optic polishing film attached on two sides—which are used to abrade and entrap a small amount of material for analysis. A freshly laundered lab coat, KN95 or surgical facemask, and nitrile examination gloves were worn during handling of all osseous materials. To avoid cross-contamination, gloves were changed, and the workstation was thoroughly sterilized with 70% alcohol wipes between each sample. Samples were obtained by rubbing the object lightly in a circular motion for 10 seconds. After sampling, the tip of the sample stick containing the abrasive film and entrapped sample is cut off, placed in an Eppendorf tube, and forwarded for analysis. Polishing films (30um alumina or diamond particles, final size 2.5 X 2.5mm) were from Precision Fiber Products, Inc., www.precisionfiberproducts.com. Polystyrene strips (1.5 X 2.5 X 400mm) were from Walthers, www.walthers.com. Photographs of each specimen were taken with a DinoLite digital microscope to document the area sampled. The sampling site is generally invisible to the naked eye, and the procedure consumes a miniscule amount of each specimen. Thus, it is considered a minimally destructive sampling technique by most museum curators and conservators. Each specimen and Eppendorf tube was given a unique sample number. Additional samples from the same location were collected on three specimens (KNK912x300; KNK3930x1571, x1822) because they yielded low-quality spectra on the first sample run. These are considered “second passes”, in which collagen was sampled from the same location but from a deeper anatomical component of the specimen, such that a better quality and/or greater quantity of collagen could be obtained. PMF analysis of the 130 samples collected was then undertaken at the Northeastern University Mass Spectrometry Core Facility. Digestion: 60µL of 50mM ammonium bicarbonate (AMBI) were added to each sample (on the sample stick tip in a 600µL Eppendorf tube) and heated to 75°C for 60min. After cooling, 8µL Promega Sequence Grade trypsin (0.02µg/µL in 50mM AMBI) was added and digestion proceeded overnight at 37°C. MALDI analysis: 2µL of the digest were added to 20µL 40% acetonitrile (ACN), 0.1% trifluoroacetic acid (TFA) with saturated α-Cyano-4-hydroxycinnamic acid (CHCA) matrix. 0.65µL of the mixture was spotted onto the MALDI plate. Spectra were obtained with an Applied Biosystems/Sciex 5800 MALDI-TOF-TOF instrument operated in positive reflector mode. Calibration was done with a standard mixture of peptides: 757.3992Da, 1046.5418Da, 1296.6848Da, 1347.7354Da, 1619.8223Da, 2093.0867Da, 2465.1983Da, and 3147.4710Da. Spectra were coadditions of 1200 – 2000 laser shots. Acquired spectra were exported from Applied Biosystems Data Explorer software as text files and imported into mMass (Strohalm et al. 2010) for analysis. Spectra were manually inspected for markers. Markers used to identify mammalian sources are compiled from Buckley et al. (2009, 2014) and Kirby (2013). The site of Iita in northwestern Greenland had near-continual occupation from 1000 CE to the mid-20th century, which presents an unparalleled opportunity to examine diachronic changes in the use of marine mammals for food and raw material resources by Pre-Inuit (Late Dorset) and Inuit-Inughuit—two culturally distinct Indigenous groups. Limited wood in High Arctic environments necessitated the use of antler, bone, and ivory for tool making. Choices in the selection and processing of osseous material reflect changes in the way these two groups used the same resources. However, modification of osseous material during the tool-making process is often to such an extent that few diagnostic features remain for species identification. Here, we include Zooarchaeology by Mass Spectrometry (ZooMS) to identify typically indeterminate pieces of manufacturing debris from preserved collagen. The minimally destructive extraction technique using polishing films had a 99% success rate. The results suggest that the selection of raw materials from certain marine mammals, particularly narwhal, by toolmakers at Iita was more frequent than previously understood through traditional faunal analysis alone. There is a paucity of narwhal from the Pre-Inuit (Late Dorset) assemblage, which contrasts starkly with the high frequency of narwhal in the Inuit-Inughuit assemblage. We demonstrate the importance of combining the analysis of bone-tool manufacturing debris with traditional dietary faunal remains to improve our understanding of resource use in coastal environments. Data is provided in two formats, .msd and .txt files. The text files are organized so that they correspond to the data in Supplementary Table 1. Further details can be found in the README.md file. The .txt files can be accessed by various software programs to visualize the data. The .msd files can be viewed using the mMass software for those who have access. Alternatively, the .msd files can be read by using the packages MALDIQuant and MALDIQuantForeign in R. mMass Data Miner: an Open Source Alternative for Mass Spectrometric Data AnalysisStrohalm M, Hassman M, Košata B, Kodíček MRapid Commun Mass Spec 22 (6), 905-908 (2008)DOI: 10.1002/rcm.3444

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    ZENODO
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      ZENODO
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    Authors: Lundy, Jasmine;

    From the 9th to 14th centuries AD, Sicily experienced a series of rapid and quite radical changes in political regime, but the impact of these regime changes on the lives of the people that experienced them remains largely elusive within the historical narrative. We use a multi-faceted lipid residue approach to give direct chemical evidence of the use of 248 everyday domestic ceramic containers from Islamic and post-Islamic contexts in western Sicily to aid our understanding of daily habits throughout this period of political change. A range of commodities was successfully identified, including animal fats, vegetable products, fruit products, (potentially including wine), and plant resins. The study highlights the complexity of residues in Early Medieval Mediterranean society, as in many cases mixtures of commodities were observed, reflecting sequential cooking events and/or the complex mixtures reflective of medieval recipes. However, overall there were no clear changes in the composition of the residues following the imposition of Norman control over the island and through subsequent periods, despite some differences between urban centres and rural sites. Thus, lending to the idea that post-Islamic populations largely flourished and benefited from the agricultural systems, resources, and recipes left by their predecessors. This data set is comprised of data files produced by Gas Chromatography-Mass Spectrometry (GC-MS) of lipids extracted using acid extraction method from pottery sherds from 9th-14th century contexts in Sicily. These data are linked to the published journal where methods of extraction, the context of pottery and the interpretation of data are fully described. Each file corresponds to the sample name as recorded in S1 data and contains a usable cdf. file. The acquisition method for all files is given in TEXT format.

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    DRYAD; ZENODO
    Dataset . 2023
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      DRYAD; ZENODO
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    Authors: Gravis, David; Roy, Nicolas; Ruffini-Ronzani, Nicolas; Houssiau, Laurent; +3 Authors

    Book production by medieval scriptoria has gained growing interest in recent studies. In this context, identifying ink compositions and parchment animal species from illuminated manuscripts is of great importance. Here, we introduce time-of-flight secondary ion mass spectrometry (ToF-SIMS) as a non-invasive tool to identify both inks and animal skins in manuscripts, at the same time. For this purpose, both positive and negative ion spectra in inked and non-inked areas were recorded. Chemical compositions of pigments (decoration) or black inks (text) were determined by searching for characteristic ion mass peaks. Animal skins were identified by data processing of raw ToF-SIMS spectra using principal component analysis (PCA). In illuminated manuscripts from the fifteenth to sixteenth century, malachite (green), azurite (blue), cinnabar (red) inorganic pigments, as well as iron-gall black ink, were identified. Carbon black and indigo (blue) organic pigments were also identified. Animal skins were identified in modern parchments of known animal species by a two-step PCA procedure. We believe the proposed method will find extensive application in material studies of medieval manuscripts, as it is non-invasive, highly sensitive and able to identify both inks and animal skins at the same time, even from traces of pigments and tiny scanned areas. In-house PCA algorithm requires python. ToF-SIMS raw data require SurfaceLab software. ATR-FTIR raw data (.0) can be read with free-licence software (Fityk). XRD diffractograms are directly exported in .txt from .xyz files. Analytical and data processing methods can be found in the associated article.

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    DRYAD; ZENODO
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      DRYAD; ZENODO
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    Authors: Oh, Inez; Schindler, Suzanne; Ghoshal, Nupur; Lai, Albert; +2 Authors

    Objectives There is much interest in utilizing clinical data for developing prediction models for Alzheimer disease (AD) risk, progression, and outcomes. Existing studies have mostly utilized curated research registries, image analysis, and structured Electronic Health Record (EHR) data. However, much critical information resides in relatively inaccessible unstructured clinical notes within the EHR. Materials and Methods We developed a natural language processing (NLP)-based pipeline to extract AD-related clinical phenotypes, documenting strategies for success and assessing the utility of mining unstructured clinical notes. We evaluated the pipeline against gold-standard manual annotations performed by two clinical dementia experts for AD-related clinical phenotypes including medical comorbidities, biomarkers, neurobehavioral test scores, behavioral indicators of cognitive decline, family history, and neuroimaging findings. Results Documentation rates for each phenotype varied in the structured versus unstructured EHR. Inter-annotator agreement was high (Cohen’s kappa = 0.72–1) and positively correlated with the NLP-based phenotype extraction pipeline’s performance (average F1-score = 0.65-0.99) for each phenotype. Discussion We developed an automated NLP-based pipeline to extract informative phenotypes that may improve the performance of eventual machine-learning predictive models for AD. In the process, we examined documentation practices for each phenotype relevant to the care of AD patients and identified factors for success. Conclusion Success of our NLP-based phenotype extraction pipeline depended on domain-specific knowledge and focus on a specific clinical domain instead of maximizing generalizability. We developed a natural language processing (NLP)-based pipeline which contains independent NLP modules that target the extraction of ten clinical phenotypes relevant to Alzheimer disease dementia progression. The pipeline was trained on unstructured clinical notes originating from Allscripts TouchWorks associated with AD dementia patient office vsits that occurred between June 1, 2013, to May 31, 2018, extracted from the Washington University in St. Louis Research Data Core (RDC), a repository of patient clinical data from BJC HealthCare and Washington University Physicians. The targeted phenotypes included neurobehavioral test scores (Clinical Dementia Rating and Mini-Mental State Exam) and their corresponding test dates, comorbidities (hypertension and depression), neuroimaging findings (presence of atrophy or infarct), behavioral indicators of dementia (repeating and misplacing), biomarker levels (total and phosphorylated tau protein levels), and family history (whether there was a family history of dementia, and if yes, which family member(s)). The clinical notes extracted from EHR were in rich text format (RTF) contained within tab-delimited files (TXT) alongside metadata such as the patient medical record number, author, and date authored. These were preprocessed before being analyzed by the NLP-based phenotype extraction pipeline. This entailed converting the TXT files to comma-separated files (CSV), accounting for additional tab, quote, and newline characters present, and stripping the RTF formatting. Data preprocessing steps were performed using the Python Pandas and striprtf (version 0.0.10) packages. Linguamatics I2E query files (*.i2qy) and Enterprise Architect Simulation Library (EASL) code for each NLP module can be found on the Linguamatics Community webpage (https://community.linguamatics.com/queries), accessible with the creation of a free account. Linguamatics I2E software is required to open the query files (*.i2qy) directly, but the logic underlying the NLP modules can be understood by referencing the EASL code.

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    Authors: Efford, Meaghan; Taft, Spencer; George, Micheal; George, Michelle; +8 Authors

    # Archaeology demonstrates sustainable Ancestral Coast Salish salmon stewardship over thousands of years pre-contact: MALDI-TOF data --- Salmon are an essential component of the ecosystem in Tsleil-Waututh Nation’s traditional, ancestral, and unceded territory, centred on present-day Burrard Inlet, BC, Canada, where Tsleil-Waututh people have been harvesting salmon, along with a wide variety of other fishes, for millennia. Tsleil-Waututh Nation is an ancestral Coast Salish community that has called the Inlet home since time immemorial. This research assesses the continuity and sustainability of the salmon fishery at təmtəmíxʷtən, an ancestral Tsleil-Waututh settlement in the Inlet, over thousands of years before European contact (1792 CE). We apply Zooarchaeology by Mass Spectrometry (ZooMS) analysis to 245 archaeological salmon vertebrae to identify the species that were harvested by the Tsleil-Waututh community that lived at təmtəmíxʷtən. The results demonstrate that Tsleil-Waututh communities consistently and preferentially fished for chum salmon (*Oncorhynchus keta*) over the period of almost 3,000 years. The consistent abundance indicates a sustainable chum salmon fishery over that time and a strong salmon-to-people relationship through generations. This research supports Tsleil-Waututh Nation’s stewardship obligations under their ancestral legal principles to maintain conditions that uphold the Nation’s way of life. ## Description of the Data and file structure This zip file contains raw MALDI-TOF spectra for each of the 245 salmonid vertebrae sampled for ZooMS. Each sample was spotted in triplicate, along with calibration standards, onto a 384 spot Bruker MALDI ground steel target plate using 1 µL of sample and 1 µL of α-cyano-hydroxycinnamic acid matrix. The samples were run on a Bruker ultraflex III MALDI TOF/TOF mass spectrometer with a Nd:YAG smart beam laser University of York in York, UK, and a SNAP averaging algorithm was used to obtain monoisotopic masses (C 4.9384, N 1.3577, O 1.4773, S 0.0417, H 7.7583). Raw spectral data has been uploaded here for each sample in triplicate. Each sample is within an individual folder with the sample name (e.g., A713). Each raw spectrum is labelled with the sample name and the spot location on the MALDI Plate (e.g., _A1,_A4, _A9). ## Sharing/access Information Links to other publicly accessible locations of the data: None. All samples were analysed within the Ancient DNA and Proteins (ADαPT) Facility in the Department of Anthropology at the University of British Columbia. A total of 245 fish vertebrae were analysed using the method published in Buckley et al. 2009, modified as described in Richter et al. Briefly, ca. 10–30 mg of bone was subsampled and demineralized 0.6 M HCl at 4°C. Samples were rinsed in 200 μL of 0.1 M NaOH to remove humic compound, then rinsed three times in the same volume of 50 mM ammonium bicarbonate solution (NH4HCO3) pH 8.0 (AmBic). Samples were gelatinized through incubation in 100 μL of AmBic at 65 °C for 1 h at 65, before being enzymatically digested overnight at 37 °C in 0.4 μg of trypsin. Digested samples were acidified to 0.1% trifluoroacetic acid (TFA) and purified using Pierce™ 100 μL C18 tips (ThermoFisher). One microliter of α-cyano-hydroxycinnamic acid (matrix) was added to 1 μL of collagen extract and spotted in triplicate with onto a 384 spot MALDI target plate alongside calibration standards. MALDI-TOF was conducted on a Bruker Ultraflex III mass spectrometer with a Nd:YAG smart beam laser, with a SNAP averaging algorithm used to obtain monoisotopic masses (C 4.9384, N 1.3577, O 1.4773, S 0.0417, H 7.7583). Triplicate spectra were averaged and visually inspected using mMass software to identify diagnostic markers published in Richter et al. The raw MALDI spectra are uploaded here. Salmon are an essential component of the ecosystem in Tsleil-Waututh Nation’s traditional, ancestral, and unceded territory, centred on present-day Burrard Inlet, BC, Canada, where Tsleil-Waututh people have been harvesting salmon, along with a wide variety of other fishes, for millennia. Tsleil-Waututh Nation is an ancestral Coast Salish community that has called the Inlet home since time immemorial. This research assesses the continuity and sustainability of the salmon fishery at təmtəmíxʷtən, an ancestral Tsleil-Waututh settlement in the Inlet, over thousands of years before European contact (1792 CE). We apply Zooarchaeology by Mass Spectrometry (ZooMS) analysis to 245 archaeological salmon vertebrae to identify the species that were harvested by the Tsleil-Waututh community that lived at təmtəmíxʷtən. The results demonstrate that Tsleil-Waututh communities consistently and preferentially fished for chum salmon (Oncorhynchus keta) over the period of almost 3,000 years. The consistent abundance indicates a sustainable chum salmon fishery over that time and a strong salmon-to-people relationship through generations. This research supports Tsleil-Waututh Nation’s stewardship obligations under their ancestral legal principles to maintain conditions that uphold the Nation’s way of life.

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    Authors: Dhrangadhariya, Anjani; Müller, Henning;

    This upload contains four main zip files. ds_cto_dict.zip: This zip file contains the four distant supervision dictionaries (P: participant.txt, I = intervention.txt, intervetion_syn.txt, O: outcome.txt) generated from clinicaltrials.gov using the Methodology described in Distant-CTO (https://aclanthology.org/2022.bionlp-1.34/). These dictionaries were used to create distant supervision labelling functions as described in the Labelling sources subsection of the Methodology. The data was derived from https://clinicaltrials.gov/ handcrafted_dictionaries.zip: This zip folder contains three files 1) gender_sexuality.txt: a list of possible genders and sexual orientations found across the web. The list needs to be more comprehensive. 2) endpoints_dict.txt: contains outcome names and the names of questionnaires used to measure outcomes assembled from PROM questionnaires and PROMs. and 3) comparator_dict: contains a list of idiosyncratic comparator terms like a sham, saline, placebo, etc., compiled from the literature search. The list needs to be more comprehensive. test_ebm_correctedlabels.tsv: EBM-PICO is a widely used dataset with PICO annotations at two levels: span-level or coarse-grained and entity-level or fine-grained. Span-level annotations encompass the full information about each class. Entity-level annotations cover the more fine-grained information at the entity level, with PICO classes further divided into fine-grained subclasses. For example, the coarse-grained Participant span is further divided into participant age, gender, condition and sample size in the randomised controlled trial. This dataset comes pre-divided into a training set (n=4,933) annotated through crowd-sourcing and an expert annotated gold test set (n=191) for evaluation. The EBM-PICO annotation guidelines caution about variable annotation quality. Abaho et al. developed a framework to post-hoc correct EBM-PICO outcomes annotation inconsistencies. Lee et al. studied annotation span disagreements suggesting variability across the annotators. Low annotation quality in the training dataset is excusable, but the errors in the test set can lead to faulty evaluation of the downstream ML methods. We evaluate 1% of the EBM-PICO training set tokens to gauge the possible reasons for the fine-grained labelling errors and use this exercise to conduct an error-focused PICO re-annotation for the EBM-PICO gold test set. The file 'test_ebm_correctedlabels.tsv' has error corrected EBM-PICO gold test set. This dataset could be used as a complementary evalution set along with EBM-PICO test set. error_analysis.zip: This .zip file contains three .tsv files for each PICO class to identify possible errors in about 1% (about 12,962 tokens) of the EBM-PICO training set. Objective: PICO (Participants, Interventions, Comparators, Outcomes) analysis is vital but time-consuming for conducting systematic reviews (SRs). Supervised machine learning can help fully automate it, but a lack of large annotated corpora limits the quality of automated PICO recognition systems. The largest currently available PICO corpus is manually annotated, which is an approach that is often too expensive for the scientific community to apply. Depending on the specific SR question, PICO criteria are extended to PICOC (C-Context), PICOT (T-timeframe), and PIBOSO (B-Background, S-Study design, O-Other) meaning the static hand-labelled corpora need to undergo costly re-annotation as per the downstream requirements. We aim to test the feasibility of designing a weak supervision system to extract these entities without hand-labelled data. Methodology: We decompose PICO spans into its constituent entities and re-purpose multiple medical and non-medical ontologies and expert-generated rules to obtain multiple noisy labels for these entities. These labels obtained using several sources are then aggregated using simple majority voting and generative modelling approaches. The resulting programmatic labels are used as weak signals to train a weakly-supervised discriminative model and observe performance changes. We explore mistakes in the currently available PICO corpus that could have led to inaccurate evaluation of several automation methods. Results: We present Weak-PICO, a weakly-supervised PICO entity recognition approach using medical and non-medical ontologies, dictionaries and expert-generated rules. Our approach does not use hand-labelled data. Conclusion: Weak supervision using weak-PICO for PICO entity recognition has encouraging results, and the approach can potentially extend to more clinical entities readily. All the datasets could be opened using text editors or Google sheets. The .zip files in the dataset can be opened using the archive utility on Mac OS and unzip functionality in Linux. (All Windows and Apple operating systems support the use of ZIP files without additional third-party software)

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    DRYAD; ZENODO
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The following results are related to Digital Humanities and Cultural Heritage. Are you interested to view more results? Visit OpenAIRE - Explore.
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    Authors: Gandon, Enora; Nonaka, Tetsushi; Endler, John; Coyle, Thelma; +1 Authors

    Potters from three communities were instructed to faithfully reproduce four different model shapes with the thinnest walls possible using two different quantities of clay (0.75 kg or 2.25 kg), giving a total of eight experimental conditions. The four shapes (referred to as Cylinder, Bowl, Sphere, and Vase, respectively) were presented as pictures without providing any indication of the absolute dimensions to be produced. The four shapes were not part of any of the potters’ daily repertoire. Potters produced five specimens for each of the eight vessel types. The experimental sessions were video-recorded under standardized conditions using a Panasonic NV-GS320 camcorder. The camera was fixed on a tripod with lens orientation centered on the vertical rotation axis of the wheel. The camera was positioned at a height of 30 cm above the level of the wheel at a horizontal distance of 4-6 m. The lower edge of the video scene was aligned with the center of the wheel. The zoom was adapted to fully cover a 36-cm high by 42-cm wide calibration object (inverted T-shape) placed on the wheel at the start of each recording. For each trial, the images of the clay body profile after each fashioning gesture were extracted from the video frames (image resolution: 720 x 576 pixels; video sampling frequency: 25 fps). The first image captured the profile immediately following the (centering and opening) pre-forming phase and the last image captured the final profile; the intervening images captured the intermediate profiles during form development. This succession of profiles captured the vessel’s morphogenesis. The overall duration of the forming process was also analyzed. From the images, we extracted the 2D coordinates of the right half of the cross-sectional profiles by tracing them out on a Cintiq 21UX Wacom (Kazo, Japan) tablet with an integrated screen. The profile coordinates were converted from pixels to centimeters using a calibration factor obtained from the digitized dimensions of the calibration object. The profiles were re-sampled to generate an equal number of points at regular height intervals along the vertical (Y) axis and the resulting coordinates were smoothed with a low-pass filter. Because wheel-thrown vessels are typically axisymmetric, profiles were subsequently converted to full pot outlines by multiplying the horizontal (X) coordinates by -1 to create the corresponding left edge. Of the total 840 vessels thrown (twenty-one potters, each throwing five specimens of eight different vessel types), 12 vessels were not included due to problems with the video recording. # Data from: cultural attraction in pottery practice: group-specific shape transformations by potters from three communities --- Experimental data used in "Cultural attraction in pottery practice: group-specific shape transformations by potters from three communities". Methodological Information: see the manuscript methods section or the methods section of the Dryad dataset (DOI:10.5061/dryad.9ghx3ffpv) for details. Briefly, the dataset contains the two-dimensional outlines of the clay form as a function of time extracted from the video recordings of a cross-cultural experiment with 21 potters from three different cultural backgrounds (French and two Indian communities--Prajapati and Multani Kumhar). ## Description of the Data and file structure ## Summary Metrics * File count: 1 * Total file size: 713 MB * File formats: .mat ## Table of Contents * Pottery_Morphogenesis_Data.mat ## Setup * Unpacking instructions: n/a * Recommended software/tools: Matlab version 2023b. --- ## File/Folder Details ## Details for: Pottery\_Morphogenesis\_Data.mat * Description: Data of the two-dimensional outlines of the clay form as a function of time extracted from the video recordings of a cross-cultural experiment with 21 potters from 3 different cultural backgrounds (French and two Indian communities--Prajapati and Multani Kumhar) replicating 5 specimens of 4 model vessel types (cylinder, bowl, sphere, and vase) with 2 different quantities of clay (0.75 kg and 2.25 kg). * Format(s): .mat * Size(s): 713 MB * The table array "Pottery_Data" stores the following variables for each trial: * Potter: Potter ID and community (e.g., 'French9', 'Prajapati6'). * Type: The name of the model vessel type replicated and mass of clay used (e.g., 'Cylinder0.75', 'Vase2.25'). * Trial: Trial number (1-5). * Outline: Cell arrays that store multiple outlines (first column: the onset of the forming phase (t0), last column: the end of the forming phase) from each trial. Each cell contains 2D outline data extracted from video images of the clay body profile following each fashioning gesture. * Time: Cell arrays that store time stamps corresponding to multiple outlines stored in the column "Outline" (first column: the onset of the forming phase (t0), last column: the end of the forming phase) from each trial. Pottery is a quintessential indicator of human cultural dynamics. Cultural alignment of behavioral repertoires and artifacts has been considered to rest upon two distinct dynamics: selective transmission of information and culture-specific biased transformation. In a cross-cultural field experiment, we tested whether community-specific morphological features of ceramic vessels would arise when the same unfamiliar shapes were reproduced by professional potters from three different communities who threw vessels using wheels. We analyzed the details of the underlying morphogenesis development of vessels in wheel throwing. When expert potters from three different communities of practice were instructed to faithfully reproduce common unfamiliar model shapes that were not parts of the daily repertoires, the morphometric variation in the final shape was not random; rather, different potters produced vessels with more morphometric variation among than within communities, indicating the presence of community-specific deviations of morphological features of vessels. Furthermore, this was found both in the final shape and in the underlying process of morphogenesis; there was more variation in the morphogenetic path among than within communities. These results suggest that the morphological features of ceramic vessels produced by potters reliably and nonrandomly diverge among different communities.

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    Authors: Liebovitch, Larry; Powers, William; Shi, Lin; Chen-Carrel, Allegra; +2 Authors

    # NLP and Machine Learning to Measure Peace from News Media [Dataset]. Dryad. https://doi.org/10.5061/dryad.2v6wwpzv6 PLOS ONE https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0292604 Authors * Larry S. Liebovitch; Department of Physics, Queens College, City University of New York, New York, New York, United States of America and Advanced Consortium on Cooperation, Conflict, and Complexity, Columbia University, New York, New York, United States of America * William Powers; Department of Physics, Queens College, City University of New York, New York, New York, United States of America * Lin Shi; Department of Physics, Queens College, City University of New York, New York, New York, United States of America * Allegra Chen-Carrel; School of Management, University of San Francisco, San Francisco, California, United States of America * Philippe Loustaunau; Vista Consulting, LLC, Arlington, Virginia, United States of America. * Peter T. Coleman; Advanced Consortium on Cooperation, Conflict, and Complexity, Columbia University, New York, New York, United States of America and Teachers College, Columbia University, New York, New York, United States of America Abstract "Hate speech" can mobilize violence and destruction. What are the characteristics of “peace speech” that reflect and support the social processes that maintain peace? In this study we used a data driven, machine learning approach to identify the words most associated with lower-peace versus higher-peace countries. Logistic regression and random forest classifiers were trained using five respected, traditional peace indices: Global Peace Index, Positive Peace Index, World Happiness Index, Fragile States Index, and Human Development Index. The feature inputs into the machine learning model were the word frequencies from the news media in each country and the output classifications were the level of peace in that country. The machine learning model was successful in properly classifying the level of peace from the news media in a country (both accuracy and F1: 96% - 100%). We also used that trained machine model to create a machine learning peace index that measured the level of peace in countries, including countries not in the training set, which correlated with the average of those five traditional peace indices (r-squared = 0.8349). Using the random forest feature importance method we found that the words in news media in lower-peace countries were characterized by words related to government, order, control and fear (such as government, state, law, security and court), while higher-peace countries were characterized by an increased prevalence of words related to optimism for the future and fun (such as time, like, home, believe and game). The detailed analysis and results based on this data set are published in the article in PLOS ONE https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0292604 ## Description of the data and file structure The starting point of our work used the NOW, News on the Web corpus https://www.english-corpora.org/now/ because it has a large amount of news media data on a large range of different topics, including on-line newspaper and magazine articles about accidents, business, crime, education, the arts, government, healthcare, law, literature, medicine, politics, real estate, religion, sports, war, as well as book, music, and movie reviews. A small sample of these sources in the United States include: AlterNet, Austin American-Statesman, Business Insider, Business Wire (press release), Chicago Tribune, FOX43.com, Jerusalem Post, Israel News, KCCI Des Moines, Kentwired, KOKI FOX 23, POWER magazine, Press of Atlantic City, The Jewish Press, USA TODAY, and Vulture. We analyzed data over the time period January 2010 through September 2020. In order to optimize this data for machine learning required natural language processing to substantially transform the NOW data so that the training algorithms would be focused on the most important elements and less sensitive to extraneous elements in the data. The programs to do this were developed as part of a Capstone project by MS students in Data Science at the Columbia Data Science Institute: Jinwoo Jung, Hyuk Joon Kwon, Hojin Lee, Tae Yoon Lim, and Matt MacKenzie as advised by Peter T. Coleman, Allegra Chen-Carrel, and Larry S. Liebovitch and are posted at [https://github.com/mbmackenzie/power-of-peace-speech](https://github.com/mbmackenzie/power-of-peace-speech). This processing consisted of four steps: 1. General text pre-processing: Removing non-word data such as: html tags like p and h and symbols such as {}, <>, , \\n and @. 2. Removing phrases not related to the article's content, such as inducing readers to subscribe and suggested links to other articles which were identified by 5-gram and cosine similarity to find those repeated phrases from each publisher. 3. Removing words (called "stop words" in nlp) such as "a", "the", "and", likely to be similar to both lower-peace and higher-peace countries so that the machine learning algorithms would be more focused on the differences between lower-peace and higher-peace countries. Removing words (called "named entities" in nlp) such as proper names of people, places, and companies, that could be confounding variables that correlate with levels of peace, independent of the language itself. 4. Lemmatizing the words, reducing all forms of words to their stem roots, such as collapsing "walk", "walking", "walked" to one word, so that all forms of each word would count equally towards the total count of that word. The final data set, transformed by these methods, consisted of a total of 723,574 media articles having a total of 57,819,434 words. Usage notes:The data analyzed in our article is available in the data set file. 1. Each text csv file is from the news media in one country. Countries are identified by their 2 letter Alpha-2 country codes: https://www.iban.com/country-codes 2. Each row is one article from an on-line news media source in that country. 3. The first columns respectively identify the: * line number * article\_id * article\_title * publisher, year * article\_text (as modified by step #1 above) * country\_mention * domestic (TRUE=local publisher) 4. The following columns respectively have * article\_text\_Ngram (as additionally modified by step #2 above) * article\_text\_Ngram\_stopword (as additionally modified by step #3 above) * article\_text\_Ngram\_stopword\_lemmatize (as additionally modified by step #4 above) ## Sharing/Access information ArXiv preprint: https://arxiv.org/abs/2305.12537 PLOS ONE article: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0292604 ## Code/Software The Python programs used to do the four natural language processing steps described above and analyze that data in the article are available at * [https://github.com/mbmackenzie/power-of-peace-speech](https://github.com/mbmackenzie/power-of-peace-speech) * [https://github.com/wpqc21/ArticleClassifier/tree/main/ArticleClassifierHSSC](https://github.com/wpqc21/ArticleClassifier/tree/main/ArticleClassifierHSSC) * [https://github.com/smilelinnn/Article-Classification](https://github.com/smilelinnn/Article-Classification) The starting point of our work used the NOW, News on the Web corpus <https://www.english-corpora.org/now/> because it has a large amount of news media data on a large range of different topics, including on-line newspaper and magazine articles about accidents, business, crime, education, the arts, government, healthcare, law, literature, medicine, politics, real estate, religion, sports, war, as well as book, music, and movie reviews. A small sample of these sources in the United States include: AlterNet, Austin American-Statesman, Business Insider, Business Wire (press release), Chicago Tribune, FOX43.com, Jerusalem Post, Israel News, KCCI Des Moines, Kentwired, KOKI FOX 23, POWER magazine, Press of Atlantic City, The Jewish Press, USA TODAY, and Vulture. We analyzed data over the time period January 2010 through September 2020. In order to optimize this data for machine learning required natural language processing to substantially transform the NOW data so that the training algorithms would be focused on the most important elements and less sensitive to extraneous elements in the data. The programs to do this were developed as part of a Capstone project by MS students in Data Science at the Columbia Data Science Institute: Jinwoo Jung, Hyuk Joon Kwon, Hojin Lee, Tae Yoon Lim, and Matt MacKenzie as advised by Peter T. Coleman, Allegra Chen-Carrel, and Larry S. Liebovitch and are posted at <https://github.com/mbmackenzie/power-of-peace-speech>. This processing consisted of four steps: 1. General text pre-processing: Removing non-word data such as: html tags like <p> and <h> and symbols such as {}, <>, \, \n and @. 2. Removing phrases not related to the article's content, such as inducing readers to subscribe and suggested links to other articles which were identified by 5-gram and cosine similarity to find those repeated phrases from each publisher. 3. Removing words (called "stop words" in nlp) such as "a", "the", "and", likely to be similar to both lower-peace and higher-peace countries so that the machine learning algorithms would be more focused on the differences between lower-peace and higher-peace countries. Removing words (called "named entities" in nlp) such as proper names of people, places, and companies, that could be confounding variables that correlate with levels of peace, independent of the language itself. 4. Lemmatizing the words, reducing all forms of words to their stem roots, such as collapsing "walk", "walking", "walked" to one word, so that all forms of each word would count equally towards the total count of that word. The final data set, transformed by these methods, consisted of a total of 723,574 media articles having a total of 57,819,434 words. USAGE NOTES The data analyzed in our article is available in the data set file. 1. Each text csv file is from the news media in one country. Countries are identified by their 2 letter Alpha-2 country codes: https://www.iban.com/country-codes 2. Each row is one article from an on-line news media source in that country. 3. The first columns respectively identify the: line number article_id article_title publisher, year article_text (as modified by step #1 in the Methods) country_mention domestic (TRUE=local publisher) 4. The following columns respectively have article_text_Ngram (as additionally modified by step #2 in the Methods) article_text_Ngram_stopword (as additionally modified by step #3 in the Methods) article_text_Ngram_stopword_lemmatize (as additionally modified by step #4 in the Methods) “Hate speech” can mobilize violence and destruction. What are the characteristics of “peace speech” that reflect and support the social processes that maintain peace? In this study we used a data driven, machine learning approach to identify the words most associated with lower-peace versus higher-peace countries. Logistic regression and random forest classifiers were trained using five respected, traditional peace indices: Global Peace Index, Positive Peace Index, World Happiness Index, Fragile States Index, and Human Development Index. The feature inputs into the machine learning model were the word frequencies from the news media in each country and the output classifications were the level of peace in that country. The machine learning model was successful in properly classifying the level of peace from the news media in a country (both accuracy and F1: 96% - 100%). We also used that trained machine model to create a machine learning peace index that measured the level of peace in countries, including countries not in the training set, which correlated with the average of those five traditional peace indices (r-squared = 0.8349). Using the random forest feature importance method we found that the words in news media in lower-peace countries were characterized by words related to government, order, control and fear (such as government, state, law, security and court), while higher-peace countries were characterized by an increased prevalence of words related to optimism for the future and fun (such as time, like, home, believe and game).

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    Authors: Segessenman, Daniel; Peters, Shanan;

    # Supplementary Data for Transgression-regression cycles drive correlations in Ediacaran-Cambrian rock and fossil records [https://doi.org/10.5061/dryad.xwdbrv1k9](https://doi.org/10.5061/dryad.xwdbrv1k9) All supplementary data files for this study, including R-scripts for analyses, an animation of fossil and stratigraphic column locations through time, tables of rock units matched to fossil occurrences, tables of fossil occurrence assigned ages, and correlations for Ediacaran and Cambrian rock and fossil quantities as separate time periods are included in this Dryad repository. ## Description of the data and file structure Supplementary Figure, Tables, and captions. Highlighting of cells in Table S3, S4, and S5 are to indicate statistical significance at different confidence levels. Green highlighting indicates a correlation that is significant at the 95% level, and yellow indicates a correlation that is significant at the 90% level. No coloring indicates correlations that are not statistically significant. ***Non-Code Supplementary Materials Include***: 2. **Figure S1 (Figure S1 - Animated E-Cm rock & fossil distributions.gif)**: .gif animated map (5 myr bins) of Ediacaran-Cambrian rock and fossil locations on North America 3. **Table S1 (Supplementary Table S1 - Matched Macrostrat units and occurrence counts.csv)**: Macrostrat unit\_id's, strat names, counts of matched PBDB occurrences, and the modeled top and bottom ages of strat units 4. **Table S2 (Supplementary Table S2 – PBDB matched occurrence ages)**: table of Ediacaran-Cambrian PBDB occurrences their PBDB formations/ages and their matched Macrostrat formations/ages. ***NOTE***: cells with 'null' values in this table are cells that were empty (had no value recorded) when pulled from the Paleobiology Database's API. 5. **Table S3 (Supplementary Table S3 Ediacaran-Cambrian Rock-Fossil 1st Difference Correlations.xlsx)**: Correlation coefficients and p-values of 1st differences for Ediacaran-cambrian rock and fossil quantities. 6. **Table S4 (Supplementary Table S4 Cambrian Rock-Fossil Correlations.xlsx)**: Correlation coefficients and p-values of raw rock and fossil quantities for only the Cambrian 7. **Table S5 (Supplementary Table S5 Ediacaran Rock-Fossil Correlations.xlsx)**: Correlation coefficients and p-values for rock and fossil quantities from only the Ediacaran 8. **Supplementary Figure and Table Captions** ## Sharing/Access information API calls used to collect data from the following publicly accessible data platforms are contained within the R-script files found within this supplementary data repository. ***Links to other publicly accessible locations of the data***: * [https://macrostrat.org](https://macrostrat.org) * [https://paleobiodb.org](https://paleobiodb.org) ## Code/Software 1. Supplement S1a-h: Data files and R scripts that produce the panels for all figures and tables - Note that all .csv files included should be ***File-names are***: - S1a\_PBDB\_Collections\_and\_Macrostrat\_column\_Map\_plotter\_(Figure\_1).R - S1b\_PBDB\_occurrences\_and\_Macrostrat\_rock\_quantity\_time\_series\_(Figures\_2-5).R - S1c\_PBDB\_fossils\_and\_Macrostrat\_rock\_correlations\_astrochron\_(Table\_1).R - S1d\_NA Ediacaran Polygons 5-6-2021 v4.csv \- S1e\_pbdb\_data\_ed\_matched\_v7\.csv - S1f\_pbdb\_data\_cm\_matched\_v7.csv - S1g\_pbdb\_ed\_all\_non\_NA\_v1\_8-21-23.csv - S1h\_pbdb\_cm\_all\_non\_NA\_v1\_8-21-23.csv Strata of the Ediacaran Period (635-538.8 Ma) yield the oldest known fossils of complex, macroscopic organisms in the geologic record. These “Ediacaran-type” macrofossils (known as the Ediacaran biota) first appear in mid-Ediacaran strata, experience an apparent decline through the terminal Ediacaran, and directly precede the Cambrian (538.8-485.4 Ma) radiation of animals. Existing hypotheses for the origin and demise of the Ediacaran biota include: changing oceanic redox states, biotic replacement by succeeding Cambrian-type fauna, and mass extinction driven by environmental change. Few studies frame trends in Ediacaran and Cambrian macroevolution from the perspective of the sedimentary rock record, despite well-documented Phanerozoic covariation of macroevolutionary patterns and sedimentary rock quantity. Here we present a quantitative analysis of North American Ediacaran–Cambrian rock and fossil records from Macrostrat and the Paleobiology Database. Marine sedimentary rock quantity increases nearly monotonically and by over a factor of five from the latest Ediacaran to the late Cambrian. Ediacaran–Cambrian fossil quantities exhibit a comparable trajectory and have strong (rs > 0.8) positive correlations with marine sedimentary area and volume flux at multiple temporal resolutions. Even so, Ediacaran fossil quantities are dramatically reduced in comparison to the Cambrian when normalized by the quantity of preserved marine rock. Although aspects of these results are consistent with the expectations of a simple fossil-preservation induced sampling bias, together they suggest that transgression-regression and a large expansion of marine shelf environments coincided with the diversification of animals during a dramatic transition that is starkly evident in both the sedimentary rock and fossil records. Two existing datasets, Macrostrat's database of rocks/stratigraphy and a subset of Paleobiology Database (PBDB) fossil occurrence data, were merged on the basis of their shared rock unit name field for Ediacaran-Cambrian age (635-485.4 Ma) rocks/fossils of North America. Once PBDB fossil occurrences were matched to Macrostrat rock units in time and space (and checked), the fossil occurrence age ranges were modified based on the Macrostrat provided age model of a given fossil occurrences' host rock. Time series of fossil occurrences were generated from this updated data. Correlation coefficients were calculated from the generated time series of fossil occurrences and rock quantities through the Ediacaran-Cambrian geologic time Periods.

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    Authors: Muller, Antoine; Barsky, Deborah; Sala-Ramos, Robert; Sharon, Gonen; +3 Authors

    Spheroids are one of the least understood lithic items yet are one of the most enduring, spanning from the Oldowan to the Middle Palaeolithic. Why and how they were made remains highly debated. We seek to address whether spheroids represent unintentional by-products of percussive tasks or if they were intentionally knapped tools with specific manufacturing goals. We apply novel 3D analysis methods, including spherical harmonics and surface curvature, to 150 limestone spheroids from 'Ubeidiya (c.1.4Ma), presently the earliest Acheulean occurrence outside of Africa, to bring a new perspective to these enigmatic artefacts. We reconstruct the spheroid reduction sequence based on trends in their scar facets and geometry, finding that the spheroid makers at 'Ubeidiya followed a premeditated reduction strategy. During their manufacture, the spheroids do not become smoother, but they become markedly more spherical. They approach an ideal sphere, a feat that likely required a mental template and skilful knapping. Acheulean bifaces are currently thought to represent the earliest evidence of hominins imposing a premeditated, symmetrical shape on stone. With evidence of spheroids occurring before the Acheulean, the intentional production of a sphere-like object now represents the oldest evidence of hominins desiring and achieving intentional geometry and symmetry in stone.

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    Authors: Ebel, Erika; LeMoine, Genevieve; Darwent, Christyann; Darwent, John; +1 Authors

    One hundred and thirty specimens were chosen for peptide mass fingerprinting (PMF) to identify previously unknown osseous materials and their taxonomic origin. Identification of these specimens promotes a better understanding of osseous tool production. PMF involves the enzymatic digestion of proteins followed by Matrix Assisted Laser Desorption-Ionization Time of Flight Mass Spectrometry (MALDI) analysis of the resultant peptide mixture. In the case of bone and ivory, Type 1 collagen is the major constitutive protein, and for each mammalian source, the amino acid sequence of Type 1 collagen, albeit highly conserved, may have one or more amino acid substitutions. These substitutions change the mass of some peptides detected in the MALDI spectrum of the protein digest, which are then used as markers for specific mammalian sources. Markers from known reference materials are compared with those from unknown samples for identification. The ZooMS (PMF) method was a simplified procedure based on that described in Kirby (2013, 2019). Since there are no cystines in collagen, the cystine reduction and end capping steps were omitted. In addition, spectral quality was sufficient to provide confident identifications in all cases where collagen was detected, so no purification or fractionation was required. Some spectra (~15%) had weak or absent A and/or G markers, but those markers were not required for accurate identifications of these samples. In all cases, only a single sample was digested and analyzed. The first author sampled the specimens at the UC Davis Zooarchaeology Lab with sample sticks—polystyrene strips with fiber optic polishing film attached on two sides—which are used to abrade and entrap a small amount of material for analysis. A freshly laundered lab coat, KN95 or surgical facemask, and nitrile examination gloves were worn during handling of all osseous materials. To avoid cross-contamination, gloves were changed, and the workstation was thoroughly sterilized with 70% alcohol wipes between each sample. Samples were obtained by rubbing the object lightly in a circular motion for 10 seconds. After sampling, the tip of the sample stick containing the abrasive film and entrapped sample is cut off, placed in an Eppendorf tube, and forwarded for analysis. Polishing films (30um alumina or diamond particles, final size 2.5 X 2.5mm) were from Precision Fiber Products, Inc., www.precisionfiberproducts.com. Polystyrene strips (1.5 X 2.5 X 400mm) were from Walthers, www.walthers.com. Photographs of each specimen were taken with a DinoLite digital microscope to document the area sampled. The sampling site is generally invisible to the naked eye, and the procedure consumes a miniscule amount of each specimen. Thus, it is considered a minimally destructive sampling technique by most museum curators and conservators. Each specimen and Eppendorf tube was given a unique sample number. Additional samples from the same location were collected on three specimens (KNK912x300; KNK3930x1571, x1822) because they yielded low-quality spectra on the first sample run. These are considered “second passes”, in which collagen was sampled from the same location but from a deeper anatomical component of the specimen, such that a better quality and/or greater quantity of collagen could be obtained. PMF analysis of the 130 samples collected was then undertaken at the Northeastern University Mass Spectrometry Core Facility. Digestion: 60µL of 50mM ammonium bicarbonate (AMBI) were added to each sample (on the sample stick tip in a 600µL Eppendorf tube) and heated to 75°C for 60min. After cooling, 8µL Promega Sequence Grade trypsin (0.02µg/µL in 50mM AMBI) was added and digestion proceeded overnight at 37°C. MALDI analysis: 2µL of the digest were added to 20µL 40% acetonitrile (ACN), 0.1% trifluoroacetic acid (TFA) with saturated α-Cyano-4-hydroxycinnamic acid (CHCA) matrix. 0.65µL of the mixture was spotted onto the MALDI plate. Spectra were obtained with an Applied Biosystems/Sciex 5800 MALDI-TOF-TOF instrument operated in positive reflector mode. Calibration was done with a standard mixture of peptides: 757.3992Da, 1046.5418Da, 1296.6848Da, 1347.7354Da, 1619.8223Da, 2093.0867Da, 2465.1983Da, and 3147.4710Da. Spectra were coadditions of 1200 – 2000 laser shots. Acquired spectra were exported from Applied Biosystems Data Explorer software as text files and imported into mMass (Strohalm et al. 2010) for analysis. Spectra were manually inspected for markers. Markers used to identify mammalian sources are compiled from Buckley et al. (2009, 2014) and Kirby (2013). The site of Iita in northwestern Greenland had near-continual occupation from 1000 CE to the mid-20th century, which presents an unparalleled opportunity to examine diachronic changes in the use of marine mammals for food and raw material resources by Pre-Inuit (Late Dorset) and Inuit-Inughuit—two culturally distinct Indigenous groups. Limited wood in High Arctic environments necessitated the use of antler, bone, and ivory for tool making. Choices in the selection and processing of osseous material reflect changes in the way these two groups used the same resources. However, modification of osseous material during the tool-making process is often to such an extent that few diagnostic features remain for species identification. Here, we include Zooarchaeology by Mass Spectrometry (ZooMS) to identify typically indeterminate pieces of manufacturing debris from preserved collagen. The minimally destructive extraction technique using polishing films had a 99% success rate. The results suggest that the selection of raw materials from certain marine mammals, particularly narwhal, by toolmakers at Iita was more frequent than previously understood through traditional faunal analysis alone. There is a paucity of narwhal from the Pre-Inuit (Late Dorset) assemblage, which contrasts starkly with the high frequency of narwhal in the Inuit-Inughuit assemblage. We demonstrate the importance of combining the analysis of bone-tool manufacturing debris with traditional dietary faunal remains to improve our understanding of resource use in coastal environments. Data is provided in two formats, .msd and .txt files. The text files are organized so that they correspond to the data in Supplementary Table 1. Further details can be found in the README.md file. The .txt files can be accessed by various software programs to visualize the data. The .msd files can be viewed using the mMass software for those who have access. Alternatively, the .msd files can be read by using the packages MALDIQuant and MALDIQuantForeign in R. mMass Data Miner: an Open Source Alternative for Mass Spectrometric Data AnalysisStrohalm M, Hassman M, Košata B, Kodíček MRapid Commun Mass Spec 22 (6), 905-908 (2008)DOI: 10.1002/rcm.3444

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    Authors: Lundy, Jasmine;

    From the 9th to 14th centuries AD, Sicily experienced a series of rapid and quite radical changes in political regime, but the impact of these regime changes on the lives of the people that experienced them remains largely elusive within the historical narrative. We use a multi-faceted lipid residue approach to give direct chemical evidence of the use of 248 everyday domestic ceramic containers from Islamic and post-Islamic contexts in western Sicily to aid our understanding of daily habits throughout this period of political change. A range of commodities was successfully identified, including animal fats, vegetable products, fruit products, (potentially including wine), and plant resins. The study highlights the complexity of residues in Early Medieval Mediterranean society, as in many cases mixtures of commodities were observed, reflecting sequential cooking events and/or the complex mixtures reflective of medieval recipes. However, overall there were no clear changes in the composition of the residues following the imposition of Norman control over the island and through subsequent periods, despite some differences between urban centres and rural sites. Thus, lending to the idea that post-Islamic populations largely flourished and benefited from the agricultural systems, resources, and recipes left by their predecessors. This data set is comprised of data files produced by Gas Chromatography-Mass Spectrometry (GC-MS) of lipids extracted using acid extraction method from pottery sherds from 9th-14th century contexts in Sicily. These data are linked to the published journal where methods of extraction, the context of pottery and the interpretation of data are fully described. Each file corresponds to the sample name as recorded in S1 data and contains a usable cdf. file. The acquisition method for all files is given in TEXT format.

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    DRYAD; ZENODO
    Dataset . 2023
    License: CC 0
    Data sources: Datacite; ZENODO
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      DRYAD; ZENODO
      Dataset . 2023
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    Authors: Gravis, David; Roy, Nicolas; Ruffini-Ronzani, Nicolas; Houssiau, Laurent; +3 Authors

    Book production by medieval scriptoria has gained growing interest in recent studies. In this context, identifying ink compositions and parchment animal species from illuminated manuscripts is of great importance. Here, we introduce time-of-flight secondary ion mass spectrometry (ToF-SIMS) as a non-invasive tool to identify both inks and animal skins in manuscripts, at the same time. For this purpose, both positive and negative ion spectra in inked and non-inked areas were recorded. Chemical compositions of pigments (decoration) or black inks (text) were determined by searching for characteristic ion mass peaks. Animal skins were identified by data processing of raw ToF-SIMS spectra using principal component analysis (PCA). In illuminated manuscripts from the fifteenth to sixteenth century, malachite (green), azurite (blue), cinnabar (red) inorganic pigments, as well as iron-gall black ink, were identified. Carbon black and indigo (blue) organic pigments were also identified. Animal skins were identified in modern parchments of known animal species by a two-step PCA procedure. We believe the proposed method will find extensive application in material studies of medieval manuscripts, as it is non-invasive, highly sensitive and able to identify both inks and animal skins at the same time, even from traces of pigments and tiny scanned areas. In-house PCA algorithm requires python. ToF-SIMS raw data require SurfaceLab software. ATR-FTIR raw data (.0) can be read with free-licence software (Fityk). XRD diffractograms are directly exported in .txt from .xyz files. Analytical and data processing methods can be found in the associated article.

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    DRYAD; ZENODO
    Dataset . 2023
    License: CC 0
    Data sources: Datacite; ZENODO
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      DRYAD; ZENODO
      Dataset . 2023
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    Authors: Oh, Inez; Schindler, Suzanne; Ghoshal, Nupur; Lai, Albert; +2 Authors

    Objectives There is much interest in utilizing clinical data for developing prediction models for Alzheimer disease (AD) risk, progression, and outcomes. Existing studies have mostly utilized curated research registries, image analysis, and structured Electronic Health Record (EHR) data. However, much critical information resides in relatively inaccessible unstructured clinical notes within the EHR. Materials and Methods We developed a natural language processing (NLP)-based pipeline to extract AD-related clinical phenotypes, documenting strategies for success and assessing the utility of mining unstructured clinical notes. We evaluated the pipeline against gold-standard manual annotations performed by two clinical dementia experts for AD-related clinical phenotypes including medical comorbidities, biomarkers, neurobehavioral test scores, behavioral indicators of cognitive decline, family history, and neuroimaging findings. Results Documentation rates for each phenotype varied in the structured versus unstructured EHR. Inter-annotator agreement was high (Cohen’s kappa = 0.72–1) and positively correlated with the NLP-based phenotype extraction pipeline’s performance (average F1-score = 0.65-0.99) for each phenotype. Discussion We developed an automated NLP-based pipeline to extract informative phenotypes that may improve the performance of eventual machine-learning predictive models for AD. In the process, we examined documentation practices for each phenotype relevant to the care of AD patients and identified factors for success. Conclusion Success of our NLP-based phenotype extraction pipeline depended on domain-specific knowledge and focus on a specific clinical domain instead of maximizing generalizability. We developed a natural language processing (NLP)-based pipeline which contains independent NLP modules that target the extraction of ten clinical phenotypes relevant to Alzheimer disease dementia progression. The pipeline was trained on unstructured clinical notes originating from Allscripts TouchWorks associated with AD dementia patient office vsits that occurred between June 1, 2013, to May 31, 2018, extracted from the Washington University in St. Louis Research Data Core (RDC), a repository of patient clinical data from BJC HealthCare and Washington University Physicians. The targeted phenotypes included neurobehavioral test scores (Clinical Dementia Rating and Mini-Mental State Exam) and their corresponding test dates, comorbidities (hypertension and depression), neuroimaging findings (presence of atrophy or infarct), behavioral indicators of dementia (repeating and misplacing), biomarker levels (total and phosphorylated tau protein levels), and family history (whether there was a family history of dementia, and if yes, which family member(s)). The clinical notes extracted from EHR were in rich text format (RTF) contained within tab-delimited files (TXT) alongside metadata such as the patient medical record number, author, and date authored. These were preprocessed before being analyzed by the NLP-based phenotype extraction pipeline. This entailed converting the TXT files to comma-separated files (CSV), accounting for additional tab, quote, and newline characters present, and stripping the RTF formatting. Data preprocessing steps were performed using the Python Pandas and striprtf (version 0.0.10) packages. Linguamatics I2E query files (*.i2qy) and Enterprise Architect Simulation Library (EASL) code for each NLP module can be found on the Linguamatics Community webpage (https://community.linguamatics.com/queries), accessible with the creation of a free account. Linguamatics I2E software is required to open the query files (*.i2qy) directly, but the logic underlying the NLP modules can be understood by referencing the EASL code.

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    Authors: Efford, Meaghan; Taft, Spencer; George, Micheal; George, Michelle; +8 Authors

    # Archaeology demonstrates sustainable Ancestral Coast Salish salmon stewardship over thousands of years pre-contact: MALDI-TOF data --- Salmon are an essential component of the ecosystem in Tsleil-Waututh Nation’s traditional, ancestral, and unceded territory, centred on present-day Burrard Inlet, BC, Canada, where Tsleil-Waututh people have been harvesting salmon, along with a wide variety of other fishes, for millennia. Tsleil-Waututh Nation is an ancestral Coast Salish community that has called the Inlet home since time immemorial. This research assesses the continuity and sustainability of the salmon fishery at təmtəmíxʷtən, an ancestral Tsleil-Waututh settlement in the Inlet, over thousands of years before European contact (1792 CE). We apply Zooarchaeology by Mass Spectrometry (ZooMS) analysis to 245 archaeological salmon vertebrae to identify the species that were harvested by the Tsleil-Waututh community that lived at təmtəmíxʷtən. The results demonstrate that Tsleil-Waututh communities consistently and preferentially fished for chum salmon (*Oncorhynchus keta*) over the period of almost 3,000 years. The consistent abundance indicates a sustainable chum salmon fishery over that time and a strong salmon-to-people relationship through generations. This research supports Tsleil-Waututh Nation’s stewardship obligations under their ancestral legal principles to maintain conditions that uphold the Nation’s way of life. ## Description of the Data and file structure This zip file contains raw MALDI-TOF spectra for each of the 245 salmonid vertebrae sampled for ZooMS. Each sample was spotted in triplicate, along with calibration standards, onto a 384 spot Bruker MALDI ground steel target plate using 1 µL of sample and 1 µL of α-cyano-hydroxycinnamic acid matrix. The samples were run on a Bruker ultraflex III MALDI TOF/TOF mass spectrometer with a Nd:YAG smart beam laser University of York in York, UK, and a SNAP averaging algorithm was used to obtain monoisotopic masses (C 4.9384, N 1.3577, O 1.4773, S 0.0417, H 7.7583). Raw spectral data has been uploaded here for each sample in triplicate. Each sample is within an individual folder with the sample name (e.g., A713). Each raw spectrum is labelled with the sample name and the spot location on the MALDI Plate (e.g., _A1,_A4, _A9). ## Sharing/access Information Links to other publicly accessible locations of the data: None. All samples were analysed within the Ancient DNA and Proteins (ADαPT) Facility in the Department of Anthropology at the University of British Columbia. A total of 245 fish vertebrae were analysed using the method published in Buckley et al. 2009, modified as described in Richter et al. Briefly, ca. 10–30 mg of bone was subsampled and demineralized 0.6 M HCl at 4°C. Samples were rinsed in 200 μL of 0.1 M NaOH to remove humic compound, then rinsed three times in the same volume of 50 mM ammonium bicarbonate solution (NH4HCO3) pH 8.0 (AmBic). Samples were gelatinized through incubation in 100 μL of AmBic at 65 °C for 1 h at 65, before being enzymatically digested overnight at 37 °C in 0.4 μg of trypsin. Digested samples were acidified to 0.1% trifluoroacetic acid (TFA) and purified using Pierce™ 100 μL C18 tips (ThermoFisher). One microliter of α-cyano-hydroxycinnamic acid (matrix) was added to 1 μL of collagen extract and spotted in triplicate with onto a 384 spot MALDI target plate alongside calibration standards. MALDI-TOF was conducted on a Bruker Ultraflex III mass spectrometer with a Nd:YAG smart beam laser, with a SNAP averaging algorithm used to obtain monoisotopic masses (C 4.9384, N 1.3577, O 1.4773, S 0.0417, H 7.7583). Triplicate spectra were averaged and visually inspected using mMass software to identify diagnostic markers published in Richter et al. The raw MALDI spectra are uploaded here. Salmon are an essential component of the ecosystem in Tsleil-Waututh Nation’s traditional, ancestral, and unceded territory, centred on present-day Burrard Inlet, BC, Canada, where Tsleil-Waututh people have been harvesting salmon, along with a wide variety of other fishes, for millennia. Tsleil-Waututh Nation is an ancestral Coast Salish community that has called the Inlet home since time immemorial. This research assesses the continuity and sustainability of the salmon fishery at təmtəmíxʷtən, an ancestral Tsleil-Waututh settlement in the Inlet, over thousands of years before European contact (1792 CE). We apply Zooarchaeology by Mass Spectrometry (ZooMS) analysis to 245 archaeological salmon vertebrae to identify the species that were harvested by the Tsleil-Waututh community that lived at təmtəmíxʷtən. The results demonstrate that Tsleil-Waututh communities consistently and preferentially fished for chum salmon (Oncorhynchus keta) over the period of almost 3,000 years. The consistent abundance indicates a sustainable chum salmon fishery over that time and a strong salmon-to-people relationship through generations. This research supports Tsleil-Waututh Nation’s stewardship obligations under their ancestral legal principles to maintain conditions that uphold the Nation’s way of life.

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    DRYAD; ZENODO
    Dataset . 2023
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    Borealis
    Dataset . 2023
    Data sources: Datacite
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      DRYAD; ZENODO
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      Borealis
      Dataset . 2023
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    Authors: Dhrangadhariya, Anjani; Müller, Henning;

    This upload contains four main zip files. ds_cto_dict.zip: This zip file contains the four distant supervision dictionaries (P: participant.txt, I = intervention.txt, intervetion_syn.txt, O: outcome.txt) generated from clinicaltrials.gov using the Methodology described in Distant-CTO (https://aclanthology.org/2022.bionlp-1.34/). These dictionaries were used to create distant supervision labelling functions as described in the Labelling sources subsection of the Methodology. The data was derived from https://clinicaltrials.gov/ handcrafted_dictionaries.zip: This zip folder contains three files 1) gender_sexuality.txt: a list of possible genders and sexual orientations found across the web. The list needs to be more comprehensive. 2) endpoints_dict.txt: contains outcome names and the names of questionnaires used to measure outcomes assembled from PROM questionnaires and PROMs. and 3) comparator_dict: contains a list of idiosyncratic comparator terms like a sham, saline, placebo, etc., compiled from the literature search. The list needs to be more comprehensive. test_ebm_correctedlabels.tsv: EBM-PICO is a widely used dataset with PICO annotations at two levels: span-level or coarse-grained and entity-level or fine-grained. Span-level annotations encompass the full information about each class. Entity-level annotations cover the more fine-grained information at the entity level, with PICO classes further divided into fine-grained subclasses. For example, the coarse-grained Participant span is further divided into participant age, gender, condition and sample size in the randomised controlled trial. This dataset comes pre-divided into a training set (n=4,933) annotated through crowd-sourcing and an expert annotated gold test set (n=191) for evaluation. The EBM-PICO annotation guidelines caution about variable annotation quality. Abaho et al. developed a framework to post-hoc correct EBM-PICO outcomes annotation inconsistencies. Lee et al. studied annotation span disagreements suggesting variability across the annotators. Low annotation quality in the training dataset is excusable, but the errors in the test set can lead to faulty evaluation of the downstream ML methods. We evaluate 1% of the EBM-PICO training set tokens to gauge the possible reasons for the fine-grained labelling errors and use this exercise to conduct an error-focused PICO re-annotation for the EBM-PICO gold test set. The file 'test_ebm_correctedlabels.tsv' has error corrected EBM-PICO gold test set. This dataset could be used as a complementary evalution set along with EBM-PICO test set. error_analysis.zip: This .zip file contains three .tsv files for each PICO class to identify possible errors in about 1% (about 12,962 tokens) of the EBM-PICO training set. Objective: PICO (Participants, Interventions, Comparators, Outcomes) analysis is vital but time-consuming for conducting systematic reviews (SRs). Supervised machine learning can help fully automate it, but a lack of large annotated corpora limits the quality of automated PICO recognition systems. The largest currently available PICO corpus is manually annotated, which is an approach that is often too expensive for the scientific community to apply. Depending on the specific SR question, PICO criteria are extended to PICOC (C-Context), PICOT (T-timeframe), and PIBOSO (B-Background, S-Study design, O-Other) meaning the static hand-labelled corpora need to undergo costly re-annotation as per the downstream requirements. We aim to test the feasibility of designing a weak supervision system to extract these entities without hand-labelled data. Methodology: We decompose PICO spans into its constituent entities and re-purpose multiple medical and non-medical ontologies and expert-generated rules to obtain multiple noisy labels for these entities. These labels obtained using several sources are then aggregated using simple majority voting and generative modelling approaches. The resulting programmatic labels are used as weak signals to train a weakly-supervised discriminative model and observe performance changes. We explore mistakes in the currently available PICO corpus that could have led to inaccurate evaluation of several automation methods. Results: We present Weak-PICO, a weakly-supervised PICO entity recognition approach using medical and non-medical ontologies, dictionaries and expert-generated rules. Our approach does not use hand-labelled data. Conclusion: Weak supervision using weak-PICO for PICO entity recognition has encouraging results, and the approach can potentially extend to more clinical entities readily. All the datasets could be opened using text editors or Google sheets. The .zip files in the dataset can be opened using the archive utility on Mac OS and unzip functionality in Linux. (All Windows and Apple operating systems support the use of ZIP files without additional third-party software)

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    DRYAD; ZENODO
    Dataset . 2022
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      DRYAD; ZENODO
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