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  • Digital Humanities and Cultural Heritage
  • 2012-2021
  • Research data
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  • Dataset
  • ZENODO
  • Digital Humanities and Cultural Her...

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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: Asmelash Teka Hadgu;

    Wikipedia is the largest encyclopedia ever assembled with the vision of enabling every human being to freely share in the sum of all knowledge. Wikipedia currently has a total of more than six million articles and over 17 billion words in its English edition. Unfortunately, millions of people cannot access this resource because it���s not available in their language. For instance, at the moment there are only 218 Tigrinya Wikipedia and 15,018 Amharic Wikipedia articles. In this project, we investigate the problem of translating Wikipedia articles from a high resource language into low resource languages using human-in-the-loop MT systems. In particular, we investigate different approaches to translate a sample of English Wikipedia articles into Tigrinya and Amharic. Currently, this repository contains 100k English Wikipeida abstracts translated using Lesan (https://lesan.ai) into Amharic and Tigrinya. Structure of data directory: data ��������� human ��������� mt ��������� google ��������� lesan ��� ��������� am.txt ��� ��������� en.txt ��� ��������� ti.txt ��������� microsoft

    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/ ZENODOarrow_drop_down
    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/
    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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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/ ZENODOarrow_drop_down
      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/
      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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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: Egeland, Charles; Fadem, Cynthia; Byerly, Ryan; Henderson, Cory; +4 Authors

    Variable Description Type Type of sample (calibration = calibration coin for Delta Innov-X Analyzer; standard = NIST geological standard; geological = geological sample from lithic raw material source; artifact = archaeological specimen) Replicate Replicate measurement (Yes or No) Source Geological source (delta = Delta Innov-x Analyzer calibration coin; nist = National Institute of Standards and Technology (NIST) geological standard; NS = Naibor Soit; NH = Naisuisui Hill; OL = Oldonye Okule; LD = Lemagarut drainage; SS = Shifting Sand; KG = Kelogi Hills; EN = Engelosin) Outcrop Individual outcrop within geological source (NSM = Naibor Soit Main Hill; NSMH = Naibor Soit Manyata Hill; NSSO = Naibor Soit Southern Outlier; NH = Naisuisui Hill; LD1 = Lemagarut Drainage 1; LD2 = Lemagarut Drainage 2; BKE = BK East; SS = Shifting Sand; KG = Kelogi Hills; EN = Engelosin; NA = Not applicable) Specimen Individual find or sample number Material Raw material type (QTZ = "Quartz-rich"; GN = Gneiss; FGV = Fine-grained volcanic) Element concentration estimate Reported for each element (e.g., P, Cl, Ca; empty cells are "non-detect") Analytical error Reported for each element (e.g., P +/-, Cl +/-, Ca +/-; no error reported for "non-detect" elements) The published analysis focused only on granulite specimens (n = 186) and, more specifically, on six elements (Fe, Ti, Zr, K, Sr, and Y) that had detection rates >75% in the granulite specimens (that is, these elements were detected in more than 75% of the granulite specimens). These elements were used in the predictive models from the published analysis. Two of the 186 granulite specimens were missing values for five out of the six elements and were therefore not included in the statistical analyses. Of the remaining 184 specimens, 55 had missing data for one element, and two of those 55 had missing data for two elements. These missing values were treated as censored data (that is, the element is present but could not be measured precisely enough for the instrument to report a value). These missing values were interpolated in one of two ways. For those specimens subjected to replicate pXRF runs (n = 7), the missing value was replaced with the mean value of the replicates. The missing values for the remaining specimens (n = 48) were replaced with the mean of the four closest (as determined in two-dimensional space) specimens with measured (rather than interpolated) values. The main data file does not include these interpolated values. Should analysts choose to use them, interpolated values can be found in the additional .csv file. The invention and proliferation of stone tool technology in the Early Stone Age (ESA) marks a watershed in human evolution. Patterns of lithic procurement, manufacture, use, and discard have much to tell us about ESA hominin cognition and land use. However, these issues cannot be fully explored outside the context of the physical attributes and spatio-temporal availability of the lithic raw materials themselves. The Olduvai Basin of northern Tanzania, which is home to both a wide variety of potential toolstones and a rich collection of ESA archaeological sites, provides an excellent opportunity to investigate the relationship between lithic technology and raw material characteristics. Here, we examine two attributes of the basin's igneous and metamorphic rocks: spatial location and fracture predictability. A total of 244 geological specimens were analyzed with non-destructive portable XRF (pXRF) to determine the geochemical distinctiveness of five primary and secondary sources, while 110 geological specimens were subjected to Schmidt rebound hardness tests to measure fracture predictability. Element concentrations derived via pXRF show significant differences between sources, and multivariate predictive models classify geological specimens with 75–80% accuracy. The predictive models identify Naibor Soit as the most likely source for a small sample of three lithic artifacts from Bed II, which supports the idea that this inselberg served as a source of toolstone during the early Pleistocene. Clear patterns in fracture predictability exist within and between both sources and rock types. Fine-grained volcanics show high rebound values (associated with high fracture predictability), while finer-grained metamorphics and coarsegrained gneisses show intermediate and low rebound values, respectively. Artifact data from Bed I and II suggest that fracture predictability played a role in raw material selection at some sites, but other attributes like durability, expediency, and nodule size and shape were more significant. A total of 244 rock specimens (aka "geological specimens") were collected from eight primary (six granulite outcrops, one gneiss outcrop, one phonolite outcrop) and one secondary (a seasonal drainage containing basalt blocks) lithic raw material sources in the Olduvai Basin. Rock specimens were flaked directly from the sources with a rockhammer. Only granulite specimens with visually quartz-rich compositions were selected. Five quartz-rich metamorphic artifacts (aka "archaeological specimens") from BK East, a ca. 1.5 million-year-old site on the south wall of the side gorge in Olduvai Gorge, were also included. Portable XRF (pXRF) analyses were conducted with an Innov-X Delta Classic Environmental Analyzer equipped with a 4W Au anode X-ray tube and a Si-PIN diode detector. All analyses were performed while the instrument was docked into a stable, hands-free test stand. An unweathered, non-cortical surface free of sediment matrix was placed over, and completely covered, the detector window. Each specimen was measured for 360 seconds using all three of the instrument's beams (120 seconds/beam). After an initial energy scale calibration test with a factory issued metal coin of known composition, the following protocol was observed: (1) a powdered sample of Standard Reference Material (SRM) 2702 with elemental concentrations certified by NIST was measured; (2) four geological/archaeological specimens were then measured; (3) the fifth geological/archaeological specimen in a series was measured five times (that is, five consecutive 360-second cycles) without being moved or reoriented; (4) after the fifth geological/archaeological specimen was measured, the SRM 2702 sample was measured once again, which initiated the next series of measurements. Element concentrations were derived with the Compton Normalization correction model and the factory-set “Soil Environmental” calibration.

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    DRYAD; ZENODO
    Dataset . 2021
    License: CC 0
    Data sources: Datacite; 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/ DRYAD; ZENODOarrow_drop_down
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      DRYAD; ZENODO
      Dataset . 2021
      License: CC 0
      Data sources: Datacite; 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: Divya M. Persaud; Robert Barnes;

    {"references": ["Barnes, R., S. Gupta, C. Traxler, T. Ortner, A. Bauer, G. Hesina, G. Paar, et al. 2018. \"Geological Analysis of Martian Rover-Derived Digital Outcrop Models Using the 3-D Visualization Tool, Planetary Robotics 3-D Viewer\u2014PRo3D.\" Earth and Space Science 5: 1\u201323. https://doi.org/10.1002/2018EA000374.", "Fraeman, A. A., B. L. Ehlmann, R. E. Arvidson, C. S. Edwards, J. P. Grotzinger, R. E. Milliken, D. P. Quinn, and M. S. Rice. 2016. \"The Stratigraphy and Evolution of Lower Mount Sharp from Spectral, Morphological, and Thermophysical Orbital Data Sets.\" Journal of Geophysical Research E: Planets 121 (9): 1713\u201336. https://doi.org/10.1002/2016JE005095.Received.", "Hughes, M. N. 2021. \"Landscape Evolution at Endeavour and Gale Craters on Mars, and How Terrain Characteristics Correlate with Mineralogy on Lower Mount Sharp, Gale Crater.\" https://doi.org/10.7936/c6se-5895.", "Milliken, R. E., J. P. Grotzinger, and B. J. Thomson. 2010. \"Paleoclimate of Mars as Captured by the Stratigraphic Record in Gale Crater.\" Geophysical Research Letters 37 (4): 1\u20136. https://doi.org/10.1029/2009GL041870.", "Persaud, D. M. (2021). Co-registered U. Arizona HiRISE DTM and ORI over Sakarya Vallis, Gale Crater, Mars [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5808371", "Persaud, D. M. (2021). Multi-Resolution Basemap of Northwest Aeolis Mons, Gale Crater, Mars [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5808381", "Thomson, B. J., N. T. Bridges, R. E. Milliken, A. M. Baldridge, S. J. Hook, J. K. Crowley, G. M. Marion, C. R. de Souza Filho, A. J. Brown, and C. M. Weitz. 2011. \"Constraints on the Origin and Evolution of the Layered Mound in Gale Crater, Mars Using Mars Reconnaissance Orbiter Data.\" Icarus 214 (2): 413\u201332. https://doi.org/10.1016/j.icarus.2011.05.002."]} This dataset comprises the unit map derived from 3D analysis of Sakarya Vallis in Gale crater, Mars. The units are named Package 1–7. These packages have been extrapolated from morpho-stratigraphic analysis of a HiRISE scene in PRo3D. They have been further extrapolated using underlying image data. Included here is a shapefile representing the marker bed (Milliken et al. 2010) in Gale crater. The "CDD" refers to the Central Debris Deposit, identified by Hughes (2021). The structural data represents dip measurements along the boundaries of these packages within the feature; the "sub-package" data represent layering within the packages. For more on how dip is calculated in PRo3D, see https://pro3d.space/. Finally, the profiles mark the locations where topographic profiles were extracted for constructing cross-sections, as discussed in the thesis Persaud (2022). These data are intended to be displayed with the HiRISE ORI (https://doi.org/10.5281/zenodo.5808371) and CTX ORI mosaic (https://doi.org/10.5281/zenodo.5808357) over Sakarya Vallis, and over the basemap over the northwest of Aeolis Mons (https://doi.org/10.5281/zenodo.5808381). Format: SHP, SHX, DBF, PRJ, QPJ Projection: Equidistant cylindrical Datum: Spheroid (r = 3396.190 km) N.B. the PROJ4 format of the project is "+proj=eqc +lat_ts=0 +lat_0=0 +lon_0=0 +x_0=0 +y_0=0 +a=3396190 +b=3396190 +units=m +no_defs" The first author is now at Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California. Contact: divya.m.persaud@jpl.nasa.gov Part of this work was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under contract to NASA. Government sponsorship acknowledged.

    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/ ZENODOarrow_drop_down
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: ZENODO
    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/
    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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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/ ZENODOarrow_drop_down
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: ZENODO
      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/
      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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    Authors: Hagmann, Dominik; Langendorf, Alarich; Steininger, Andreas;

    Photos for the 2015's 3d model of the interior of the so-called witch tower (���Hexenturm���) at the south easteren corner of the outer fortifications of Ulmerfeld Castle. The model was made using 3d photogrammetry (image based modeling) and mast aerial photography.

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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: ZENODO
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: ZENODO
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    Authors: Hätty, Anna; Schlechtweg, Dominik; Schulte im Walde, Sabine;

    This data collection contains synchronic semantic relatedness judgments for German word usage pairs drawn from general language and the domain of cooking. Find a description of the data format, code to process the data and further datasets on the WUGsite. See previous versions for additional plots, tables and testsets. Please find more information on the provided data in the paper referenced below. Version: 3.0.0, 15.12.2021. Reference Anna Hätty, Dominik Schlechtweg, Sabine Schulte im Walde. 2019. SURel: A Gold Standard for Incorporating Meaning Shifts into Term Extraction. In Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM). Minneapolis, Minnesota, USA, 2019. DURel metrics inferred from median instead of mean edge weights (for compatibility with other WUG data sets), removal of previous metrics and additional data (refer to previous versions)

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    ZENODO
    Dataset . 2021
    License: CC BY ND
    Data sources: Datacite
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      ZENODO
      Dataset . 2021
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    Authors: Sevenants, Walter; Boudry, Tanja; Dondeyne, Stefaan;

    GIS-bestanden (MAPINFO-formaat) met de resultaten van het syntheseproject "Het Romeins wegennet in Vlaanderen. Een evaluatie op basis van archeologische wegvindplaatsen". (Deze MAPINFO bestanden kunnen o.a. met QGIS omgezet worden naar shapefiles, GeoJSON, of andere meer courante GIS-bestandstypes) - HUB-Lines - INV-Lines - Primaire Wegen - Romeinse Centrale & Speciale Plaatsen - Romeinse Wegdelen - Niet-ontsloten gebieden Dit archeologisch syntheseproject streeft naar een actualisering van onze kennis over de materi��le aspecten van het Romeins wegennet in Vlaanderen. Deze actualisering verleent niet alleen inzicht in wat we nu meer weten over het Romeins wegennet dan pak weg 65 jaar geleden, maar ook welke kennisleemtes er nog zijn. De bijdrage aan de kwaliteitsverbetering van het archeologisch onderzoek op het vlak van methoden en technieken vormt de tweede doelstelling van dit onderzoek. Op basis van de verkregen inzichten worden enkele aanbevelingen geformuleerd m.b.t. het archeologisch onderzoek naar en van Romeinse wegen in Vlaanderen.

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    ZENODO
    Dataset . 2021
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2021
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      ZENODO
      Dataset . 2021
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    Authors: Kulkarni, Mayank; Mahata, Debanjan; Arora, Ravneet; Bhowmik, Rajarshi;

    In this work, we explore how to learn task-specific language models aimed towards learning rich representation of keyphrases from text documents. We experiment with different masking strategies for pre-training transformer language models (LMs) in discriminative as well as generative settings. In the discriminative setting, we introduce a new pre-training objective - Keyphrase Boundary Infilling with Replacement (KBIR), showing large gains in performance (up to 9.26 points in F1) over SOTA, when LM pre-trained using KBIR is fine-tuned for the task of keyphrase extraction. In the generative setting, we introduce a new pre-training setup for BART - KeyBART, that reproduces the keyphrases related to the input text in the CatSeq format, instead of the denoised original input. This also led to gains in performance (up to 4.33 points in F1@M) over SOTA for keyphrase generation. Additionally, we also fine-tune the pre-trained language models on named entity recognition (NER), question answering (QA), relation extraction (RE), abstractive summarization and achieve comparable performance with that of the SOTA, showing that learning rich representation of keyphrases is indeed beneficial for many other fundamental NLP tasks. As a part of this zip file we release the KBIR model which is continually pre-trained on RoBERTa-Large and also the KeyBART model which is continually pre-trained on BART-Large. Both these models can be used in place of a RoBERTa-Large or BART-Large model in PyTorch codebases and also with HuggingFace.

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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: ZENODO
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      ZENODO
      Dataset . 2021
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      Data sources: Datacite
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: ZENODO
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    Authors: Barefoot, Eric; Straub, Kyle;

    Information about past environments is stored in sedimentary rocks via biogeochemical markers stored in the sediments. Using these markers, the signal of paleoclimate and other environmental factors can be reconstructed from the strata. However, because sediment accumulation occurs stochastically, the stratigraphic record is often difficult to reconstruct with confidence. It is generally thought that with a sufficient sample size though, noise averages out, and the true signal can be reconstructed. This assumption is valid when the statistics of erosion and deposition remain steady throughout the interval of interest. In fact, it is known that changes in climate can alter the statistics of erosion and deposition, but the impact of this effect on paleoclimate reconstructions remains poorly understood. This dataset describes a set of physical delta experiments conducted at the Tulane University Sediment Dynamics and Stratigraphy Laboratory. Throughout the experiment, the level of flooding intensity that the delta was exposed to alternated between two end-member values, with transitions of varying durations. We monitored channel dynamics, and reconstructed synthetic climate records from the strata to see how the changing statistics of sediment accumulation impacted the preservation of environmental signals in the strata. This dataset is an HDF5 dataset, which is a general format. The data largely consist of a set of 3D arrays that contain 2D topography and imagery data, where the third dimension is time. Each data object is paired with a 1D vector that links datasets across the time dimension, since data were collected at different intervals. The appropriate linking datasets are also included as CSVs. E.B. acknowledges support from a Pathfinder Fellowship provided by CUAHSI with support from the National Science Foundation (NSF) Cooperative Agreement No. EAR-1849458. E.B. additionally acknowledges support from SEPM via a Student Assistance Grant.

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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: ZENODO
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: ZENODO
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    Authors: Romanov, Maxim;

    The dataset contains three collections of mainly literary Arabic texts from the 19th and early 20th centuries. corpus022_JurjiZaydan_Dated is a dated corpus of 22 historical novels by Jurj�� Zayd��n. It is well established that Jurj�� Zayd��n was publishing roughly one novel per year and the dates of publication are well known, which makes this corpus a valuable material for testing chronological changes in the style of individual writers. corpus065 is a corpus of 65 books by 8 authors; corpus300 contains 300 books by 28 authors; Texts have been collected from https://www.hindawi.org/; the original EPUB files have been converted into clean text files (UTF8 encoding) and Arabic orthography has been normalized in the following manner: short vowels removed; the orthography of alif simplified (all alifs converted into bare alifs are used); alif maq�����ra��s converted to y����s. For the names of authors and the names of works, the URIs follow the naming conventions used in the OpenITI project (https://github.com/openiti, for the most up-to-date description, see https://kitab-project.org/corpus-and-data). However, for better compatibility with R stylo, underscore (_) is used to connect URIs: AUTHOR_TITLE (in the dated Jurj�� Zayd��n corpus files are named YEAR_TITLE).

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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: ZENODO
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2021
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    Authors: Florian Barth; Tillmann Dönicke; Benjamin Gittel; Luisa Gödeke; +4 Authors

    MONACO: Modes of Narration and Attribution Corpus This corpus is constructed by the project group Modes of Narration and Attribution (MONA). We provide German literary texts annotated with three base phenomena: Generalising Interpretation (GI), Comment, and Non-fictional Speech (NfR), as well as Attribution on top of them. DFG Schwerpunktprogramm SPP 2207 "Computational Literary Studies" Online: https://gepris.dfg.de/gepris/projekt/402743989 https://dfg-spp-cls.github.io/ Teilprojekt: "Structuring Literature - Variants and Functions of Reflextive Passages in Narrative Fiction" Online: https://gepris.dfg.de/gepris/projekt/424264086 https://dfg-spp-cls.github.io/projects_en/2020/01/24/TP-Structuring_Literature/ https://www.uni-goettingen.de/de/structuring+literature/626921.html Source repository: https://gitlab.gwdg.de/mona/korpus-public/-/releases/v2.0

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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: ZENODO
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: 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: Asmelash Teka Hadgu;

    Wikipedia is the largest encyclopedia ever assembled with the vision of enabling every human being to freely share in the sum of all knowledge. Wikipedia currently has a total of more than six million articles and over 17 billion words in its English edition. Unfortunately, millions of people cannot access this resource because it���s not available in their language. For instance, at the moment there are only 218 Tigrinya Wikipedia and 15,018 Amharic Wikipedia articles. In this project, we investigate the problem of translating Wikipedia articles from a high resource language into low resource languages using human-in-the-loop MT systems. In particular, we investigate different approaches to translate a sample of English Wikipedia articles into Tigrinya and Amharic. Currently, this repository contains 100k English Wikipeida abstracts translated using Lesan (https://lesan.ai) into Amharic and Tigrinya. Structure of data directory: data ��������� human ��������� mt ��������� google ��������� lesan ��� ��������� am.txt ��� ��������� en.txt ��� ��������� ti.txt ��������� microsoft

    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/ ZENODOarrow_drop_down
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: ZENODO
      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/
      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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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: Egeland, Charles; Fadem, Cynthia; Byerly, Ryan; Henderson, Cory; +4 Authors

    Variable Description Type Type of sample (calibration = calibration coin for Delta Innov-X Analyzer; standard = NIST geological standard; geological = geological sample from lithic raw material source; artifact = archaeological specimen) Replicate Replicate measurement (Yes or No) Source Geological source (delta = Delta Innov-x Analyzer calibration coin; nist = National Institute of Standards and Technology (NIST) geological standard; NS = Naibor Soit; NH = Naisuisui Hill; OL = Oldonye Okule; LD = Lemagarut drainage; SS = Shifting Sand; KG = Kelogi Hills; EN = Engelosin) Outcrop Individual outcrop within geological source (NSM = Naibor Soit Main Hill; NSMH = Naibor Soit Manyata Hill; NSSO = Naibor Soit Southern Outlier; NH = Naisuisui Hill; LD1 = Lemagarut Drainage 1; LD2 = Lemagarut Drainage 2; BKE = BK East; SS = Shifting Sand; KG = Kelogi Hills; EN = Engelosin; NA = Not applicable) Specimen Individual find or sample number Material Raw material type (QTZ = "Quartz-rich"; GN = Gneiss; FGV = Fine-grained volcanic) Element concentration estimate Reported for each element (e.g., P, Cl, Ca; empty cells are "non-detect") Analytical error Reported for each element (e.g., P +/-, Cl +/-, Ca +/-; no error reported for "non-detect" elements) The published analysis focused only on granulite specimens (n = 186) and, more specifically, on six elements (Fe, Ti, Zr, K, Sr, and Y) that had detection rates >75% in the granulite specimens (that is, these elements were detected in more than 75% of the granulite specimens). These elements were used in the predictive models from the published analysis. Two of the 186 granulite specimens were missing values for five out of the six elements and were therefore not included in the statistical analyses. Of the remaining 184 specimens, 55 had missing data for one element, and two of those 55 had missing data for two elements. These missing values were treated as censored data (that is, the element is present but could not be measured precisely enough for the instrument to report a value). These missing values were interpolated in one of two ways. For those specimens subjected to replicate pXRF runs (n = 7), the missing value was replaced with the mean value of the replicates. The missing values for the remaining specimens (n = 48) were replaced with the mean of the four closest (as determined in two-dimensional space) specimens with measured (rather than interpolated) values. The main data file does not include these interpolated values. Should analysts choose to use them, interpolated values can be found in the additional .csv file. The invention and proliferation of stone tool technology in the Early Stone Age (ESA) marks a watershed in human evolution. Patterns of lithic procurement, manufacture, use, and discard have much to tell us about ESA hominin cognition and land use. However, these issues cannot be fully explored outside the context of the physical attributes and spatio-temporal availability of the lithic raw materials themselves. The Olduvai Basin of northern Tanzania, which is home to both a wide variety of potential toolstones and a rich collection of ESA archaeological sites, provides an excellent opportunity to investigate the relationship between lithic technology and raw material characteristics. Here, we examine two attributes of the basin's igneous and metamorphic rocks: spatial location and fracture predictability. A total of 244 geological specimens were analyzed with non-destructive portable XRF (pXRF) to determine the geochemical distinctiveness of five primary and secondary sources, while 110 geological specimens were subjected to Schmidt rebound hardness tests to measure fracture predictability. Element concentrations derived via pXRF show significant differences between sources, and multivariate predictive models classify geological specimens with 75–80% accuracy. The predictive models identify Naibor Soit as the most likely source for a small sample of three lithic artifacts from Bed II, which supports the idea that this inselberg served as a source of toolstone during the early Pleistocene. Clear patterns in fracture predictability exist within and between both sources and rock types. Fine-grained volcanics show high rebound values (associated with high fracture predictability), while finer-grained metamorphics and coarsegrained gneisses show intermediate and low rebound values, respectively. Artifact data from Bed I and II suggest that fracture predictability played a role in raw material selection at some sites, but other attributes like durability, expediency, and nodule size and shape were more significant. A total of 244 rock specimens (aka "geological specimens") were collected from eight primary (six granulite outcrops, one gneiss outcrop, one phonolite outcrop) and one secondary (a seasonal drainage containing basalt blocks) lithic raw material sources in the Olduvai Basin. Rock specimens were flaked directly from the sources with a rockhammer. Only granulite specimens with visually quartz-rich compositions were selected. Five quartz-rich metamorphic artifacts (aka "archaeological specimens") from BK East, a ca. 1.5 million-year-old site on the south wall of the side gorge in Olduvai Gorge, were also included. Portable XRF (pXRF) analyses were conducted with an Innov-X Delta Classic Environmental Analyzer equipped with a 4W Au anode X-ray tube and a Si-PIN diode detector. All analyses were performed while the instrument was docked into a stable, hands-free test stand. An unweathered, non-cortical surface free of sediment matrix was placed over, and completely covered, the detector window. Each specimen was measured for 360 seconds using all three of the instrument's beams (120 seconds/beam). After an initial energy scale calibration test with a factory issued metal coin of known composition, the following protocol was observed: (1) a powdered sample of Standard Reference Material (SRM) 2702 with elemental concentrations certified by NIST was measured; (2) four geological/archaeological specimens were then measured; (3) the fifth geological/archaeological specimen in a series was measured five times (that is, five consecutive 360-second cycles) without being moved or reoriented; (4) after the fifth geological/archaeological specimen was measured, the SRM 2702 sample was measured once again, which initiated the next series of measurements. Element concentrations were derived with the Compton Normalization correction model and the factory-set “Soil Environmental” calibration.

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    DRYAD; ZENODO
    Dataset . 2021
    License: CC 0
    Data sources: Datacite; ZENODO
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      DRYAD; ZENODO
      Dataset . 2021
      License: CC 0
      Data sources: Datacite; 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: Divya M. Persaud; Robert Barnes;

    {"references": ["Barnes, R., S. Gupta, C. Traxler, T. Ortner, A. Bauer, G. Hesina, G. Paar, et al. 2018. \"Geological Analysis of Martian Rover-Derived Digital Outcrop Models Using the 3-D Visualization Tool, Planetary Robotics 3-D Viewer\u2014PRo3D.\" Earth and Space Science 5: 1\u201323. https://doi.org/10.1002/2018EA000374.", "Fraeman, A. A., B. L. Ehlmann, R. E. Arvidson, C. S. Edwards, J. P. Grotzinger, R. E. Milliken, D. P. Quinn, and M. S. Rice. 2016. \"The Stratigraphy and Evolution of Lower Mount Sharp from Spectral, Morphological, and Thermophysical Orbital Data Sets.\" Journal of Geophysical Research E: Planets 121 (9): 1713\u201336. https://doi.org/10.1002/2016JE005095.Received.", "Hughes, M. N. 2021. \"Landscape Evolution at Endeavour and Gale Craters on Mars, and How Terrain Characteristics Correlate with Mineralogy on Lower Mount Sharp, Gale Crater.\" https://doi.org/10.7936/c6se-5895.", "Milliken, R. E., J. P. Grotzinger, and B. J. Thomson. 2010. \"Paleoclimate of Mars as Captured by the Stratigraphic Record in Gale Crater.\" Geophysical Research Letters 37 (4): 1\u20136. https://doi.org/10.1029/2009GL041870.", "Persaud, D. M. (2021). Co-registered U. Arizona HiRISE DTM and ORI over Sakarya Vallis, Gale Crater, Mars [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5808371", "Persaud, D. M. (2021). Multi-Resolution Basemap of Northwest Aeolis Mons, Gale Crater, Mars [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5808381", "Thomson, B. J., N. T. Bridges, R. E. Milliken, A. M. Baldridge, S. J. Hook, J. K. Crowley, G. M. Marion, C. R. de Souza Filho, A. J. Brown, and C. M. Weitz. 2011. \"Constraints on the Origin and Evolution of the Layered Mound in Gale Crater, Mars Using Mars Reconnaissance Orbiter Data.\" Icarus 214 (2): 413\u201332. https://doi.org/10.1016/j.icarus.2011.05.002."]} This dataset comprises the unit map derived from 3D analysis of Sakarya Vallis in Gale crater, Mars. The units are named Package 1–7. These packages have been extrapolated from morpho-stratigraphic analysis of a HiRISE scene in PRo3D. They have been further extrapolated using underlying image data. Included here is a shapefile representing the marker bed (Milliken et al. 2010) in Gale crater. The "CDD" refers to the Central Debris Deposit, identified by Hughes (2021). The structural data represents dip measurements along the boundaries of these packages within the feature; the "sub-package" data represent layering within the packages. For more on how dip is calculated in PRo3D, see https://pro3d.space/. Finally, the profiles mark the locations where topographic profiles were extracted for constructing cross-sections, as discussed in the thesis Persaud (2022). These data are intended to be displayed with the HiRISE ORI (https://doi.org/10.5281/zenodo.5808371) and CTX ORI mosaic (https://doi.org/10.5281/zenodo.5808357) over Sakarya Vallis, and over the basemap over the northwest of Aeolis Mons (https://doi.org/10.5281/zenodo.5808381). Format: SHP, SHX, DBF, PRJ, QPJ Projection: Equidistant cylindrical Datum: Spheroid (r = 3396.190 km) N.B. the PROJ4 format of the project is "+proj=eqc +lat_ts=0 +lat_0=0 +lon_0=0 +x_0=0 +y_0=0 +a=3396190 +b=3396190 +units=m +no_defs" The first author is now at Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California. Contact: divya.m.persaud@jpl.nasa.gov Part of this work was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under contract to NASA. Government sponsorship acknowledged.

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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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    Authors: Hagmann, Dominik; Langendorf, Alarich; Steininger, Andreas;

    Photos for the 2015's 3d model of the interior of the so-called witch tower (���Hexenturm���) at the south easteren corner of the outer fortifications of Ulmerfeld Castle. The model was made using 3d photogrammetry (image based modeling) and mast aerial photography.

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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: ZENODO
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      ZENODO
      Dataset . 2021
      License: CC BY
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      ZENODO
      Dataset . 2021
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    Authors: Hätty, Anna; Schlechtweg, Dominik; Schulte im Walde, Sabine;

    This data collection contains synchronic semantic relatedness judgments for German word usage pairs drawn from general language and the domain of cooking. Find a description of the data format, code to process the data and further datasets on the WUGsite. See previous versions for additional plots, tables and testsets. Please find more information on the provided data in the paper referenced below. Version: 3.0.0, 15.12.2021. Reference Anna Hätty, Dominik Schlechtweg, Sabine Schulte im Walde. 2019. SURel: A Gold Standard for Incorporating Meaning Shifts into Term Extraction. In Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM). Minneapolis, Minnesota, USA, 2019. DURel metrics inferred from median instead of mean edge weights (for compatibility with other WUG data sets), removal of previous metrics and additional data (refer to previous versions)

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    ZENODO
    Dataset . 2021
    License: CC BY ND
    Data sources: Datacite
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      ZENODO
      Dataset . 2021
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    Authors: Sevenants, Walter; Boudry, Tanja; Dondeyne, Stefaan;

    GIS-bestanden (MAPINFO-formaat) met de resultaten van het syntheseproject "Het Romeins wegennet in Vlaanderen. Een evaluatie op basis van archeologische wegvindplaatsen". (Deze MAPINFO bestanden kunnen o.a. met QGIS omgezet worden naar shapefiles, GeoJSON, of andere meer courante GIS-bestandstypes) - HUB-Lines - INV-Lines - Primaire Wegen - Romeinse Centrale & Speciale Plaatsen - Romeinse Wegdelen - Niet-ontsloten gebieden Dit archeologisch syntheseproject streeft naar een actualisering van onze kennis over de materi��le aspecten van het Romeins wegennet in Vlaanderen. Deze actualisering verleent niet alleen inzicht in wat we nu meer weten over het Romeins wegennet dan pak weg 65 jaar geleden, maar ook welke kennisleemtes er nog zijn. De bijdrage aan de kwaliteitsverbetering van het archeologisch onderzoek op het vlak van methoden en technieken vormt de tweede doelstelling van dit onderzoek. Op basis van de verkregen inzichten worden enkele aanbevelingen geformuleerd m.b.t. het archeologisch onderzoek naar en van Romeinse wegen in Vlaanderen.

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    ZENODO
    Dataset . 2021
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2021
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      ZENODO
      Dataset . 2021
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      Data sources: Datacite
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    Authors: Kulkarni, Mayank; Mahata, Debanjan; Arora, Ravneet; Bhowmik, Rajarshi;

    In this work, we explore how to learn task-specific language models aimed towards learning rich representation of keyphrases from text documents. We experiment with different masking strategies for pre-training transformer language models (LMs) in discriminative as well as generative settings. In the discriminative setting, we introduce a new pre-training objective - Keyphrase Boundary Infilling with Replacement (KBIR), showing large gains in performance (up to 9.26 points in F1) over SOTA, when LM pre-trained using KBIR is fine-tuned for the task of keyphrase extraction. In the generative setting, we introduce a new pre-training setup for BART - KeyBART, that reproduces the keyphrases related to the input text in the CatSeq format, instead of the denoised original input. This also led to gains in performance (up to 4.33 points in F1@M) over SOTA for keyphrase generation. Additionally, we also fine-tune the pre-trained language models on named entity recognition (NER), question answering (QA), relation extraction (RE), abstractive summarization and achieve comparable performance with that of the SOTA, showing that learning rich representation of keyphrases is indeed beneficial for many other fundamental NLP tasks. As a part of this zip file we release the KBIR model which is continually pre-trained on RoBERTa-Large and also the KeyBART model which is continually pre-trained on BART-Large. Both these models can be used in place of a RoBERTa-Large or BART-Large model in PyTorch codebases and also with HuggingFace.

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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: ZENODO
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      ZENODO
      Dataset . 2021
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      Data sources: Datacite
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: ZENODO
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    Authors: Barefoot, Eric; Straub, Kyle;

    Information about past environments is stored in sedimentary rocks via biogeochemical markers stored in the sediments. Using these markers, the signal of paleoclimate and other environmental factors can be reconstructed from the strata. However, because sediment accumulation occurs stochastically, the stratigraphic record is often difficult to reconstruct with confidence. It is generally thought that with a sufficient sample size though, noise averages out, and the true signal can be reconstructed. This assumption is valid when the statistics of erosion and deposition remain steady throughout the interval of interest. In fact, it is known that changes in climate can alter the statistics of erosion and deposition, but the impact of this effect on paleoclimate reconstructions remains poorly understood. This dataset describes a set of physical delta experiments conducted at the Tulane University Sediment Dynamics and Stratigraphy Laboratory. Throughout the experiment, the level of flooding intensity that the delta was exposed to alternated between two end-member values, with transitions of varying durations. We monitored channel dynamics, and reconstructed synthetic climate records from the strata to see how the changing statistics of sediment accumulation impacted the preservation of environmental signals in the strata. This dataset is an HDF5 dataset, which is a general format. The data largely consist of a set of 3D arrays that contain 2D topography and imagery data, where the third dimension is time. Each data object is paired with a 1D vector that links datasets across the time dimension, since data were collected at different intervals. The appropriate linking datasets are also included as CSVs. E.B. acknowledges support from a Pathfinder Fellowship provided by CUAHSI with support from the National Science Foundation (NSF) Cooperative Agreement No. EAR-1849458. E.B. additionally acknowledges support from SEPM via a Student Assistance Grant.

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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: ZENODO
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: ZENODO
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    Authors: Romanov, Maxim;

    The dataset contains three collections of mainly literary Arabic texts from the 19th and early 20th centuries. corpus022_JurjiZaydan_Dated is a dated corpus of 22 historical novels by Jurj�� Zayd��n. It is well established that Jurj�� Zayd��n was publishing roughly one novel per year and the dates of publication are well known, which makes this corpus a valuable material for testing chronological changes in the style of individual writers. corpus065 is a corpus of 65 books by 8 authors; corpus300 contains 300 books by 28 authors; Texts have been collected from https://www.hindawi.org/; the original EPUB files have been converted into clean text files (UTF8 encoding) and Arabic orthography has been normalized in the following manner: short vowels removed; the orthography of alif simplified (all alifs converted into bare alifs are used); alif maq�����ra��s converted to y����s. For the names of authors and the names of works, the URIs follow the naming conventions used in the OpenITI project (https://github.com/openiti, for the most up-to-date description, see https://kitab-project.org/corpus-and-data). However, for better compatibility with R stylo, underscore (_) is used to connect URIs: AUTHOR_TITLE (in the dated Jurj�� Zayd��n corpus files are named YEAR_TITLE).

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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: ZENODO
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2021
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    Authors: Florian Barth; Tillmann Dönicke; Benjamin Gittel; Luisa Gödeke; +4 Authors

    MONACO: Modes of Narration and Attribution Corpus This corpus is constructed by the project group Modes of Narration and Attribution (MONA). We provide German literary texts annotated with three base phenomena: Generalising Interpretation (GI), Comment, and Non-fictional Speech (NfR), as well as Attribution on top of them. DFG Schwerpunktprogramm SPP 2207 "Computational Literary Studies" Online: https://gepris.dfg.de/gepris/projekt/402743989 https://dfg-spp-cls.github.io/ Teilprojekt: "Structuring Literature - Variants and Functions of Reflextive Passages in Narrative Fiction" Online: https://gepris.dfg.de/gepris/projekt/424264086 https://dfg-spp-cls.github.io/projects_en/2020/01/24/TP-Structuring_Literature/ https://www.uni-goettingen.de/de/structuring+literature/626921.html Source repository: https://gitlab.gwdg.de/mona/korpus-public/-/releases/v2.0

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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: ZENODO
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
      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/
      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: ZENODO
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