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  • Digital Humanities and Cultural Heritage
  • Research data
  • 2013-2022
  • Digital Humanities and Cultural Heritage

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  • Research data . Audiovisual . 2022
    Open Access German
    Authors: 
    van Oorschot, Frederike;
    Publisher: Zenodo

    Der Vortrag entfaltet hermeneutische und epistemologische Fragen, die durch die sich entwickelnde digitale Forschung in den Geisteswissenschaften (Digital Humanities, DH) ausgelöst werden. Im Vordergrund steht die Skizze einer auf die DH bezogenen Wissenschaftsphilosophie anhand der folgenden Leitfragen: Was bedeutet das Narrativ einer „neuen“ Wissenschaft? Wer ist Subjekt der DH? Wo finden sich neue epistemische Logiken? Was ist das forschungspolitische Setting der DH? Und wie verhält sich dies alles zum Selbstverständnis „klassischer“ Geisteswissenschaften? Dabei zielt der Vortrag auf eine „digitale Hermeneutik“ in den Geisteswissenschaften.

  • Open Access English
    Authors: 
    Dhrangadhariya, Anjani; Müller, Henning;
    Publisher: Dryad

    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)

  • Open Access
    Authors: 
    Leonardo Santiago Benitez Pereira;
    Publisher: Zenodo

    Collection of 300 support tickets manually labeled for semantic similarity, obtained from a IT support company in the Florianópolis (Brazil) region. Each ticket is represented by an unstructured text field, which is typed by the user that opened the call. The labeling process was performed in 2022 by three IT support professionals. The corpus contains tickets in many languages, mainly English, German, Portuguese and Spanish. All Personal Identifiable Information (PII) and sensitive information were removed (substituted by a tag indicating the original content, for instance: the sentence "this text was written by Leonardo" is converted to "this text was written by [NAME]"). The removal was performed in three steps: first, the automated machine learning-based tool AWS Comprehend PII Removal was used; then, a sequence of custom regular expressions was applied; last, the entire corpus was manually verified.

  • Open Access
    Authors: 
    Jan Moens; Koen De Groote;
    Publisher: Zenodo

    Bijlage bij 'Moens J. & De Groote K. 2022: Ieper - De Meersen. Deel 2. De studie van het leer', een onderzoeksrapport van het agentschap Onroerend Erfgoed: - volledige inventaris van de leervondsten (als .xlsx-bestand)

  • English
    Authors: 
    Bentley, Michael; Roberts, Stephen; Heredia Barion, Pablo; Strenlin, Jorge; Spiegel, Cornelia; Niedermann, Samuel; Wacker, Lukas;
    Publisher: NERC EDS UK Polar Data Centre

    Mapping and Stratigraphic section sample collection Mapping was undertaken in ARC-GIS, with final layouts achieved in Adobe Illustrator v. 26.2.1 or CorelDRAW v. 2020. Lithostratigraphic descriptions and radiocarbon sampling were undertaken at an outer peninsula stratigraphic profile referred to as "new Pingfo II" and a new river section adjacent to the 'Potter Cove section' sampled by Sugden and John (1973). We also sampled terrestrial moss samples for radiocarbon dating from a recently exposed 'Inland outcrop' which is located inside the 1956 CE limit, around 700 m from the active glacier front. The stratigraphic sections were characterised using textural criteria, fabric, composition, sedimentary structures, and grain size analysis to determine the relationships between different depositional units. Radiocarbon (C-14) dating: Twenty-one Accelerator Mass Spectrometry (AMS) radiocarbon (C-14) ages were obtained from seaweed, marine mollusc shells, penguin and undetermined bones, remnants of terrestrial mosses embedded in stratigraphic profiles and moraine sediments. Calibration of marine sample radiocarbon ages (marine shells and seaweed) was undertaken in Oxcal v. 4.4 using the Marine20 calibration curve (Bronk Ramsey, 2009; Heaton et al., 2020), and a newly recalculated local marine reservoir age offset (delta R) of 666 plus-minus 76 C-14 years (Heaton et al., 2020), which represents the weighted mean delta R of four radiocarbon-dated marine samples collected prior to 1950 CE from the northern Antarctic Peninsula and Signy Island in the online Marine20 database (http://calib.org/marine/). Terrestrial and aquatic moss samples were calibrated using the Southern Hemisphere SHCal20 calibration curve in Oxcal v. 4.4 (Hogg et al., 2020). Post-bomb (more than 1950 CE) ages were corrected according to 13C/12C isotopic ratios from measured pMC with the 'present day' pMC value defined as 107.5 percent (2010 CE) and calibrated using the SHCal13 SH Zone 1-2 Bomb curve in CALIBomb (Reimer and Reimer, 2004; Hua et al., 2013). Cosmogenic Helium-3 (He-3) nuclide surface exposure dating (CSED): Five samples were collected for He-3 CSED using a hammer and chisel to remove the upper few centimetres of exposed surfaces. Differential GPS (dGPS) measurements were undertaken using a Trimble Pathfinder ProXH to determine the precise location and altitude of boulders in relation to the landmark DALL 66019M002 (62.23787 degrees S, 58.66455 degrees W, ellipsoidal height 39.376 m) triangulation station located on the Argentine Carlini base, a few hundred metres away from the sampled erratics. dGPS precision is better than 10 cm in all axes, but ellipsoid correction errors are larger. Exposure ages were calculated using the CRONUScalc calculator (Version 2.0; Marrero et al., 2016) with the time-dependent Lal (1991)/Stone (2000) scaling model (Lm) for altitude at Antarctic pressure conditions and the primary calibration data set for He-3 in pyroxene, which yields a long term sea-level high latitude (SLHL) scaled production rate of 122 plus-minus 13 at g-1 a-1 (Borchers et al., 2016). External age uncertainties include production rate uncertainties. Exposure ages determined with other scaling models (e.g., Lifton et al., 2014) vary by up to around 6 percent. We report internal and external uncertainties. Following Balco et al. (2008), external uncertainties are used for comparison with calibrated AMS radiocarbon ages and error ranges. Grain size analysis: Thirteen samples were dry sieved to separate the fraction larger than 2 mm, placed in an ultrasonic bath for 10 seconds, then placed in a reciprocating shaker and left overnight. Samples were wet sieved to separate the fraction less than 0.063 mm (silt and clay size), and the coarser fraction was dried in an oven at 50 degrees Celsius and dry sieved into sand fractions (more than 1mm, more than 0.5 mm, more than 0.25 mm, more than 0.125 mm and more than 0.063 mm). The silt and clay fractions were transferred to a sedimentation cylinder and fine and coarse silt separated from clays after settling using the pipette method. The clay fraction was then concentrated using a centrifuge, and all fractions were dried in an oven at 40 degrees Celsius. Additional statistical analysis was undertaken, and figures constructed, using R v. 4.1.0/RStudio v. 1.4.1717 (primarily packages Tidyverse, ggplot2, Vegan, Rioja, Ggally v. 2.1.2, RBacon, Rcarbon, Bchron), Sigmaplot v. 14.0, C2 (Juggins, 2007), MATLAB v. R2021a, with final layouts achieved in Adobe Illustrator v. 26.2.1 or CorelDRAW v. 2020. Code, data, all packages used and package references can be found at: https://github.com/stever60/Potter Peninsula The dataset comprises of stratigraphic chronological and sedimentological data from Potter Peninsula, King George Island, South Shetland Islands. The data have been used to constrain deglaciation and glacier dynamics on Potter Peninsula. Data collected in this study were funded by: Centro de Investigaciones en Ciencias de la Tierra (CICTERRA), the Direccion Nacional del Antartico/Instituto Antartico Argentino (DNA/IAA) in the framework of the Project PICTA, 2011 - 0102, IAA "Geomorfologia y Geologia Glaciar del Archipielago James Ross e Islas Shetland del Sur, Sector Norte de la Peninsula Antartica"; the Alfred Wegener Institute (AWI) research program Polar regions and Coasts in a changing Earth System (PACES II); IMCONet (FP7 IRSES, action no. 318718); the Natural Environment Research Council (NERC/BAS-CGS Grant no.81); the NERC/BAS science programmes CACHE-PEP: Natural climate variability - extending the Americas palaeoclimate transect through the Antarctic Peninsula to the pole and GRADES-QWAD: Quaternary West Antarctic Deglaciations. We thank the crews of the Argentine research station "Carlini'" and the adjoined German Dallmann-Labor (AWI) Laboratory, the Uruguayan research station "Artigas", the Russian Bellingshausen Station, the Chinese Great Wall Station, Base Presidente Eduardo Frei Montalva, the Brazilian Navy Almirante Maximiano, the UK Navy HMS Endurance and NERC/BAS James Clark Ross for logistical support during the 2006, 2011, 2014 and 2015 field seasons. Radiocarbon samples were prepared at the Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research and British Antarctic Survey. AMS measurements were undertaken at ETH Zurich and Beta Analytical, Miami, and 13C/12C isotope ratios were used to calculate Conventional Radiocarbon Ages. Pre-bomb calibrated ages have been rounded to the nearest 10 years, and to the nearest hundred years in the manuscript text to reflect realistic total (internal and external) uncertainties. Post-bomb ages have been rounded to the nearest year.

  • English
    Authors: 
    Roberts, Stephen; Pearson, Emma; Czalbowski, Tamara; Davies, Sarah; Grosjean, Martin; Arcusa, Stephanie; Perren, Bianca;
    Publisher: NERC EDS UK Polar Data Centre

    Core collection Sediment cores were collected using a Livingston piston corer from the deepest point(~5 m of water depth) in Matias Lake(L5: 62.2450°S, 58.6655°W,~70-75 m a.s.l., 20-30 cm total recovered sediment depth). We extracted 13 short cores from the depocentre in Matias Lake and along a surface transect towards Rudy Lake. Sediment recovery depth ranged from 20 and 60 cm before encountering an impenetrable diamicton layer. Data from cores MAT1 (L5-H1) (27 cm) and MAT2 (L5-H2) (29 cm) extracted from the deepest part of Matias Lake (5.8 m; 62° 14' 42.054"S, 58° 39' 53.82"W) are included in this dataset. Chronology Obtaining basal radiocarbon ages from basal and bulk minerogenic sediments in the Matias Lake cores proved challenging due to a general lack of organic carbon.Pb-210 Cs-137 and Am-241 dating of the uppermost 10 cm of the lake sediment records was undertaken and dating model calculations followed standard procedures defined in Appleby and Oldfield (1978). Pb-210 age estimates were derived using the constant rate of supply (CRS) method (Appleby and Oldfield, 1978) and incorporated into Bayesian age-depth models. The Pb-210 Constant Rate of Supply (CRS) age model shows that the uppermost 10 cm have been deposited since c. 1850 CE, and the well-defined 137Cs peak at 5 cm depth is coherent with the Pb-210 age model. Geochemistry and Sedimentology Physical properties (gamma-ray density (GRD), magnetic susceptibility, fractional porosity, resistivity and impedance) were measured using Geotek® multi-sensor core logger (MSCL). Non-destructive ITRAXTM (Cox Analytical) micro-X-ray fluorescence (micro-XRF) and Bartington Magnetic Susceptibility High Resolution Surface Scanning Sensor (MS2E) measurements were undertaken at Aberystwyth University. Contiguous bulk, wet sediment geochemical Energy Dispersive XRF-CS (Energy Dispersive Spectroscopy) analysis was obtained using a chromium (Cr) X-ray tube (X-radiography image settings: 40 kV, 40 mA, 200 ms; XRF-CSCr settings: 30 kV, 40 mA, dwell time of 10 seconds, at 100 micrometers or 2 mm. Hyperspectral image (HSI) scanning was performed using the Specim Ltd. single core scanner (PFD-CL-65-V10E line scan camera, 400-1000 nm) according to the protocol of Butz et al. (2015). The spatial resolution (pixel size) was set at 69 micrometers x 69 micrometers and the spectral resolution is 2.8 nm sampled at an interval of 0.78 nm. Raw data were normalised with a BaSO4 reference and spectral endmembers were calculated using the software ENVI 5.03. Quantitative estimates of pigments were obtained using the Relative Absorption Band Depth (RABD) method, which uses ratios and normalised reflectance data from distinct wavelengths. The spectral index RABD660;670 (RABD at 660-670 nm) was calculated from the continuum between 590 and 730 nm (Butz et al., 2015) with I-band wavelengths between 660 and 670 nm using equations RABD660;670 = (6*R590+7*R730)/13/Rmin(660;670) and RABD660;670 [I-Band] = (6*R590+7*R730)/13/ Rmin(660;670)/Rmean (Rein and Sirocko, 2002). Additional statistical analysis was undertaken, and figures constructed, using R v. 4.1.0/RStudio v. 1.4.1717 (primarily packages Tidyverse, ggplot2, Vegan, Rioja, Ggally v. 2.1.2, RBacon, Rcarbon, Bchron), Sigmaplot v. 14.0, C2 (Juggins, 2007), MATLAB v. R2021a, with final layouts achieved in Adobe Illustrator v. 26.2.1 or CorelDRAW v. 2020. Code, data, all packages used, and package references can be found at: https://github.com/stever60/Potter_Peninsula The dataset comprises of lake site photos, data and multiproxy data from Lake L5 (aka Matias Lake), a small lake basin at 62.2450 S, 58.6655 W on Potter Peninsula, King George Island, South Shetland Islands. The data have been used to constrain deglaciation and glacier dynamics on Potter Peninsula. Data for the Lake L5 (Matias Lake) sediment record consist of downcore measurements of chronology, geochemistry, and sedimentology proxy data collected from the depocentre in November 2011. Data collected in this study were funded by: Centro de Investigaciones en Ciencias de la Tierra (CICTERRA), the Direccion Nacional del Antartico/Instituto Antartico Argentino (DNA/IAA) in the framework of the Project PICTA, 2011 - 0102, IAA and Geomorfologia y Geologia Glaciar del Archipielago James Ross e Islas Shetland del Sur, Sector Norte de la Peninsula Antartica, the Alfred Wegener Institute (AWI) research program Polar regions and Coasts in a changing Earth System (PACES II); IMCONet (FP7 IRSES, action no. 318718); the Natural Environment Research Council (NERC/BAS-CGS Grant no.81); the NERC/BAS science programmes CACHE-PEP: Natural climate variability - extending the Americas palaeoclimate transect through the Antarctic Peninsula to the pole and GRADES-QWAD: Quaternary West Antarctic Deglaciations. We thank the crews of the Argentine research station "Carlini" and the adjoined German Dallmann-Labor (AWI) Laboratory, the Uruguayan research station "Artigas", the Russian Bellingshausen Station, the Chinese Great Wall Station, Base Presidente Eduardo Frei Montalva, the Brazilian Navy Almirante Maximiano, the UK Navy HMS Endurance and NERC/BAS James Clark Ross for logistical support during the 2006, 2011, 2014 and 2015 field seasons. Chronology Pb-210 age estimates were derived using the constant rate of supply (CRS) method (Appleby and Oldfield, 1978) and incorporated into Bayesian age-depth models. Geochemistry & Sedimentology ITRAX-XRF Raw count per second (cps) data were analysed using the Q-spec software v8.6.0 (Cox Analytical), with MSE values minimised to optimise the fit of 'as measured' spectra to a modelled spectrum. Data are presented as percentages of the Total Scatter Normalised ratio sum (%∑TSN or, more simply, %TSN, which are equivalent to percentages of the cps sum, or %cps) to account for downcore variations in count rate, density, water and organic content. Data less than mean minus two-sigma kcps (mainly due to gaps in the core) and greater than MSE plus two-sigma (representing a poor fit between measured to modelled spectra) were filtered before analysis.?Noisy? elements were eliminated by comparing cps and using %TSN thresholds of >0.1% mean and >0.5% maximum , and by examining autocorrelation profiles for each element. Elements are presented as natural log (log n or Ln) ratios. Chronology Pb-210 Cs-137 and Am-241 dating of the uppermost 10 cm of the lake sediment records was undertaken on a well-type gamma spectrometry (Ge-detector, GWC 2522-7500 SL, Canberra Industries Inc., USA) and processed with GENIE 2000 3.0 (Canberra Industries Inc., USA). Geochemistry and Sedimentology ITRAX XRF core scanner fitted with a Molybdenum (Mo) anode X-ray tube and a Bartington Magnetic Susceptibility High Resolution Surface Scanning Sensor (MS2E) Geotek multi-sensor core logger (MSCL) Hyperspectral image (HSI) scanning was performed using the Specim Ltd. single core scanner (PFD-CL-65-V10E line scan camera, 400?1000 nm) according to the protocol of Butz et al. (2015). GEOTEK MSCL data were measured at 2 mm intervals Geochemical XRF-Core scanning data were measured at 100 μm and 2 mm contiguous intervals. SPECIM hyperspectral data were measured at 69 μm intervals

  • English
    Authors: 
    Roberts, Stephen; Hocking, Emma; Heredia Barion, Pablo;
    Publisher: NERC EDS UK Polar Data Centre

    The dataset comprises of compilations of new and published age data constraining glacier advance, retreat and aquatic moss layers in lakes from the South Shetland Islands. The data were used in data analysis in the following manuscripts to constrain deglaciation and glacier dynamics on Potter Peninsula and Fildes Peninsula, King George Island South Shetland Islands: Heredia Barion P, Roberts SJ, Spiegel C, Binnie SA, Wacker L, Davies J, et al. (submitted - a) Mid-late Holocene deglaciation and glacier readvances on the Fildes Peninsula, King George Island, NW Antarctic Peninsula. The Holocene. Heredia Barion P, Strelin JA, Roberts SJ, Spiegel C, Wacker L, Niedermann S, et al. (submitted - b). Holocene deglaciation, glacial dynamics and the geomorphology of Potter Peninsula, King George Island (Isla 25 de Mayo), NW Antarctic Peninsula. Frontiers in Earth Science. Data collected in this study were funded by: Centro de Investigaciones en Ciencias de la Tierra (CICTERRA), the Direccion Nacional del Antartico/Instituto Antartico Argentino (DNA/IAA) in the framework of the Project PICTA, 2011 - 0102, IAA "Geomorfologia y Geologia Glaciar del Archipielago James Ross e Islas Shetland del Sur, Sector Norte de la Peninsula Antartica"; the Alfred Wegener Institute (AWI) research program Polar regions and Coasts in a changing Earth System (PACES II); IMCONet (FP7 IRSES, action no. 318718); the Natural Environment Research Council (NERC/BAS-CGS Grant no.81); the NERC/BAS science programmes CACHE-PEP: Natural climate variability - extending the Americas palaeoclimate transect through the Antarctic Peninsula to the pole and GRADES-QWAD: Quaternary West Antarctic Deglaciations. We thank the crews of the Argentine research station "Carlini" and the adjoined German Dallmann-Labor (AWI) Laboratory, the Uruguayan research station "Artigas", the Russian Bellingshausen Station, the Chinese Great Wall Station, Base Presidente Eduardo Frei Montalva, the Brazilian Navy Almirante Maximiano, the UK Navy HMS Endurance and NERC/BAS James Clark Ross for logistical support during the 2006, 2011, 2014 and 2015 field seasons. Compilations of new and published age data constraining glacier advance, retreat and aquatic moss layers in lakes from the South Shetland Islands used in data analysis in the following manuscripts to constrain deglaciation and glacier dynamics on Potter Peninsula and Fildes Peninsula, King George Island South Shetland Islands: Heredia Barion P, Roberts SJ, Spiegel C, Binnie SA, Wacker L, Davies J, et al. (submitted - a) Mid-late Holocene deglaciation and glacier readvances on the Fildes Peninsula, King George Island, NW Antarctic Peninsula. The Holocene. Heredia Barion P, Strelin JA, Roberts SJ, Spiegel C, Wacker L, Niedermann S, et al. (submitted - b). Holocene glacial dynamics and geomorphology of Potter Peninsula, King George Island (NW Antarctic Peninsula). Frontiers in Earth Science. Additional statistical analysis was undertaken, and figures constructed, using R v. 4.1.0/RStudio v. 1.4.1717 (primarily packages Tidyverse, ggplot2, Vegan, Rioja, Ggally v. 2.1.2, RBacon, Rcarbon, Bchron), Sigmaplot v. 14.0, C2 (Juggins, 2007), MATLAB v. R2021a, with final layouts achieved in Adobe Illustrator v. 26.2.1 or CorelDRAW v. 2020. Code, data, all packages used and package references can be found at: https://github.com/stever60/Potter Peninsula

  • English
    Authors: 
    Roberts, Stephen; Pearson, Emma; Czalbowski, Tamara; Davies, Sarah; Grosjean, Martin; Arcusa, Stephanie; Perren, Bianca;
    Publisher: NERC EDS UK Polar Data Centre

    Core collection: Sediment cores were collected using a Russian and Livingston corers from the deepest point (around 2 m of water depth) in Lake L15 (GPS Lake) (L15: -62.24057 degrees S, -58.67760 degrees W, around 30 m a.s.l., 11-55 cm total recovered sediment depth). Eight cores were collected from a grid of 25 (around1 m spaced) holes drilled through the around 75-100 cm thick lake ice above the deepest accessible point (around 2 m): Data from L15-H2 (-62.24057 degrees S, -58.67760 degrees W) with 30 cm of sediment and L15-H16 (-62.24056 degrees S, -58.67757 degrees W) with 55 cm of sediment are included in this dataset. Other cores taken at this site were: L15-H4 = 11 cm, L15-H9 = 39 cm, L15-H17 = 45 cm, L15-H19 = 34 cm, L15-H20 = 61 cm and L15-H22 = 49 cm. Chronology: Pb-210 Cs-137 and Am-241 dating of the uppermost 10 cm of the lake sediment records was undertaken and dating model calculations followed standard procedures defined in Appleby and Oldfield (1978). Pb-210 age estimates were derived using the constant rate of supply (CRS) method (Appleby and Oldfield, 1978) and incorporated into Bayesian age-depth models. The Pb-210 CRS age model produced a low sedimentation rate in the top 10 cm and the sample at around 4-5 cm is likely to be at least 150 years old. Radiocarbon ages of aquatic moss and macrophytic material from the uppermost lithological Unit 3 in Lake L15 were calibrated using the Southern Hemisphere SHCal20 calibration curve in Oxcal v. 4.4 (Hogg et al., 2020). Post-bomb (more than 1950 CE) ages were corrected according to 13C/12C isotopic ratios from measured pMC with the 'present day' pMC value defined as 107.5 percent (2010 CE) and calibrated using the SHCal13 SH Zone 1-2 Bomb curve in CALIBomb (Reimer and Reimer, 2004; Hua et al., 2013). Calibrated ages range from post 1950 CE to c. 0.6 ka cal BP. Geochemistry & Sedimentology: Physical properties (gamma-ray density (GRD), magnetic susceptibility, fractional porosity, resistivity and impedance) were measured using Geotek multi-sensor core logger (MSCL). Non-destructive ITRAXTM (Cox Analytical) micro-X-ray fluorescence (micro-XRF) and Bartington Magnetic Susceptibility High Resolution Surface Scanning Sensor (MS2E) measurements were undertaken at Aberystwyth University. Contiguous bulk, wet sediment geochemical Energy Dispersive XRF-CS (Energy Dispersive Spectroscopy) analysis was obtained using a chromium (Cr) X-ray tube (X-radiography image settings: 40 kV, 40 mA, 200 ms; XRF-CSCr settings: 30 kV, 40 mA, dwell time of 10 seconds, at 100 micrometer or 2 mm. Hyperspectral image (HSI) scanning was performed using the Specim Ltd. single core scanner (PFD-CL-65-V10E line scan camera, 400-1000 nm) according to the protocol of Butz et al. (2015). The spatial resolution (pixel size) was set at 69 micrometer x 69 micrometer and the spectral resolution is 2.8 nm sampled at an interval of 0.78 nm. Raw data were normalised with a BaSO4 reference and spectral endmembers were calculated using the software ENVI 5.03. Quantitative estimates of pigments were obtained using the Relative Absorption Band Depth (RABD) method, which uses ratios and normalised reflectance data from distinct wavelengths. The spectral index RABD660;670 (RABD at 660-670 nm) was calculated from the continuum between 590 and 730 nm (Butz et al., 2015) with I-band wavelengths between 660 and 670 nm using equations RABD660;670 = (6 R590+7 R730)/13/Rmin(660;670) and RABD660;670 (I-Band) = (6 R590+7 R730)/13/ Rmin(660;670)/Rmean (Rein and Sirocko, 2002). Additional statistical analysis was undertaken, and figures constructed, using R v. 4.1.0/RStudio v. 1.4.1717 (primarily packages Tidyverse, ggplot2, Vegan, Rioja, Ggally v. 2.1.2, RBacon, Rcarbon, Bchron), Sigmaplot v. 14.0, C2 (Juggins, 2007), MATLAB v. R2021a, with final layouts achieved in Adobe Illustrator v. 26.2.1 or CorelDRAW v. 2020. Code, data, all packages used, and package references can be found at: https://github.com/stever60/Potter_Peninsula Chronology Pb-210 age estimates were derived using the constant rate of supply (CRS) method (Appleby and Oldfield, 1978) and incorporated into Bayesian age-depth models. The prerequisite for the CRS model was not fulfilled because the flux of Pb-210 has changed through time and is not constant, but Pb-210 data are consistent with the radiocarbon ages that show sediment between 6 and 6.5 cm dates to 620 plus-minus 80 a cal BP, and 3-3.5 cm and 0-0.5 cm depth were deposited in the 'post-bomb' (post-1950 CE) era, most likely between -40 - -44 cal yr BP (1990-1994 CE) The Cs-137 data are inconsistent with the Pb-210 CRS age model and it is possible that the steep increase in Cs-137 in the upper 2 cm relates to a Cs-137 'soil reservoir', which is leaching Cs-137 into the lake from snow or lake-ice melting. Geochemistry and Sedimentology ITRAX-XRF Raw count per second (cps) data were analysed using the Q-spec software v8.6.0 (Cox Analytical), with MSE values minimised to optimise the fit of 'as measured' spectra to a modelled spectrum. Data are presented as percentages of the Total Scatter Normalised ratio sum (percent sigma TSN or, more simply, percent TSN, which are equivalent to percentages of the cps sum, or percent cps) to account for downcore variations in count rate, density, water and organic content. Data less than mean minus two-sigma kcps (mainly due to gaps in the core) and greater than MSE plus two-sigma (representing a poor fit between measured to modelled spectra) were filtered before analysis. 'Noisy' elements were eliminated by comparing cps and using percent TSN thresholds of more than 0.1 percent mean and more than 0.5 percent maximum, and by examining autocorrelation profiles for each element. Elements are presented as natural log (log n or Ln) ratios. The dataset comprises of lake site photos, data and multiproxy data from Lake L15 (aka GPS Lake), a small lake basin at 62.24057 S, 58.6776 W on Potter Peninsula, King George Island, South Shetland Islands. The data have been used to constrain deglaciation and glacier dynamics on Potter Peninsula. Data for the Lake L15 (GPS Lake) sediment record consist of downcore measurements of chronology, geochemistry, and sedimentology proxy data collected from the depocentre in November 2011. Data collected in this study were funded by: Centro de Investigaciones en Ciencias de la Tierra (CICTERRA), the Direccion Nacional del Antartico/Instituto Antartico Argentino (DNA/IAA) in the framework of the Project PICTA, 2011 - 0102, IAA "Geomorfologia y Geologia Glaciar del Archipielago James Ross e Islas Shetland del Sur, Sector Norte de la Peninsula Antartica"; the Alfred Wegener Institute (AWI) research program Polar regions and Coasts in a changing Earth System (PACES II); IMCONet (FP7 IRSES, action no. 318718); the Natural Environment Research Council (NERC/BAS-CGS Grant no.81); the NERC/BAS science programmes CACHE-PEP: Natural climate variability - extending the Americas palaeoclimate transect through the Antarctic Peninsula to the pole and GRADES-QWAD: Quaternary West Antarctic Deglaciations. We thank the crews of the Argentine research station "Carlini" and the adjoined German Dallmann-Labor (AWI) Laboratory, the Uruguayan research station "Artigas", the Russian Bellingshausen Station, the Chinese Great Wall Station, Base Presidente Eduardo Frei Montalva, the Brazilian Navy Almirante Maximiano, the UK Navy HMS Endurance and NERC/BAS James Clark Ross for logistical support during the 2006, 2011, 2014 and 2015 field seasons. Chronology Pb-210 Cs-137 and Am-241 dating of the uppermost 10 cm of the lake sediment records was undertaken on a well-type gamma spectrometry (Ge-detector, GWC 2522-7500 SL, Canberra Industries Inc., USA) and processed with GENIE 2000 3.0 (Canberra Industries Inc., USA). Geochemistry and Sedimentology Geotek multi-sensor core logger (MSCL) ITRAX XRF core scanner fitted with a Molybdenum (Mo) anode X-ray tube and a Bartington Magnetic Susceptibility High Resolution Surface Scanning Sensor (MS2E) Hyperspectral image (HSI) scanning was performed using the Specim Ltd. single core scanner (PFD-CL-65-V10E line scan camera, 400-1000 nm) according to the protocol of Butz et al. (2015).

  • Open Access English
    Authors: 
    Sarker, Abeed;
    Publisher: Zenodo

    This dataset accompanies the article titled: "Can accurate demographic information about people who use prescription medications non-medically be derived from Twitter?" submitted to PNAS. See the README.txt file for more details.

  • Research data . Sound . 2022 . Embargo End Date: 02 Dec 2022
    Open Access

    Chemin de croix dans l'église de Cevo

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  • Research data . Audiovisual . 2022
    Open Access German
    Authors: 
    van Oorschot, Frederike;
    Publisher: Zenodo

    Der Vortrag entfaltet hermeneutische und epistemologische Fragen, die durch die sich entwickelnde digitale Forschung in den Geisteswissenschaften (Digital Humanities, DH) ausgelöst werden. Im Vordergrund steht die Skizze einer auf die DH bezogenen Wissenschaftsphilosophie anhand der folgenden Leitfragen: Was bedeutet das Narrativ einer „neuen“ Wissenschaft? Wer ist Subjekt der DH? Wo finden sich neue epistemische Logiken? Was ist das forschungspolitische Setting der DH? Und wie verhält sich dies alles zum Selbstverständnis „klassischer“ Geisteswissenschaften? Dabei zielt der Vortrag auf eine „digitale Hermeneutik“ in den Geisteswissenschaften.

  • Open Access English
    Authors: 
    Dhrangadhariya, Anjani; Müller, Henning;
    Publisher: Dryad

    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)

  • Open Access
    Authors: 
    Leonardo Santiago Benitez Pereira;
    Publisher: Zenodo

    Collection of 300 support tickets manually labeled for semantic similarity, obtained from a IT support company in the Florianópolis (Brazil) region. Each ticket is represented by an unstructured text field, which is typed by the user that opened the call. The labeling process was performed in 2022 by three IT support professionals. The corpus contains tickets in many languages, mainly English, German, Portuguese and Spanish. All Personal Identifiable Information (PII) and sensitive information were removed (substituted by a tag indicating the original content, for instance: the sentence "this text was written by Leonardo" is converted to "this text was written by [NAME]"). The removal was performed in three steps: first, the automated machine learning-based tool AWS Comprehend PII Removal was used; then, a sequence of custom regular expressions was applied; last, the entire corpus was manually verified.

  • Open Access
    Authors: 
    Jan Moens; Koen De Groote;
    Publisher: Zenodo

    Bijlage bij 'Moens J. & De Groote K. 2022: Ieper - De Meersen. Deel 2. De studie van het leer', een onderzoeksrapport van het agentschap Onroerend Erfgoed: - volledige inventaris van de leervondsten (als .xlsx-bestand)

  • English
    Authors: 
    Bentley, Michael; Roberts, Stephen; Heredia Barion, Pablo; Strenlin, Jorge; Spiegel, Cornelia; Niedermann, Samuel; Wacker, Lukas;
    Publisher: NERC EDS UK Polar Data Centre

    Mapping and Stratigraphic section sample collection Mapping was undertaken in ARC-GIS, with final layouts achieved in Adobe Illustrator v. 26.2.1 or CorelDRAW v. 2020. Lithostratigraphic descriptions and radiocarbon sampling were undertaken at an outer peninsula stratigraphic profile referred to as "new Pingfo II" and a new river section adjacent to the 'Potter Cove section' sampled by Sugden and John (1973). We also sampled terrestrial moss samples for radiocarbon dating from a recently exposed 'Inland outcrop' which is located inside the 1956 CE limit, around 700 m from the active glacier front. The stratigraphic sections were characterised using textural criteria, fabric, composition, sedimentary structures, and grain size analysis to determine the relationships between different depositional units. Radiocarbon (C-14) dating: Twenty-one Accelerator Mass Spectrometry (AMS) radiocarbon (C-14) ages were obtained from seaweed, marine mollusc shells, penguin and undetermined bones, remnants of terrestrial mosses embedded in stratigraphic profiles and moraine sediments. Calibration of marine sample radiocarbon ages (marine shells and seaweed) was undertaken in Oxcal v. 4.4 using the Marine20 calibration curve (Bronk Ramsey, 2009; Heaton et al., 2020), and a newly recalculated local marine reservoir age offset (delta R) of 666 plus-minus 76 C-14 years (Heaton et al., 2020), which represents the weighted mean delta R of four radiocarbon-dated marine samples collected prior to 1950 CE from the northern Antarctic Peninsula and Signy Island in the online Marine20 database (http://calib.org/marine/). Terrestrial and aquatic moss samples were calibrated using the Southern Hemisphere SHCal20 calibration curve in Oxcal v. 4.4 (Hogg et al., 2020). Post-bomb (more than 1950 CE) ages were corrected according to 13C/12C isotopic ratios from measured pMC with the 'present day' pMC value defined as 107.5 percent (2010 CE) and calibrated using the SHCal13 SH Zone 1-2 Bomb curve in CALIBomb (Reimer and Reimer, 2004; Hua et al., 2013). Cosmogenic Helium-3 (He-3) nuclide surface exposure dating (CSED): Five samples were collected for He-3 CSED using a hammer and chisel to remove the upper few centimetres of exposed surfaces. Differential GPS (dGPS) measurements were undertaken using a Trimble Pathfinder ProXH to determine the precise location and altitude of boulders in relation to the landmark DALL 66019M002 (62.23787 degrees S, 58.66455 degrees W, ellipsoidal height 39.376 m) triangulation station located on the Argentine Carlini base, a few hundred metres away from the sampled erratics. dGPS precision is better than 10 cm in all axes, but ellipsoid correction errors are larger. Exposure ages were calculated using the CRONUScalc calculator (Version 2.0; Marrero et al., 2016) with the time-dependent Lal (1991)/Stone (2000) scaling model (Lm) for altitude at Antarctic pressure conditions and the primary calibration data set for He-3 in pyroxene, which yields a long term sea-level high latitude (SLHL) scaled production rate of 122 plus-minus 13 at g-1 a-1 (Borchers et al., 2016). External age uncertainties include production rate uncertainties. Exposure ages determined with other scaling models (e.g., Lifton et al., 2014) vary by up to around 6 percent. We report internal and external uncertainties. Following Balco et al. (2008), external uncertainties are used for comparison with calibrated AMS radiocarbon ages and error ranges. Grain size analysis: Thirteen samples were dry sieved to separate the fraction larger than 2 mm, placed in an ultrasonic bath for 10 seconds, then placed in a reciprocating shaker and left overnight. Samples were wet sieved to separate the fraction less than 0.063 mm (silt and clay size), and the coarser fraction was dried in an oven at 50 degrees Celsius and dry sieved into sand fractions (more than 1mm, more than 0.5 mm, more than 0.25 mm, more than 0.125 mm and more than 0.063 mm). The silt and clay fractions were transferred to a sedimentation cylinder and fine and coarse silt separated from clays after settling using the pipette method. The clay fraction was then concentrated using a centrifuge, and all fractions were dried in an oven at 40 degrees Celsius. Additional statistical analysis was undertaken, and figures constructed, using R v. 4.1.0/RStudio v. 1.4.1717 (primarily packages Tidyverse, ggplot2, Vegan, Rioja, Ggally v. 2.1.2, RBacon, Rcarbon, Bchron), Sigmaplot v. 14.0, C2 (Juggins, 2007), MATLAB v. R2021a, with final layouts achieved in Adobe Illustrator v. 26.2.1 or CorelDRAW v. 2020. Code, data, all packages used and package references can be found at: https://github.com/stever60/Potter Peninsula The dataset comprises of stratigraphic chronological and sedimentological data from Potter Peninsula, King George Island, South Shetland Islands. The data have been used to constrain deglaciation and glacier dynamics on Potter Peninsula. Data collected in this study were funded by: Centro de Investigaciones en Ciencias de la Tierra (CICTERRA), the Direccion Nacional del Antartico/Instituto Antartico Argentino (DNA/IAA) in the framework of the Project PICTA, 2011 - 0102, IAA "Geomorfologia y Geologia Glaciar del Archipielago James Ross e Islas Shetland del Sur, Sector Norte de la Peninsula Antartica"; the Alfred Wegener Institute (AWI) research program Polar regions and Coasts in a changing Earth System (PACES II); IMCONet (FP7 IRSES, action no. 318718); the Natural Environment Research Council (NERC/BAS-CGS Grant no.81); the NERC/BAS science programmes CACHE-PEP: Natural climate variability - extending the Americas palaeoclimate transect through the Antarctic Peninsula to the pole and GRADES-QWAD: Quaternary West Antarctic Deglaciations. We thank the crews of the Argentine research station "Carlini'" and the adjoined German Dallmann-Labor (AWI) Laboratory, the Uruguayan research station "Artigas", the Russian Bellingshausen Station, the Chinese Great Wall Station, Base Presidente Eduardo Frei Montalva, the Brazilian Navy Almirante Maximiano, the UK Navy HMS Endurance and NERC/BAS James Clark Ross for logistical support during the 2006, 2011, 2014 and 2015 field seasons. Radiocarbon samples were prepared at the Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research and British Antarctic Survey. AMS measurements were undertaken at ETH Zurich and Beta Analytical, Miami, and 13C/12C isotope ratios were used to calculate Conventional Radiocarbon Ages. Pre-bomb calibrated ages have been rounded to the nearest 10 years, and to the nearest hundred years in the manuscript text to reflect realistic total (internal and external) uncertainties. Post-bomb ages have been rounded to the nearest year.

  • English
    Authors: 
    Roberts, Stephen; Pearson, Emma; Czalbowski, Tamara; Davies, Sarah; Grosjean, Martin; Arcusa, Stephanie; Perren, Bianca;
    Publisher: NERC EDS UK Polar Data Centre

    Core collection Sediment cores were collected using a Livingston piston corer from the deepest point(~5 m of water depth) in Matias Lake(L5: 62.2450°S, 58.6655°W,~70-75 m a.s.l., 20-30 cm total recovered sediment depth). We extracted 13 short cores from the depocentre in Matias Lake and along a surface transect towards Rudy Lake. Sediment recovery depth ranged from 20 and 60 cm before encountering an impenetrable diamicton layer. Data from cores MAT1 (L5-H1) (27 cm) and MAT2 (L5-H2) (29 cm) extracted from the deepest part of Matias Lake (5.8 m; 62° 14' 42.054"S, 58° 39' 53.82"W) are included in this dataset. Chronology Obtaining basal radiocarbon ages from basal and bulk minerogenic sediments in the Matias Lake cores proved challenging due to a general lack of organic carbon.Pb-210 Cs-137 and Am-241 dating of the uppermost 10 cm of the lake sediment records was undertaken and dating model calculations followed standard procedures defined in Appleby and Oldfield (1978). Pb-210 age estimates were derived using the constant rate of supply (CRS) method (Appleby and Oldfield, 1978) and incorporated into Bayesian age-depth models. The Pb-210 Constant Rate of Supply (CRS) age model shows that the uppermost 10 cm have been deposited since c. 1850 CE, and the well-defined 137Cs peak at 5 cm depth is coherent with the Pb-210 age model. Geochemistry and Sedimentology Physical properties (gamma-ray density (GRD), magnetic susceptibility, fractional porosity, resistivity and impedance) were measured using Geotek® multi-sensor core logger (MSCL). Non-destructive ITRAXTM (Cox Analytical) micro-X-ray fluorescence (micro-XRF) and Bartington Magnetic Susceptibility High Resolution Surface Scanning Sensor (MS2E) measurements were undertaken at Aberystwyth University. Contiguous bulk, wet sediment geochemical Energy Dispersive XRF-CS (Energy Dispersive Spectroscopy) analysis was obtained using a chromium (Cr) X-ray tube (X-radiography image settings: 40 kV, 40 mA, 200 ms; XRF-CSCr settings: 30 kV, 40 mA, dwell time of 10 seconds, at 100 micrometers or 2 mm. Hyperspectral image (HSI) scanning was performed using the Specim Ltd. single core scanner (PFD-CL-65-V10E line scan camera, 400-1000 nm) according to the protocol of Butz et al. (2015). The spatial resolution (pixel size) was set at 69 micrometers x 69 micrometers and the spectral resolution is 2.8 nm sampled at an interval of 0.78 nm. Raw data were normalised with a BaSO4 reference and spectral endmembers were calculated using the software ENVI 5.03. Quantitative estimates of pigments were obtained using the Relative Absorption Band Depth (RABD) method, which uses ratios and normalised reflectance data from distinct wavelengths. The spectral index RABD660;670 (RABD at 660-670 nm) was calculated from the continuum between 590 and 730 nm (Butz et al., 2015) with I-band wavelengths between 660 and 670 nm using equations RABD660;670 = (6*R590+7*R730)/13/Rmin(660;670) and RABD660;670 [I-Band] = (6*R590+7*R730)/13/ Rmin(660;670)/Rmean (Rein and Sirocko, 2002). Additional statistical analysis was undertaken, and figures constructed, using R v. 4.1.0/RStudio v. 1.4.1717 (primarily packages Tidyverse, ggplot2, Vegan, Rioja, Ggally v. 2.1.2, RBacon, Rcarbon, Bchron), Sigmaplot v. 14.0, C2 (Juggins, 2007), MATLAB v. R2021a, with final layouts achieved in Adobe Illustrator v. 26.2.1 or CorelDRAW v. 2020. Code, data, all packages used, and package references can be found at: https://github.com/stever60/Potter_Peninsula The dataset comprises of lake site photos, data and multiproxy data from Lake L5 (aka Matias Lake), a small lake basin at 62.2450 S, 58.6655 W on Potter Peninsula, King George Island, South Shetland Islands. The data have been used to constrain deglaciation and glacier dynamics on Potter Peninsula. Data for the Lake L5 (Matias Lake) sediment record consist of downcore measurements of chronology, geochemistry, and sedimentology proxy data collected from the depocentre in November 2011. Data collected in this study were funded by: Centro de Investigaciones en Ciencias de la Tierra (CICTERRA), the Direccion Nacional del Antartico/Instituto Antartico Argentino (DNA/IAA) in the framework of the Project PICTA, 2011 - 0102, IAA and Geomorfologia y Geologia Glaciar del Archipielago James Ross e Islas Shetland del Sur, Sector Norte de la Peninsula Antartica, the Alfred Wegener Institute (AWI) research program Polar regions and Coasts in a changing Earth System (PACES II); IMCONet (FP7 IRSES, action no. 318718); the Natural Environment Research Council (NERC/BAS-CGS Grant no.81); the NERC/BAS science programmes CACHE-PEP: Natural climate variability - extending the Americas palaeoclimate transect through the Antarctic Peninsula to the pole and GRADES-QWAD: Quaternary West Antarctic Deglaciations. We thank the crews of the Argentine research station "Carlini" and the adjoined German Dallmann-Labor (AWI) Laboratory, the Uruguayan research station "Artigas", the Russian Bellingshausen Station, the Chinese Great Wall Station, Base Presidente Eduardo Frei Montalva, the Brazilian Navy Almirante Maximiano, the UK Navy HMS Endurance and NERC/BAS James Clark Ross for logistical support during the 2006, 2011, 2014 and 2015 field seasons. Chronology Pb-210 age estimates were derived using the constant rate of supply (CRS) method (Appleby and Oldfield, 1978) and incorporated into Bayesian age-depth models. Geochemistry & Sedimentology ITRAX-XRF Raw count per second (cps) data were analysed using the Q-spec software v8.6.0 (Cox Analytical), with MSE values minimised to optimise the fit of 'as measured' spectra to a modelled spectrum. Data are presented as percentages of the Total Scatter Normalised ratio sum (%∑TSN or, more simply, %TSN, which are equivalent to percentages of the cps sum, or %cps) to account for downcore variations in count rate, density, water and organic content. Data less than mean minus two-sigma kcps (mainly due to gaps in the core) and greater than MSE plus two-sigma (representing a poor fit between measured to modelled spectra) were filtered before analysis.?Noisy? elements were eliminated by comparing cps and using %TSN thresholds of >0.1% mean and >0.5% maximum , and by examining autocorrelation profiles for each element. Elements are presented as natural log (log n or Ln) ratios. Chronology Pb-210 Cs-137 and Am-241 dating of the uppermost 10 cm of the lake sediment records was undertaken on a well-type gamma spectrometry (Ge-detector, GWC 2522-7500 SL, Canberra Industries Inc., USA) and processed with GENIE 2000 3.0 (Canberra Industries Inc., USA). Geochemistry and Sedimentology ITRAX XRF core scanner fitted with a Molybdenum (Mo) anode X-ray tube and a Bartington Magnetic Susceptibility High Resolution Surface Scanning Sensor (MS2E) Geotek multi-sensor core logger (MSCL) Hyperspectral image (HSI) scanning was performed using the Specim Ltd. single core scanner (PFD-CL-65-V10E line scan camera, 400?1000 nm) according to the protocol of Butz et al. (2015). GEOTEK MSCL data were measured at 2 mm intervals Geochemical XRF-Core scanning data were measured at 100 μm and 2 mm contiguous intervals. SPECIM hyperspectral data were measured at 69 μm intervals

  • English
    Authors: 
    Roberts, Stephen; Hocking, Emma; Heredia Barion, Pablo;
    Publisher: NERC EDS UK Polar Data Centre

    The dataset comprises of compilations of new and published age data constraining glacier advance, retreat and aquatic moss layers in lakes from the South Shetland Islands. The data were used in data analysis in the following manuscripts to constrain deglaciation and glacier dynamics on Potter Peninsula and Fildes Peninsula, King George Island South Shetland Islands: Heredia Barion P, Roberts SJ, Spiegel C, Binnie SA, Wacker L, Davies J, et al. (submitted - a) Mid-late Holocene deglaciation and glacier readvances on the Fildes Peninsula, King George Island, NW Antarctic Peninsula. The Holocene. Heredia Barion P, Strelin JA, Roberts SJ, Spiegel C, Wacker L, Niedermann S, et al. (submitted - b). Holocene deglaciation, glacial dynamics and the geomorphology of Potter Peninsula, King George Island (Isla 25 de Mayo), NW Antarctic Peninsula. Frontiers in Earth Science. Data collected in this study were funded by: Centro de Investigaciones en Ciencias de la Tierra (CICTERRA), the Direccion Nacional del Antartico/Instituto Antartico Argentino (DNA/IAA) in the framework of the Project PICTA, 2011 - 0102, IAA "Geomorfologia y Geologia Glaciar del Archipielago James Ross e Islas Shetland del Sur, Sector Norte de la Peninsula Antartica"; the Alfred Wegener Institute (AWI) research program Polar regions and Coasts in a changing Earth System (PACES II); IMCONet (FP7 IRSES, action no. 318718); the Natural Environment Research Council (NERC/BAS-CGS Grant no.81); the NERC/BAS science programmes CACHE-PEP: Natural climate variability - extending the Americas palaeoclimate transect through the Antarctic Peninsula to the pole and GRADES-QWAD: Quaternary West Antarctic Deglaciations. We thank the crews of the Argentine research station "Carlini" and the adjoined German Dallmann-Labor (AWI) Laboratory, the Uruguayan research station "Artigas", the Russian Bellingshausen Station, the Chinese Great Wall Station, Base Presidente Eduardo Frei Montalva, the Brazilian Navy Almirante Maximiano, the UK Navy HMS Endurance and NERC/BAS James Clark Ross for logistical support during the 2006, 2011, 2014 and 2015 field seasons. Compilations of new and published age data constraining glacier advance, retreat and aquatic moss layers in lakes from the South Shetland Islands used in data analysis in the following manuscripts to constrain deglaciation and glacier dynamics on Potter Peninsula and Fildes Peninsula, King George Island South Shetland Islands: Heredia Barion P, Roberts SJ, Spiegel C, Binnie SA, Wacker L, Davies J, et al. (submitted - a) Mid-late Holocene deglaciation and glacier readvances on the Fildes Peninsula, King George Island, NW Antarctic Peninsula. The Holocene. Heredia Barion P, Strelin JA, Roberts SJ, Spiegel C, Wacker L, Niedermann S, et al. (submitted - b). Holocene glacial dynamics and geomorphology of Potter Peninsula, King George Island (NW Antarctic Peninsula). Frontiers in Earth Science. Additional statistical analysis was undertaken, and figures constructed, using R v. 4.1.0/RStudio v. 1.4.1717 (primarily packages Tidyverse, ggplot2, Vegan, Rioja, Ggally v. 2.1.2, RBacon, Rcarbon, Bchron), Sigmaplot v. 14.0, C2 (Juggins, 2007), MATLAB v. R2021a, with final layouts achieved in Adobe Illustrator v. 26.2.1 or CorelDRAW v. 2020. Code, data, all packages used and package references can be found at: https://github.com/stever60/Potter Peninsula

  • English
    Authors: 
    Roberts, Stephen; Pearson, Emma; Czalbowski, Tamara; Davies, Sarah; Grosjean, Martin; Arcusa, Stephanie; Perren, Bianca;
    Publisher: NERC EDS UK Polar Data Centre

    Core collection: Sediment cores were collected using a Russian and Livingston corers from the deepest point (around 2 m of water depth) in Lake L15 (GPS Lake) (L15: -62.24057 degrees S, -58.67760 degrees W, around 30 m a.s.l., 11-55 cm total recovered sediment depth). Eight cores were collected from a grid of 25 (around1 m spaced) holes drilled through the around 75-100 cm thick lake ice above the deepest accessible point (around 2 m): Data from L15-H2 (-62.24057 degrees S, -58.67760 degrees W) with 30 cm of sediment and L15-H16 (-62.24056 degrees S, -58.67757 degrees W) with 55 cm of sediment are included in this dataset. Other cores taken at this site were: L15-H4 = 11 cm, L15-H9 = 39 cm, L15-H17 = 45 cm, L15-H19 = 34 cm, L15-H20 = 61 cm and L15-H22 = 49 cm. Chronology: Pb-210 Cs-137 and Am-241 dating of the uppermost 10 cm of the lake sediment records was undertaken and dating model calculations followed standard procedures defined in Appleby and Oldfield (1978). Pb-210 age estimates were derived using the constant rate of supply (CRS) method (Appleby and Oldfield, 1978) and incorporated into Bayesian age-depth models. The Pb-210 CRS age model produced a low sedimentation rate in the top 10 cm and the sample at around 4-5 cm is likely to be at least 150 years old. Radiocarbon ages of aquatic moss and macrophytic material from the uppermost lithological Unit 3 in Lake L15 were calibrated using the Southern Hemisphere SHCal20 calibration curve in Oxcal v. 4.4 (Hogg et al., 2020). Post-bomb (more than 1950 CE) ages were corrected according to 13C/12C isotopic ratios from measured pMC with the 'present day' pMC value defined as 107.5 percent (2010 CE) and calibrated using the SHCal13 SH Zone 1-2 Bomb curve in CALIBomb (Reimer and Reimer, 2004; Hua et al., 2013). Calibrated ages range from post 1950 CE to c. 0.6 ka cal BP. Geochemistry & Sedimentology: Physical properties (gamma-ray density (GRD), magnetic susceptibility, fractional porosity, resistivity and impedance) were measured using Geotek multi-sensor core logger (MSCL). Non-destructive ITRAXTM (Cox Analytical) micro-X-ray fluorescence (micro-XRF) and Bartington Magnetic Susceptibility High Resolution Surface Scanning Sensor (MS2E) measurements were undertaken at Aberystwyth University. Contiguous bulk, wet sediment geochemical Energy Dispersive XRF-CS (Energy Dispersive Spectroscopy) analysis was obtained using a chromium (Cr) X-ray tube (X-radiography image settings: 40 kV, 40 mA, 200 ms; XRF-CSCr settings: 30 kV, 40 mA, dwell time of 10 seconds, at 100 micrometer or 2 mm. Hyperspectral image (HSI) scanning was performed using the Specim Ltd. single core scanner (PFD-CL-65-V10E line scan camera, 400-1000 nm) according to the protocol of Butz et al. (2015). The spatial resolution (pixel size) was set at 69 micrometer x 69 micrometer and the spectral resolution is 2.8 nm sampled at an interval of 0.78 nm. Raw data were normalised with a BaSO4 reference and spectral endmembers were calculated using the software ENVI 5.03. Quantitative estimates of pigments were obtained using the Relative Absorption Band Depth (RABD) method, which uses ratios and normalised reflectance data from distinct wavelengths. The spectral index RABD660;670 (RABD at 660-670 nm) was calculated from the continuum between 590 and 730 nm (Butz et al., 2015) with I-band wavelengths between 660 and 670 nm using equations RABD660;670 = (6 R590+7 R730)/13/Rmin(660;670) and RABD660;670 (I-Band) = (6 R590+7 R730)/13/ Rmin(660;670)/Rmean (Rein and Sirocko, 2002). Additional statistical analysis was undertaken, and figures constructed, using R v. 4.1.0/RStudio v. 1.4.1717 (primarily packages Tidyverse, ggplot2, Vegan, Rioja, Ggally v. 2.1.2, RBacon, Rcarbon, Bchron), Sigmaplot v. 14.0, C2 (Juggins, 2007), MATLAB v. R2021a, with final layouts achieved in Adobe Illustrator v. 26.2.1 or CorelDRAW v. 2020. Code, data, all packages used, and package references can be found at: https://github.com/stever60/Potter_Peninsula Chronology Pb-210 age estimates were derived using the constant rate of supply (CRS) method (Appleby and Oldfield, 1978) and incorporated into Bayesian age-depth models. The prerequisite for the CRS model was not fulfilled because the flux of Pb-210 has changed through time and is not constant, but Pb-210 data are consistent with the radiocarbon ages that show sediment between 6 and 6.5 cm dates to 620 plus-minus 80 a cal BP, and 3-3.5 cm and 0-0.5 cm depth were deposited in the 'post-bomb' (post-1950 CE) era, most likely between -40 - -44 cal yr BP (1990-1994 CE) The Cs-137 data are inconsistent with the Pb-210 CRS age model and it is possible that the steep increase in Cs-137 in the upper 2 cm relates to a Cs-137 'soil reservoir', which is leaching Cs-137 into the lake from snow or lake-ice melting. Geochemistry and Sedimentology ITRAX-XRF Raw count per second (cps) data were analysed using the Q-spec software v8.6.0 (Cox Analytical), with MSE values minimised to optimise the fit of 'as measured' spectra to a modelled spectrum. Data are presented as percentages of the Total Scatter Normalised ratio sum (percent sigma TSN or, more simply, percent TSN, which are equivalent to percentages of the cps sum, or percent cps) to account for downcore variations in count rate, density, water and organic content. Data less than mean minus two-sigma kcps (mainly due to gaps in the core) and greater than MSE plus two-sigma (representing a poor fit between measured to modelled spectra) were filtered before analysis. 'Noisy' elements were eliminated by comparing cps and using percent TSN thresholds of more than 0.1 percent mean and more than 0.5 percent maximum, and by examining autocorrelation profiles for each element. Elements are presented as natural log (log n or Ln) ratios. The dataset comprises of lake site photos, data and multiproxy data from Lake L15 (aka GPS Lake), a small lake basin at 62.24057 S, 58.6776 W on Potter Peninsula, King George Island, South Shetland Islands. The data have been used to constrain deglaciation and glacier dynamics on Potter Peninsula. Data for the Lake L15 (GPS Lake) sediment record consist of downcore measurements of chronology, geochemistry, and sedimentology proxy data collected from the depocentre in November 2011. Data collected in this study were funded by: Centro de Investigaciones en Ciencias de la Tierra (CICTERRA), the Direccion Nacional del Antartico/Instituto Antartico Argentino (DNA/IAA) in the framework of the Project PICTA, 2011 - 0102, IAA "Geomorfologia y Geologia Glaciar del Archipielago James Ross e Islas Shetland del Sur, Sector Norte de la Peninsula Antartica"; the Alfred Wegener Institute (AWI) research program Polar regions and Coasts in a changing Earth System (PACES II); IMCONet (FP7 IRSES, action no. 318718); the Natural Environment Research Council (NERC/BAS-CGS Grant no.81); the NERC/BAS science programmes CACHE-PEP: Natural climate variability - extending the Americas palaeoclimate transect through the Antarctic Peninsula to the pole and GRADES-QWAD: Quaternary West Antarctic Deglaciations. We thank the crews of the Argentine research station "Carlini" and the adjoined German Dallmann-Labor (AWI) Laboratory, the Uruguayan research station "Artigas", the Russian Bellingshausen Station, the Chinese Great Wall Station, Base Presidente Eduardo Frei Montalva, the Brazilian Navy Almirante Maximiano, the UK Navy HMS Endurance and NERC/BAS James Clark Ross for logistical support during the 2006, 2011, 2014 and 2015 field seasons. Chronology Pb-210 Cs-137 and Am-241 dating of the uppermost 10 cm of the lake sediment records was undertaken on a well-type gamma spectrometry (Ge-detector, GWC 2522-7500 SL, Canberra Industries Inc., USA) and processed with GENIE 2000 3.0 (Canberra Industries Inc., USA). Geochemistry and Sedimentology Geotek multi-sensor core logger (MSCL) ITRAX XRF core scanner fitted with a Molybdenum (Mo) anode X-ray tube and a Bartington Magnetic Susceptibility High Resolution Surface Scanning Sensor (MS2E) Hyperspectral image (HSI) scanning was performed using the Specim Ltd. single core scanner (PFD-CL-65-V10E line scan camera, 400-1000 nm) according to the protocol of Butz et al. (2015).

  • Open Access English
    Authors: 
    Sarker, Abeed;
    Publisher: Zenodo

    This dataset accompanies the article titled: "Can accurate demographic information about people who use prescription medications non-medically be derived from Twitter?" submitted to PNAS. See the README.txt file for more details.

  • Research data . Sound . 2022 . Embargo End Date: 02 Dec 2022
    Open Access

    Chemin de croix dans l'église de Cevo