cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4653562
title: RESPOND Dataset - Conflicting Europeanization

description: Work Package 6 asked partners to investigate the impact of migration debates on competing discourses of Europeanization. The intention was to chart, on a comparative basis, changing national contestation of belonging to a European community. Below we present qualitative data collected from a sample of political speeches and media sources in six EU countries: Sweden, Greece, Hungary, Germany, Italy and Poland. Heuristic and thematic models were derived from a literature review of theoretical debates on the post-crisis contestation of “Europe”, with particular focus on themes of identity, sovereignty and borders. This allowed for a unified comparative perspective on conflicts over Europeanisation, which formed the basis of country reports and the comparative report. For political speeches, teams were presented with an analytical grid for analysing statements by political leaders in their respective countries. This was arranged into three core categories of analysis: the polity (EU or member state); the audience (who was talked to/who was talked about); and migration-related propositions for action. Teams were asked to extract keyword phrases exemplifying themes. A similar approach was taken with analysis of media texts, with a four-part analytical grid divided into EU institutions (e.g. “conflict with Commission”); European values (e.g. “solidarity”); migration/refugees (e.g. “hordes”); and identity frames (e.g. “insiders/outsiders”). This allowed us to undertake comparative analysis which formed the groundwork of the Work Package 6 comparative report. This work package was a joint project under the leadership of Glasgow Caledonian University (GCU) and the Austrian Academy of Sciences (OEAW). The following partners participated: • Uppsala University (dataset entries from Sweden) • Göttingen University (dataset entries from Germany) • Glasgow Caledonian University (dataset entries Hungary) • University of Florence (dataset entries from Italy) • University of Aegean (dataset entries from Greece) • University of Warsaw (dataset entries from Poland) • Austrian Academy of Sciences (dataset entries from Austria)
DOI: 10.5281/zenodo.4653561
title: RESPOND Dataset - Conflicting Europeanization

description: Work Package 6 asked partners to investigate the impact of migration debates on competing discourses of Europeanization. The intention was to chart, on a comparative basis, changing national contestation of belonging to a European community. Below we present qualitative data collected from a sample of political speeches and media sources in six EU countries: Sweden, Greece, Hungary, Germany, Italy and Poland. Heuristic and thematic models were derived from a literature review of theoretical debates on the post-crisis contestation of “Europe”, with particular focus on themes of identity, sovereignty and borders. This allowed for a unified comparative perspective on conflicts over Europeanisation, which formed the basis of country reports and the comparative report. For political speeches, teams were presented with an analytical grid for analysing statements by political leaders in their respective countries. This was arranged into three core categories of analysis: the polity (EU or member state); the audience (who was talked to/who was talked about); and migration-related propositions for action. Teams were asked to extract keyword phrases exemplifying themes. A similar approach was taken with analysis of media texts, with a four-part analytical grid divided into EU institutions (e.g. “conflict with Commission”); European values (e.g. “solidarity”); migration/refugees (e.g. “hordes”); and identity frames (e.g. “insiders/outsiders”). This allowed us to undertake comparative analysis which formed the groundwork of the Work Package 6 comparative report. This work package was a joint project under the leadership of Glasgow Caledonian University (GCU) and the Austrian Academy of Sciences (OEAW). The following partners participated: • Uppsala University (dataset entries from Sweden) • Göttingen University (dataset entries from Germany) • Glasgow Caledonian University (dataset entries Hungary) • University of Florence (dataset entries from Italy) • University of Aegean (dataset entries from Greece) • University of Warsaw (dataset entries from Poland) • Austrian Academy of Sciences (dataset entries from Austria)
DOI: 10.6084/m9.figshare.12885524
title: Mass-Spec raw data

description: Mass-Spec of His-JAK2 IP
DOI: 10.6084/m9.figshare.12885524.v5
title: Mass-Spec raw data

description: Mass-Spec of His-JAK2 IP
DOI: 10.6084/m9.figshare.12885524.v4
title: Mass-Spec raw data

description: Mass-Spec of His-JAK2 IP
DOI: 10.5281/zenodo.4646813
title: DATABASE: RESPOND Survey in Sweden

description: The survey focuses on Syrian migrants (18 years or older) arriving Sweden in 2011 and upward and was conducted in Sweden between June 2019 and May 2020. Respondents were recruited in multiple locations in Sweden, categorized broadly according to the administrative division of the Swedish municipalities (Statistiska Centralbyrån, 2010). Five regional groups were categorized: Stockholm region, Malmö region, Göteborg region, middle region (cities) and small region (villages and towns).
DOI: 10.5281/zenodo.4646812
title: DATABASE: RESPOND Survey in Sweden

description: The survey focuses on Syrian migrants (18 years or older) arriving Sweden in 2011 and upward and was conducted in Sweden between June 2019 and May 2020. Respondents were recruited in multiple locations in Sweden, categorized broadly according to the administrative division of the Swedish municipalities (Statistiska Centralbyrån, 2010). Five regional groups were categorized: Stockholm region, Malmö region, Göteborg region, middle region (cities) and small region (villages and towns).
DOI: 10.5281/zenodo.4018050
title: Database: RESPOND Survey in Turkey

description: The following folder provides an introduction text and the survey database itself (both in SPSS and Excel formats). The survey was part of Work package 7 of the Horizon 2020 RESPOND project (770564), and conducted between June and October 2019 in four Turkish cities: Istanbul (234 interviews), Şanliurfa (205 interviews), Izmir (199 interviews) and Batman (151interviews). The final samples includes 789 respondents.
DOI: 10.5281/zenodo.4018049
title: Database: RESPOND Survey in Turkey

description: The following folder provides an introduction text and the survey database itself (both in SPSS and Excel formats). The survey was part of Work package 7 of the Horizon 2020 RESPOND project (770564), and conducted between June and October 2019 in four Turkish cities: Istanbul (234 interviews), Şanliurfa (205 interviews), Izmir (199 interviews) and Batman (151interviews). The final samples includes 789 respondents.
DOI: 10.5281/zenodo.4653288
title: OVATION-PRIME-2013 Met Office Nowcast Verification Dataset

description: <pre>The accompanying data is made available under the terms of the Non-Commercial Government Licence (http://www.nationalarchives.gov.uk/doc/non-commercial-government-licence/version/2/) The auroral nowcast files have been produced using the version of the OVATION-Prime 2013 (OP-2013) model that was used operationally at the UK Met Office until December 2020. The auroral nowcast data has been hindcast for this study using historic solar wind measurements input to the OP-2013 model. Model files span 01-05-2000 - 31-10-2002. The model output is provided in magnetic coordinates. The time in the filename is the time of the hourly forecast cycle output. The time listed at the top of the file is 30 mins prior to the forecast cycle ouput time i.e. the model run time, or time of the input data used to produce the 30 min nowcast. The data archive also contains a file (log_fail.txt) that logs the forecast cycles which failed during this period due to missing input data. Column 1 (mlt) is the magnetic local time (MLT) in hours of local time, with a grid resolution of 0.25 hours of MLT. Column 2 (mlat) is the magnetic latitude in degrees, with a grid resolution of 0.5 degrees. Column 3 (je) is the auroral flux calculated for each grid cell in erg/cm^2 s. Column 4 (probabilities) is the probability of aurora occurring in each grid cell as calculated from the auroral flux for each grid cell in Column 3. </pre>
DOI: 10.5281/zenodo.4653287
title: OVATION-PRIME-2013 Met Office Nowcast Verification Dataset

description: <pre>The accompanying data is made available under the terms of the Non-Commercial Government Licence (http://www.nationalarchives.gov.uk/doc/non-commercial-government-licence/version/2/) The auroral nowcast files have been produced using the version of the OVATION-Prime 2013 (OP-2013) model that was used operationally at the UK Met Office until December 2020. The auroral nowcast data has been hindcast for this study using historic solar wind measurements input to the OP-2013 model. Model files span 01-05-2000 - 31-10-2002. The model output is provided in magnetic coordinates. The time in the filename is the time of the hourly forecast cycle output. The time listed at the top of the file is 30 mins prior to the forecast cycle ouput time i.e. the model run time, or time of the input data used to produce the 30 min nowcast. The data archive also contains a file (log_fail.txt) that logs the forecast cycles which failed during this period due to missing input data. Column 1 (mlt) is the magnetic local time (MLT) in hours of local time, with a grid resolution of 0.25 hours of MLT. Column 2 (mlat) is the magnetic latitude in degrees, with a grid resolution of 0.5 degrees. Column 3 (je) is the auroral flux calculated for each grid cell in erg/cm^2 s. Column 4 (probabilities) is the probability of aurora occurring in each grid cell as calculated from the auroral flux for each grid cell in Column 3. </pre>
DOI: 10.5066/p9sqv3sb
title: High-Resolution Airborne Gravity Gradiometry, Magnetic, and Radiometric Data of Mountain Pass, Southeast Mojave Desert, California

description: A detailed airborne gravity gradiometry, magnetic, and radiometric survey of Mountain Pass, California was flown by CGG Canada Services Ltd. (CGG). The high-resolution helicopter survey was flown at a flight-line spacing of 100 and 200 m, a flight-line azimuth of 70 degrees, a nominal flight-line elevation above ground of 70 m, and consists of about 1,814 line-kilometers. Tie lines were spaced at a 1-km interval with a flight-line azimuth of 160 degrees. Data were collected using a HeliFALCON airborne gravity gradiometry system, Scintrex CS-3 cesium magnetometer, Radiation Solutions RS-500 spectrometer, and Riegl LMS-Q1401-80n laser scanner and processed by CGG. Gravity gradiometry data include corrections for residual aircraft motion, self gradient, terrain corrections, and tie-line and micro-levelling. Magnetic data were corrected by the contractor for diurnal variations of the Earth?s magnetic field, tie-line leveled, micro-leveled, and an International Geomagnetic Reference Field of the Earth was removed. Radiometric data include corrections for aircraft and cosmic background radiation, radon background, Compton scattering effects, and variations in altitude. Data are provided in ASCII (.csv) and Geosoft database (.gdb) format, database channels and descriptions are listed in the survey report, and grids of gravity and hillshade are in ASCII Grid eXchange Format (.gxf). Maps and grids of magnetic and radiometric data were released by Ponce and Denton (2018a-d). References: Ponce, D.A., and Denton, K.M., 2018a, Aeromagnetic map of Mountain Pass and vicinity, California and Nevada: U.S. Geological Survey Scientific Investigations Map 3412-B, 6 p., 1 pl., scale 1:62,500, https://doi.org/10.3133/sim3412B. Ponce, D.A., and Denton, K.M., 2018b, High-resolution aeromagnetic survey of Mountain Pass, California: U.S. Geological Survey data release, https://doi.org/doi:10.5066/P92XVOOF. Ponce, D.A., and Denton, K.M., 2018c, Airborne radiometric maps of Mountain Pass, California: U.S. Geological Survey Scientific Investigations Map 3412-C, 6 p., 1 pl., scale 1:62,500, https://doi.org/10.3133/sim3412C. Ponce, D.A., and Denton, K.M., 2018d, High-resolution airborne radiometric survey of Mountain Pass, California: U.S. Geological Survey data release, https://doi.org/10.5066/P9ENLS6D.
DOI: 10.5281/zenodo.4653628
title: Trail Running Races 2020-2029

description: Extraction of ITRA races from 2020 to 2029. Extraction date was April, 2021 so there were no races after June, 2022. Dataset can be updated running the script located on https://github.com/amurgo/TrailRunningRaces
DOI: 10.5281/zenodo.4649514
title: Environmental drivers of three neighbouring monodominant stands in Pantanal wetland

description: These data were used in the publication of the paper entitled "<strong>Environmental drivers of three neighbouring monodominant stands in Pantanal wetland" </strong>published in the Journal of Vegetation Science. Enviali.csv are the environmental data (Soil chemicals, Soil texture, and inundation) and Specali.csv are data from the abundance of species in the sampled monodominant stands. The units of environmental data are P, S, B, Cu. Fe, Mn, and Zn = mg/dm3; Organic Matter - g/dm3; K, Ca, Mg, H+Al, and Al - mmolc/dm3. Inundation (Water total) is in cm. Sand (coarse and fine), Silt and Clay are in g/Kg.
DOI: 10.5281/zenodo.4649513
title: Environmental drivers of three neighbouring monodominant stands in Pantanal wetland

description: These data were used in the publication of the paper entitled "<strong>Environmental drivers of three neighbouring monodominant stands in Pantanal wetland" </strong>published in the Journal of Vegetation Science. Enviali.csv are the environmental data (Soil chemicals, Soil texture, and inundation) and Specali.csv are data from the abundance of species in the sampled monodominant stands. The units of environmental data are P, S, B, Cu. Fe, Mn, and Zn = mg/dm3; Organic Matter - g/dm3; K, Ca, Mg, H+Al, and Al - mmolc/dm3. Inundation (Water total) is in cm. Sand (coarse and fine), Silt and Clay are in g/Kg.
DOI: 10.5066/p9w6khk2
title: Historical groundwater and produced water chemistry data compiled for the San Ardo Oil Field and vicinity, Monterey County, central California

description: This digital dataset includes 255 samples from 41 groundwater wells and 271 samples of produced water from 143 sites (including 101 wells) located within 3 miles of the San Ardo Oil Field in Monterey County, central California. Historical geochemistry data were compiled from multiple data sources for selected water-quality indicators, major and minor ions, nutrients, and trace elements. Ancillary data in the form of top perforation or top of sample interval (petroleum wells), bottom perforation or bottom of sample interval (petroleum wells), well depth, or hole depth (water wells), land-surface (water wells) or reference point (petroleum wells) elevation at the well head, well location, and identifier information were also compiled.
DOI: 10.6084/m9.figshare.12730796
title: Data for Sunday et al. 2020 submission

description: See Sunday et al., 2020 "Novel and familiar object recognition rely on the same ability" for details
DOI: 10.6084/m9.figshare.12730796.v1
title: Data for Sunday et al. 2020 submission

description: See Sunday et al., 2020 "Novel and familiar object recognition rely on the same ability" for details
DOI: 10.5281/zenodo.4627952
title: NASA SPoRT Dust Event Labels

description: GENERAL INFORMATION 1. Title of Dataset: SPoRT Dust Event Labels 2. Author Information: A. Nicholas Elmer<br> NASA Postdoctoral Program<br> NASA Marshall Space Flight Center<br> Huntsville, Alabama, USA<br> nicholas.j.elmer@nasa.gov B. Emily Berndt<br> Earth Science Office<br> NASA Marshall Space Flight Center<br> Huntsville, Alabama, USA<br> emily.b.berndt@nasa.gov 3. Date of data collection: 2018-01-14 to 2020-06-09 4. Geographic location of data collection: Southwest United States<br> West longitude: 126.0 W<br> East longitude: 90.0 W<br> South latitude: 24.0 N<br> North latitude: 45.0 N 5. Funding source: <br> Data collection was supported by the NASA Short-term Prediction Research and Transition (SPoRT)<br> project at NASA Marshall Space Flight Center. DATA & FILE OVERVIEW 1. File List:<br> testing_dataset.txt<br> training_dataset.txt<br> validation_dataset.txt<br> Georeferenced polygon shapefiles, comprising .shp, .shx, .dbf, and .prj files with timestamp {YYYY}{MM}{DD}T{HH}{MM}{SS}. 2. Relationship between files:<br> This dataset contains:<br> 1) Georeferenced (WGS 1984) polygon shapefiles containing image classification for airborne dust.<br> 2) Text files listing the timestamp of shapefiles used in the training, testing, and validation datasets<br> used by the Berndt et al. (2021) random forest dust detection model. METHODOLOGICAL INFORMATION. 1. Description of methods for collection:<br> The dust labels were manually assigned by atmospheric scientists based largely on the GOES-16 ABI Dust RGB imagery but<br> supplemented by GOES-16 true color imagery, Area Forecast Discussions issued by NOAA National Weather Service<br> Weather Forecast Offices, and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO)<br> measurements. 2. Methods for processing the data:<br> GOES-16 ABI Dust RGB imagery was downloaded from Amazon Web Services and regridded to a 2-km rectangular grid. Feature labels were manually drawn on the imagery and classified by experts with the aid of a Python Graphical User Interface (GUI) based on the Tkinter python package. DATA-SPECIFIC INFORMATION FOR SHAPEFILES: 1. Shapefile coordinate system: WGS 1984 2. Number of Shapefiles: 83 3. Number of Polygons per Shapefile: Varies 4. Number of Attributes per polygon: 1 5. Attribute list:<br> A. Class: 0 --&gt; No Dust<br> 1 --&gt; Dust<br> 2 --&gt; Reserved for future use<br> 3 --&gt; No Data Value
DOI: 10.5281/zenodo.4627951
title: NASA SPoRT Dust Event Labels

description: GENERAL INFORMATION 1. Title of Dataset: SPoRT Dust Event Labels 2. Author Information: A. Nicholas Elmer<br> NASA Postdoctoral Program<br> NASA Marshall Space Flight Center<br> Huntsville, Alabama, USA<br> nicholas.j.elmer@nasa.gov B. Emily Berndt<br> Earth Science Office<br> NASA Marshall Space Flight Center<br> Huntsville, Alabama, USA<br> emily.b.berndt@nasa.gov 3. Date of data collection: 2018-01-14 to 2020-06-09 4. Geographic location of data collection: Southwest United States<br> West longitude: 126.0 W<br> East longitude: 90.0 W<br> South latitude: 24.0 N<br> North latitude: 45.0 N 5. Funding source: <br> Data collection was supported by the NASA Short-term Prediction Research and Transition (SPoRT)<br> project at NASA Marshall Space Flight Center. DATA & FILE OVERVIEW 1. File List:<br> testing_dataset.txt<br> training_dataset.txt<br> validation_dataset.txt<br> Georeferenced polygon shapefiles, comprising .shp, .shx, .dbf, and .prj files with timestamp {YYYY}{MM}{DD}T{HH}{MM}{SS}. 2. Relationship between files:<br> This dataset contains:<br> 1) Georeferenced (WGS 1984) polygon shapefiles containing image classification for airborne dust.<br> 2) Text files listing the timestamp of shapefiles used in the training, testing, and validation datasets<br> used by the Berndt et al. (2021) random forest dust detection model. METHODOLOGICAL INFORMATION. 1. Description of methods for collection:<br> The dust labels were manually assigned by atmospheric scientists based largely on the GOES-16 ABI Dust RGB imagery but<br> supplemented by GOES-16 true color imagery, Area Forecast Discussions issued by NOAA National Weather Service<br> Weather Forecast Offices, and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO)<br> measurements. 2. Methods for processing the data:<br> GOES-16 ABI Dust RGB imagery was downloaded from Amazon Web Services and regridded to a 2-km rectangular grid. Feature labels were manually drawn on the imagery and classified by experts with the aid of a Python Graphical User Interface (GUI) based on the Tkinter python package. DATA-SPECIFIC INFORMATION FOR SHAPEFILES: 1. Shapefile coordinate system: WGS 1984 2. Number of Shapefiles: 83 3. Number of Polygons per Shapefile: Varies 4. Number of Attributes per polygon: 1 5. Attribute list:<br> A. Class: 0 --&gt; No Dust<br> 1 --&gt; Dust<br> 2 --&gt; Reserved for future use<br> 3 --&gt; No Data Value
DOI: 10.5281/zenodo.4641460
title: QCD Jet Samples with Particle Flow Constituents

description: <strong>Data Description</strong> QCD jet samples for anomalous jet tagging at the LHC. 705,643 events are stored with the fatjet selection criteria applied. Four vectors and the particle identification codes for jet constituents are recorded and a few high-level features are provided. <strong>Data Generation</strong> Jet samples in this dataset are generated in the QCD di-jet production process \(p p \to j j\) with MadGraph, Pythia8, and Delphes (no pile-up effects simulated). Particle flow objects are used to cluster jets. FastJet was used for jet clustering. Jets are clustered using the anti-kt algorithm with the cone size R=1.0. Leading jet: \(p_T&gt;450 \, \textrm{GeV}\); sub-leading jet: \(p_T&gt;200\, \textrm{GeV}\) <strong>Data Structure</strong> To get jets: f['objects/jets'] For jets, there are two datasets: ['constituents', 'obs']. (jets information is stored with the higher-pt jet first) `obs[:, n_j - 1]`: jet four vectors and n-subjettiness for the \(n_j\)-th jet (pt, eta, phi, m, tau1, tau2, tau3, tau4, tau5) pt-sorted (highest first) jet constituents information are stored in variable length arrays for the \(n_j\)-th jet `constituents[:, n_j - 1]`: \(\{ E_i, P_{xi}, P_{yi}, P_{zi}, \textrm{PID}_i\}\) (PID: PDG for tracks; [22] for photons; [0] for neutral hadrons) <strong>Extra Notes</strong> Quick access: get leading jet constituents with `f["objects/jets/constituents"][:, 0]`
description: {"references": ["Cheng et al. (2020). Variational Autoencoders for Anomalous Jet Tagging. arXiv: 2007.01850."]}
DOI: 10.5281/zenodo.4641459
title: QCD Jet Samples with Particle Flow Constituents

description: <strong>Data Description</strong> QCD jet samples for anomalous jet tagging at the LHC. 705,643 events are stored with the fatjet selection criteria applied. Four vectors and the particle identification codes for jet constituents are recorded and a few high-level features are provided. <strong>Data Generation</strong> Jet samples in this dataset are generated in the QCD di-jet production process \(p p \to j j\) with MadGraph, Pythia8, and Delphes (no pile-up effects simulated). Particle flow objects are used to cluster jets. FastJet was used for jet clustering. Jets are clustered using the anti-kt algorithm with the cone size R=1.0. Leading jet: \(p_T&gt;450 \, \textrm{GeV}\); sub-leading jet: \(p_T&gt;200\, \textrm{GeV}\) <strong>Data Structure</strong> To get jets: f['objects/jets'] For jets, there are two datasets: ['constituents', 'obs']. (jets information is stored with the higher-pt jet first) `obs[:, n_j - 1]`: jet four vectors and n-subjettiness for the \(n_j\)-th jet (pt, eta, phi, m, tau1, tau2, tau3, tau4, tau5) pt-sorted (highest first) jet constituents information are stored in variable length arrays for the \(n_j\)-th jet `constituents[:, n_j - 1]`: \(\{ E_i, P_{xi}, P_{yi}, P_{zi}, \textrm{PID}_i\}\) (PID: PDG for tracks; [22] for photons; [0] for neutral hadrons) <strong>Extra Notes</strong> Quick access: get leading jet constituents with `f["objects/jets/constituents"][:, 0]`
description: {"references": ["Cheng et al. (2020). Variational Autoencoders for Anomalous Jet Tagging. arXiv: 2007.01850."]}