cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25384/sage.6159518.v1
title: Data analyses EUP-17-1385 III -Supplemental material for Deciding about the unknown: The effect of party and ideological cues on forming opinions about the European Union

description: Supplemental material, Data analyses EUP-17-1385 III for Deciding about the unknown: The effect of party and ideological cues on forming opinions about the European Union by Mariano Torcal, Sergio Martini and Lluis Orriols in European Union Politics
DOI: 10.25384/sage.6159641.v1
title: Supplementary Figures 3 and 4 -Supplemental material for A phase 1 study to assess the pharmacokinetics, safety, and tolerability of fremanezumab doses (225 mg, 675 mg and 900 mg) in Japanese and Caucasian healthy subjects

description: Supplemental material, Supplementary Figures 3 and 4 for A phase 1 study to assess the pharmacokinetics, safety, and tolerability of fremanezumab doses (225 mg, 675 mg and 900 mg) in Japanese and Caucasian healthy subjects by Orit Cohen-Barak, Sivan Weiss, Michele Rasamoelisolo, Nicola Faulhaber, Paul P Yeung, Pippa S Loupe, Esther Yoon, Mohit D Gandhi, Ofer Spiegelstein and Ernesto Aycardi in Cephalalgia
DOI: 10.6084/m9.figshare.26860624
title: Additional file 1 of Analysis of D-dimer levels for the detection of deep venous thrombosis for patients with spinal metastasis undergoing decompression with fixation

description: Additional file 1. Parameters data of all the patients
DOI: 10.6084/m9.figshare.26860624.v1
title: Additional file 1 of Analysis of D-dimer levels for the detection of deep venous thrombosis for patients with spinal metastasis undergoing decompression with fixation

description: Additional file 1. Parameters data of all the patients
DOI: 10.25384/sage.6174113.v1
title: FinalCMPSData – Supplemental material for Terrain ruggedness and land cover: Improved data for most research designs

description: Supplemental material, FinalCMPSData for Terrain ruggedness and land cover: Improved data for most research designs by Andrew Shaver, David B. Carter and TseringWangyal Shawa in Conflict Management and Peace Science
DOI: 10.25384/sage.6097592
title: CarterMcCullough_Functions – Supplemental material for A Simple, Principled Approach to Combining Evidence From Meta-Analysis and High-Quality Replications

description: Supplemental material, CarterMcCullough_Functions for A Simple, Principled Approach to Combining Evidence From Meta-Analysis and High-Quality Replications by Evan C. Carter and Michael E. McCullough in Advances in Methods and Practices in Psychological Science
DOI: 10.25384/sage.6176648.v1
title: Supplemental Material, JCR-15-0379.R1 - Self-censorship of Conflict-related Information in the Context of Intractable Conflict

description: Supplemental Material, JCR-15-0379.R1 for Self-censorship of Conflict-related Information in the Context of Intractable Conflict by Eldad Shahar, Boaz Hameiri, Daniel Bar-Tal, and Amiram Raviv in Journal of Conflict Resolution
DOI: 10.25384/sage.6097595.v1
title: CarterMcCullough_PowerFigures – Supplemental material for A Simple, Principled Approach to Combining Evidence From Meta-Analysis and High-Quality Replications

description: Supplemental material, CarterMcCullough_PowerFigures for A Simple, Principled Approach to Combining Evidence From Meta-Analysis and High-Quality Replications by Evan C. Carter and Michael E. McCullough in Advances in Methods and Practices in Psychological Science
DOI: 10.25384/sage.6097598
title: CarterMcCullough_PracticalSignificance – Supplemental material for A Simple, Principled Approach to Combining Evidence From Meta-Analysis and High-Quality Replications

description: Supplemental material, CarterMcCullough_PracticalSignificance for A Simple, Principled Approach to Combining Evidence From Meta-Analysis and High-Quality Replications by Evan C. Carter and Michael E. McCullough in Advances in Methods and Practices in Psychological Science
DOI: 10.25384/sage.6098360
title: Supplemental Material, SPPS766409_suppl_mat - Deeply Confusing: Conflating Difficulty With Deep Revelation on Personality Assessment

description: Supplemental Material, SPPS766409_suppl_mat for Deeply Confusing: Conflating Difficulty With Deep Revelation on Personality Assessment by Randy Stein and Alexander B. Swan in Social Psychological and Personality Science
DOI: 10.25384/sage.6176633
title: Supplemental Material, ReplicationMaterials - Managing Resource-related Conflict: A Framework of Lootable Resource Management and Postconflict Stabilization

description: Supplemental Material, ReplicationMaterials for Managing Resource-related Conflict: A Framework of Lootable Resource Management and Postconflict Stabilization by Vita Roy in Journal of Conflict Resolution
DOI: 10.25384/sage.6176642.v1
title: Supplemental Material, ReplicationMaterials_KelmendiRadin - UNsatisfied? Public Support for Postconflict International Missions

description: Supplemental Material, ReplicationMaterials_KelmendiRadin for UNsatisfied? Public Support for Postconflict International Missions by Pellumb Kelmendi, and Andrew Radin in Journal of Conflict Resolution
DOI: 10.25384/sage.6176645.v1
title: Supplemental Material, download - Covert Operations, Wars, Detainee Destinations, and the Psychology of Democratic Peace

description: Supplemental Material, download for Covert Operations, Wars, Detainee Destinations, and the Psychology of Democratic Peace by Christian Crandall, Owen Cox, Ryan Beasley, and Mariya Omelicheva in Journal of Conflict Resolution
DOI: 10.25384/sage.6120758
title: Supplementary_Material – Supplemental material for CD133 Promotes Adhesion to the Ovarian Cancer Metastatic Niche

description: Supplemental material, Supplementary_Material for CD133 Promotes Adhesion to the Ovarian Cancer Metastatic Niche by Lynn Roy, Alexander Bobbs, Rachel Sattler, Jeffrey L Kurkewich, Paige B Dausinas, Prakash Nallathamby and Karen D Cowden Dahl in Cancer Growth and Metastasis
DOI: 10.25384/sage.6127190
title: Supplemental Material, Do_file_for_replication - Killing for God? Factional Violence on the Transnational Stage

description: Supplemental Material, Do_file_for_replication for Killing for God? Factional Violence on the Transnational Stage by Jori Breslawski, Brandon Ives in Journal of Conflict Resolution
DOI: 10.25384/sage.6127196.v1
title: Supplemental Material, killingforgodfinaldataset_FINAL - Killing for God? Factional Violence on the Transnational Stage

description: Supplemental Material, killingforgodfinaldataset_FINAL for Killing for God? Factional Violence on the Transnational Stage by Jori Breslawski, Brandon Ives in Journal of Conflict Resolution
DOI: 10.6084/m9.figshare.26860642
title: Additional file 1 of Non-invasive versus arterial pressure monitoring in the pre-hospital critical care environment: a paired comparison of concurrently recorded measurements

description: Supplementary Material 1
DOI: 10.6084/m9.figshare.26860642.v1
title: Additional file 1 of Non-invasive versus arterial pressure monitoring in the pre-hospital critical care environment: a paired comparison of concurrently recorded measurements

description: Supplementary Material 1
DOI: 10.15468/dl.wr69xq
title: Occurrence Download

description: A dataset containing 59771 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Mustela erminea Linnaeus, 1758",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 59771 records from 478 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0105736-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5439/1237276
title: aafgust200.a1

description: AAF gust probe data at 200 Hz sample rate
DOI: 10.5281/zenodo.6596565
title: Questionnaire used to evaluate the usability of two declarative computer languages to model geometric events in space. Responses from usability questions.

description: This dataset contains results for questions 4-14 described in referenced document (10.5281/zenodo.6569980). Results are from two different samples.
DOI: 10.5281/zenodo.6596564
title: Questionnaire used to evaluate the usability of two declarative computer languages to model geometric events in space. Responses from usability questions.

description: This dataset contains results for questions 4-14 described in referenced document (10.5281/zenodo.6569980). Results are from two different samples.
DOI: 10.6084/m9.figshare.19930178
title: TF-C Pretrain SleepEEG

description: - Paper: Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency - Paper link: - Github repo: https://github.com/mims-harvard/TFC-pretraining - Project website: <br> <strong>SleepEEG </strong>contains 153 whole-night sleeping Electroencephalography (EEG) recordings that monitored by sleep cassette. The data is collected from 82 healthy subjects. The 1-lead EEG signal is sampled at 100 Hz. We segment the EEG signals into segments (window size is 200) without overlapping and each segment forms a sample. Every sample is associated with one of the five sleeping patterns/stages: Wake (W), Non-rapid eye movement (N1, N2, N3) and Rapid Eye Movement (REM). <br> <br>
DOI: 10.5281/zenodo.6908356
title: AgsSAT Multiannual (2017-2021) Sentinel-2 Geomedian Composites

description: AgsSAT Multiannual (2017-2021) Sentinel-2 Geomedian Composites The 2,815 images available for the state of Aguascalientes, Mexico for the years 2017 to 2021 were processed using the Open Data Cube (ODC) platform [Lewis et al. (2017), Gavin et al. (2018), https://www.opendatacube.org/]. These images correspond to multiple coverages of the region of interest. The images were then used to generate cloud-free annual composites by applying geometric median (geomedian) algorithm, as defined in [Roberts et al. (2017)]. Geomedian algorithm produces a pixel-level summary for every pixel, in this case this means that each summary corresponds to a 10m x 10m region in the territory and its observations throughout a calendar year. All these summary pixels form a 12-band (coastal aerosol, blue, green, red, vegetation red edge 5, vegetation red edge 6, vegetation red edge 7, near-infrared, narrow nir, water vapor, swir1 and swir2) composite of the state of Aguascalientes. Another product called GeoMad was generated, which calculates the robust dispersion statistic called MAD, as defined in [Roberts, D., Dunn, B., & Mueller, N. (2018)]. In the resulting image composite, each of the three-pixel bands represents the variation over three distances: Spectral Distance (smad), Euclidean Distance (emad) and the Bray-Curtis Distance (bcmad). More bands were generated to represent different environmental conditions during the study years (2017-2021), these conditions can be captured by analyzing various combinations of bands, these combinations are also called spectral indices, which allow detecting vegetation, presence of water, urbanization, etc., Finally, 28 indices divided into 4 categories were calculated: Vegetation Indices (Atmospherically Resistant Vegetation Index, Kaufman 1972) (Enhanced Vegetation Index, Huete 2002): (Modified Soil Adjusted Vegetation Index, Qi Et Al. 1994) (Normalized Difference Chlorophyll Index, Mishra & Mishra, 2012) (Normalised Difference Moisture Index, Gao 1996) (Normalized Difference Vegetation Index, Rouse 1973) (Optimized Soil Adjusted Vegetation Index, Rondeaux. 1996) (Simple Ratio Vegetation Index Jordan, C.F.1 969) (Soil Adjusted Vegetation Index, Huete 1988) (Visible Atmospherically Resistant Index, Gittleson 2002) Built-up Indexes (Band Ration For Built-up Area, Waqar 2012) (Built-up Area Extraction Index, Bouzekri 2015) (Built-up Index, He Et Al. 2010) (Index-based Built-up Index, Xu 2008) (New Built-up Index, Jieli Et Al. 2010) (Normalized Difference Built-up Index, Zha 2003) (Normalized Built-up Area Index, Waqar 2012) (Urban Index, Kawamura 1996) Water Indices (Modified Normalized Difference Water Index, Xu 1996) (Normalized Difference Water Index, Mcfeeters 1996) (Water Index, Fisher 2016) Other Indices (Bare Soil Index, Rikimaru Et Al. 2002) (Bare Soil Index, Wanhui 2004) (Burn Area Index, Martin 1998) (Clay Minerals Ratio, Drury 1987) (Ferrous Minerals Ratio, Segal 1982) (Iron Oxide Ratio, Segal 1982) (Normalized Burn Ratio, Lopez Garcia 1991) (Normalised Difference Snow Index, Hall 1995).
DOI: 10.5281/zenodo.6909613
title: AgsSAT Multiannual (2017-2021) Sentinel-2 Vegetation Indices Composites

description: AgsSAT Multiannual (2017-2021) Sentinel-2 Vegetation Indices Composites The 2,815 images available for the state of Aguascalientes, Mexico for the years 2017 to 2021 were processed using the Open Data Cube (ODC) platform [Lewis et al. (2017), Gavin et al. (2018), https://www.opendatacube.org/]. These images correspond to multiple coverages of the region of interest. The images were then used to generate cloud-free annual composites by applying geometric median (geomedian) algorithm, as defined in [Roberts et al. (2017)]. Geomedian algorithm produces a pixel-level summary for every pixel, in this case this means that each summary corresponds to a 10m x 10m region in the territory and its observations throughout a calendar year. All these summary pixels form a 12-band (coastal aerosol, blue, green, red, vegetation red edge 5, vegetation red edge 6, vegetation red edge 7, near-infrared, narrow nir, water vapor, swir1 and swir2) composite of the state of Aguascalientes. Another product called GeoMad was generated, which calculates the robust dispersion statistic called MAD, as defined in [Roberts, D., Dunn, B., & Mueller, N. (2018)]. In the resulting image composite, each of the three-pixel bands represents the variation over three distances: Spectral Distance (smad), Euclidean Distance (emad) and the Bray-Curtis Distance (bcmad). More bands were generated to represent different environmental conditions during the study years (2017-2021), these conditions can be captured by analyzing various combinations of bands, these combinations are also called spectral indices, which allow detecting vegetation, presence of water, urbanization, etc., Finally, 28 indices divided into 4 categories were calculated: Vegetation Indices (Atmospherically Resistant Vegetation Index, Kaufman 1972) (Enhanced Vegetation Index, Huete 2002): (Modified Soil Adjusted Vegetation Index, Qi Et Al. 1994) (Normalized Difference Chlorophyll Index, Mishra & Mishra, 2012) (Normalised Difference Moisture Index, Gao 1996) (Normalized Difference Vegetation Index, Rouse 1973) (Optimized Soil Adjusted Vegetation Index, Rondeaux. 1996) (Simple Ratio Vegetation Index Jordan, C.F.1 969) (Soil Adjusted Vegetation Index, Huete 1988) (Visible Atmospherically Resistant Index, Gittleson 2002) Built-up Indexes (Band Ration For Built-up Area, Waqar 2012) (Built-up Area Extraction Index, Bouzekri 2015) (Built-up Index, He Et Al. 2010) (Index-based Built-up Index, Xu 2008) (New Built-up Index, Jieli Et Al. 2010) (Normalized Difference Built-up Index, Zha 2003) (Normalized Built-up Area Index, Waqar 2012) (Urban Index, Kawamura 1996) Water Indices (Modified Normalized Difference Water Index, Xu 1996) (Normalized Difference Water Index, Mcfeeters 1996) (Water Index, Fisher 2016) Other Indices (Bare Soil Index, Rikimaru Et Al. 2002) (Bare Soil Index, Wanhui 2004) (Burn Area Index, Martin 1998) (Clay Minerals Ratio, Drury 1987) (Ferrous Minerals Ratio, Segal 1982) (Iron Oxide Ratio, Segal 1982) (Normalized Burn Ratio, Lopez Garcia 1991) (Normalised Difference Snow Index, Hall 1995).
DOI: 10.5281/zenodo.6909612
title: AgsSAT Multiannual (2017-2021) Sentinel-2 Vegetation Indices Composites

description: AgsSAT Multiannual (2017-2021) Sentinel-2 Vegetation Indices Composites The 2,815 images available for the state of Aguascalientes, Mexico for the years 2017 to 2021 were processed using the Open Data Cube (ODC) platform [Lewis et al. (2017), Gavin et al. (2018), https://www.opendatacube.org/]. These images correspond to multiple coverages of the region of interest. The images were then used to generate cloud-free annual composites by applying geometric median (geomedian) algorithm, as defined in [Roberts et al. (2017)]. Geomedian algorithm produces a pixel-level summary for every pixel, in this case this means that each summary corresponds to a 10m x 10m region in the territory and its observations throughout a calendar year. All these summary pixels form a 12-band (coastal aerosol, blue, green, red, vegetation red edge 5, vegetation red edge 6, vegetation red edge 7, near-infrared, narrow nir, water vapor, swir1 and swir2) composite of the state of Aguascalientes. Another product called GeoMad was generated, which calculates the robust dispersion statistic called MAD, as defined in [Roberts, D., Dunn, B., & Mueller, N. (2018)]. In the resulting image composite, each of the three-pixel bands represents the variation over three distances: Spectral Distance (smad), Euclidean Distance (emad) and the Bray-Curtis Distance (bcmad). More bands were generated to represent different environmental conditions during the study years (2017-2021), these conditions can be captured by analyzing various combinations of bands, these combinations are also called spectral indices, which allow detecting vegetation, presence of water, urbanization, etc., Finally, 28 indices divided into 4 categories were calculated: Vegetation Indices (Atmospherically Resistant Vegetation Index, Kaufman 1972) (Enhanced Vegetation Index, Huete 2002): (Modified Soil Adjusted Vegetation Index, Qi Et Al. 1994) (Normalized Difference Chlorophyll Index, Mishra & Mishra, 2012) (Normalised Difference Moisture Index, Gao 1996) (Normalized Difference Vegetation Index, Rouse 1973) (Optimized Soil Adjusted Vegetation Index, Rondeaux. 1996) (Simple Ratio Vegetation Index Jordan, C.F.1 969) (Soil Adjusted Vegetation Index, Huete 1988) (Visible Atmospherically Resistant Index, Gittleson 2002) Built-up Indexes (Band Ration For Built-up Area, Waqar 2012) (Built-up Area Extraction Index, Bouzekri 2015) (Built-up Index, He Et Al. 2010) (Index-based Built-up Index, Xu 2008) (New Built-up Index, Jieli Et Al. 2010) (Normalized Difference Built-up Index, Zha 2003) (Normalized Built-up Area Index, Waqar 2012) (Urban Index, Kawamura 1996) Water Indices (Modified Normalized Difference Water Index, Xu 1996) (Normalized Difference Water Index, Mcfeeters 1996) (Water Index, Fisher 2016) Other Indices (Bare Soil Index, Rikimaru Et Al. 2002) (Bare Soil Index, Wanhui 2004) (Burn Area Index, Martin 1998) (Clay Minerals Ratio, Drury 1987) (Ferrous Minerals Ratio, Segal 1982) (Iron Oxide Ratio, Segal 1982) (Normalized Burn Ratio, Lopez Garcia 1991) (Normalised Difference Snow Index, Hall 1995).
DOI: 10.5281/zenodo.6908502
title: AgsSAT Multiannual (2017-2021) Sentinel-2 GeoMAD Composites

description: AgsSAT Multiannual (2017-2021) Sentinel-2 GeoMAD Composites The 2,815 images available for the state of Aguascalientes, Mexico for the years 2017 to 2021 were processed using the Open Data Cube (ODC) platform [Lewis et al. (2017), Gavin et al. (2018), https://www.opendatacube.org/]. These images correspond to multiple coverages of the region of interest. The images were then used to generate cloud-free annual composites by applying geometric median (geomedian) algorithm, as defined in [Roberts et al. (2017)]. Geomedian algorithm produces a pixel-level summary for every pixel, in this case this means that each summary corresponds to a 10m x 10m region in the territory and its observations throughout a calendar year. All these summary pixels form a 12-band (coastal aerosol, blue, green, red, vegetation red edge 5, vegetation red edge 6, vegetation red edge 7, near-infrared, narrow nir, water vapor, swir1 and swir2) composite of the state of Aguascalientes. Another product called GeoMad was generated, which calculates the robust dispersion statistic called MAD, as defined in [Roberts, D., Dunn, B., & Mueller, N. (2018)]. In the resulting image composite, each of the three-pixel bands represents the variation over three distances: Spectral Distance (smad), Euclidean Distance (emad) and the Bray-Curtis Distance (bcmad). More bands were generated to represent different environmental conditions during the study years (2017-2021), these conditions can be captured by analyzing various combinations of bands, these combinations are also called spectral indices, which allow detecting vegetation, presence of water, urbanization, etc., Finally, 28 indices divided into 4 categories were calculated: Vegetation Indices (Atmospherically Resistant Vegetation Index, Kaufman 1972) (Enhanced Vegetation Index, Huete 2002): (Modified Soil Adjusted Vegetation Index, Qi Et Al. 1994) (Normalized Difference Chlorophyll Index, Mishra & Mishra, 2012) (Normalised Difference Moisture Index, Gao 1996) (Normalized Difference Vegetation Index, Rouse 1973) (Optimized Soil Adjusted Vegetation Index, Rondeaux. 1996) (Simple Ratio Vegetation Index Jordan, C.F.1 969) (Soil Adjusted Vegetation Index, Huete 1988) (Visible Atmospherically Resistant Index, Gittleson 2002) Built-up Indexes (Band Ration For Built-up Area, Waqar 2012) (Built-up Area Extraction Index, Bouzekri 2015) (Built-up Index, He Et Al. 2010) (Index-based Built-up Index, Xu 2008) (New Built-up Index, Jieli Et Al. 2010) (Normalized Difference Built-up Index, Zha 2003) (Normalized Built-up Area Index, Waqar 2012) (Urban Index, Kawamura 1996) Water Indices (Modified Normalized Difference Water Index, Xu 1996) (Normalized Difference Water Index, Mcfeeters 1996) (Water Index, Fisher 2016) Other Indices (Bare Soil Index, Rikimaru Et Al. 2002) (Bare Soil Index, Wanhui 2004) (Burn Area Index, Martin 1998) (Clay Minerals Ratio, Drury 1987) (Ferrous Minerals Ratio, Segal 1982) (Iron Oxide Ratio, Segal 1982) (Normalized Burn Ratio, Lopez Garcia 1991) (Normalised Difference Snow Index, Hall 1995).