cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.6253946.v1
title: Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine — Cumulative Mining Area (KML)

description: p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; line-height: 15.0px; font: 13.0px Arial; -webkit-text-stroke: #000000} p.p2 {margin: 0.0px 0.0px 0.0px 0.0px; line-height: 15.0px; font: 13.0px Arial; -webkit-text-stroke: #000000; min-height: 15.0px} span.s1 {font-kerning: none} These data accompany the 2018 manuscript published in <i>PLOS One</i> titled "Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine". In this manuscript, researchers used the Google Earth Engine platform and freely-accessible Landsat imagery to create a yearly dataset (1985 through 2015) of surface coal mining in the Appalachian region of the United States of America. <br> This specific dataset is a KML file of the cumulative mining area as determined by this study from the period 1985 through 2015. This dataset is the overall mining “footprint” in Appalachia, indicating any location where mining has occurred during this period. This dataset does not indicate, however, which year(s) mining was occurring in any given location.
DOI: 10.6084/m9.figshare.6253943
title: Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine — Cumulative Mining Area (GeoTIFF)

description: These data accompany the 2018 manuscript published in <i>PLOS One</i> titled "Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine". In this manuscript, researchers used the Google Earth Engine platform and freely-accessible Landsat imagery to create a yearly dataset (1985 through 2015) of surface coal mining in the Appalachian region of the United States of America. <br>This specific dataset is a GeoTIFF file of the cumulative mining area as determined by this study from the period 1985 through 2015. This dataset is the overall mining “footprint” in Appalachia, indicating any location where mining has occurred during this period. This dataset does not indicate, however, which year(s) mining was occurring in any given location. <br>
DOI: 10.6084/m9.figshare.10275935.v6
title: Carbon sequestration in artificial silicate soils facilitated by arbuscular mycorrhizal fungi and glomalin-related soil protein

description: Raw date and processing prodecures of them in the paper: Carbon sequestration in artificial silicate soils facilitated by arbuscular mycorrhizal fungi and glomalin-related soil protein.
DOI: 10.6084/m9.figshare.6253943.v1
title: Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine — Cumulative Mining Area (GeoTIFF)

description: These data accompany the 2018 manuscript published in <i>PLOS One</i> titled "Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine". In this manuscript, researchers used the Google Earth Engine platform and freely-accessible Landsat imagery to create a yearly dataset (1985 through 2015) of surface coal mining in the Appalachian region of the United States of America. <br>This specific dataset is a GeoTIFF file of the cumulative mining area as determined by this study from the period 1985 through 2015. This dataset is the overall mining “footprint” in Appalachia, indicating any location where mining has occurred during this period. This dataset does not indicate, however, which year(s) mining was occurring in any given location. <br>
DOI: 10.6084/m9.figshare.6253940
title: Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine — Cumulative Mining Area (shapefile)

description: These data accompany the 2018 manuscript published in <i>PLOS One</i> titled "Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine". In this manuscript, researchers used the Google Earth Engine platform and freely-accessible Landsat imagery to create a yearly dataset (1985 through 2015) of surface coal mining in the Appalachian region of the United States of America. <br> <br> This specific dataset is an Esri shapefile of the cumulative mining area as determined by this study from the period 1985 through 2015. This dataset is the overall mining “footprint” in Appalachia, indicating any location where mining has occurred during this period. This dataset does not indicate, however, which year(s) mining was occurring in any given location.
DOI: 10.6084/m9.figshare.6253940.v1
title: Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine — Cumulative Mining Area (shapefile)

description: These data accompany the 2018 manuscript published in <i>PLOS One</i> titled "Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine". In this manuscript, researchers used the Google Earth Engine platform and freely-accessible Landsat imagery to create a yearly dataset (1985 through 2015) of surface coal mining in the Appalachian region of the United States of America. <br> <br> This specific dataset is an Esri shapefile of the cumulative mining area as determined by this study from the period 1985 through 2015. This dataset is the overall mining “footprint” in Appalachia, indicating any location where mining has occurred during this period. This dataset does not indicate, however, which year(s) mining was occurring in any given location.
DOI: 10.6084/m9.figshare.6253937
title: Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine — Cumulative Mining Area (GeoJSON)

description: These data accompany the 2018 manuscript published in <i>PLOS One</i> titled "Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine". In this manuscript, researchers used the Google Earth Engine platform and freely-accessible Landsat imagery to create a yearly dataset (1985 through 2015) of surface coal mining in the Appalachian region of the United States of America. <br> This specific dataset is a GeoJSON file of the cumulative mining area as determined by this study from the period 1985 through 2015. This dataset is the overall mining “footprint” in Appalachia, indicating any location where mining has occurred during this period. This dataset does not indicate, however, which year(s) mining was occurring in any given location.
DOI: 10.6084/m9.figshare.6253937.v1
title: Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine — Cumulative Mining Area (GeoJSON)

description: These data accompany the 2018 manuscript published in <i>PLOS One</i> titled "Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine". In this manuscript, researchers used the Google Earth Engine platform and freely-accessible Landsat imagery to create a yearly dataset (1985 through 2015) of surface coal mining in the Appalachian region of the United States of America. <br> This specific dataset is a GeoJSON file of the cumulative mining area as determined by this study from the period 1985 through 2015. This dataset is the overall mining “footprint” in Appalachia, indicating any location where mining has occurred during this period. This dataset does not indicate, however, which year(s) mining was occurring in any given location.
DOI: 10.6084/m9.figshare.6253925
title: Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine — Processing Mask (GeoTIFF)

description: These data accompany the 2018 manuscript published in <i>PLOS One</i> titled "Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine". In this manuscript, researchers used the Google Earth Engine platform and freely-accessible Landsat imagery to create a yearly dataset (1985 through 2015) of surface coal mining in the Appalachian region of the United States of America. <br> This specific dataset is a GeoTIFF file of the processing mask. This mask omits land uses/covers including urban areas, roads, and rivers from the mine delineation analysis. The mask is based off various datasets from 2015.
DOI: 10.6084/m9.figshare.6253925.v1
title: Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine — Processing Mask (GeoTIFF)

description: These data accompany the 2018 manuscript published in <i>PLOS One</i> titled "Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine". In this manuscript, researchers used the Google Earth Engine platform and freely-accessible Landsat imagery to create a yearly dataset (1985 through 2015) of surface coal mining in the Appalachian region of the United States of America. <br> This specific dataset is a GeoTIFF file of the processing mask. This mask omits land uses/covers including urban areas, roads, and rivers from the mine delineation analysis. The mask is based off various datasets from 2015.
DOI: 10.6084/m9.figshare.6253922
title: Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine — Study Area (GeoJSON)

description: These data accompany the 2018 manuscript published in <i>PLOS One</i> titled "Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine". In this manuscript, researchers used the Google Earth Engine platform and freely-accessible Landsat imagery to create a yearly dataset (1985 through 2015) of surface coal mining in the Appalachian region of the United States of America. <br> This specific dataset is a GeoJSON file of the study area.
DOI: 10.6084/m9.figshare.6253919
title: Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine – Study Area (KML)

description: These data accompany the 2018 manuscript published in <i>PLOS One</i> titled "Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine". In this manuscript, researchers used the Google Earth Engine platform and freely-accessible Landsat imagery to create a yearly dataset (1985 through 2015) of surface coal mining in the Appalachian region of the United States of America. <br> <br> This specific dataset is a KML file of the study area.
DOI: 10.6084/m9.figshare.6253919.v2
title: Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine – Study Area (KML)

description: These data accompany the 2018 manuscript published in <i>PLOS One</i> titled "Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine". In this manuscript, researchers used the Google Earth Engine platform and freely-accessible Landsat imagery to create a yearly dataset (1985 through 2015) of surface coal mining in the Appalachian region of the United States of America. <br> <br> This specific dataset is a KML file of the study area.
DOI: 10.6084/m9.figshare.6253919.v1
title: Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine – Study Area (kml)

description: These data accompany the 2018 manuscript published in <i>PLOS One</i> titled "Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine". In this manuscript, researchers used the Google Earth Engine platform and freely-accessible Landsat imagery to create a yearly dataset (1985 through 2015) of surface coal mining in the Appalachian region of the United States of America. <br> <br> This specific dataset is a KML file of the study area.
DOI: 10.6084/m9.figshare.6253901
title: Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine — Study Area (shapefile)

description: These data accompany the 2018 manuscript published in <i>PLOS One</i> titled "Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine". In this manuscript, researchers used the Google Earth Engine platform and freely-accessible Landsat imagery to create a yearly dataset (1985 through 2015) of surface coal mining in the Appalachian region of the United States of America. <br>This specific dataset is an Esri shapefile of the study area.
DOI: 10.6084/m9.figshare.6253901.v1
title: Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine — Study Area (shapefile)

description: These data accompany the 2018 manuscript published in <i>PLOS One</i> titled "Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine". In this manuscript, researchers used the Google Earth Engine platform and freely-accessible Landsat imagery to create a yearly dataset (1985 through 2015) of surface coal mining in the Appalachian region of the United States of America. <br>This specific dataset is an Esri shapefile of the study area.
DOI: 10.6084/m9.figshare.6253868
title: Untitled ItemExercise and multiple sclerosis

description: Many of the symptoms associated with MS are reduced through physical exercise. Exercise is a great way for everyone to stay strong, control weight, improve fitness and ward off chronic disease.<br>What type of exercise is best / recommended for MS?<br>Overcoming Barriers<br>Using technology to your advantage<br><strong></strong><strong></strong>
DOI: 10.6084/m9.figshare.6253868.v1
title: Untitled Item

description: Many of the symptoms associated with MS are reduced through physical exercise. Exercise is a great way for everyone to stay strong, control weight, improve fitness and ward off chronic disease.<br>What type of exercise is best / recommended for MS?<br>Overcoming Barriers<br>Using technology to your advantage<br><strong></strong><strong></strong>
DOI: 10.6084/m9.figshare.6253595
title: A return to the genetic heritage of durum wheat to cope with drought heightened by climate change

description: These data include the pedigree of the cultivars studied in this work and the physicochemical characteristics of the soil. These data also include the Analysis of variance procedure of some parameters.
DOI: 10.6084/m9.figshare.6253559
title: Phylogenetic Trees

description: Ultrametric Trees obtained with BEAST used for species delineation analyses with GMYC model.<br>Maximum Likelihood Trees obtained with metabarcoding (eOTU, SV) analyeses.
DOI: 10.6084/m9.figshare.6253595.v1
title: A return to the genetic heritage of durum wheat to cope with drought heightened by climate change

description: These data include the pedigree of the cultivars studied in this work and the physicochemical characteristics of the soil. These data also include the Analysis of variance procedure of some parameters.
DOI: 10.6084/m9.figshare.6253559.v2
title: Phylogenetic Trees

description: Ultrametric Trees obtained with BEAST used for species delineation analyses with GMYC model.<br>Maximum Likelihood Trees obtained with metabarcoding (eOTU, SV) analyeses.
DOI: 10.6084/m9.figshare.6253559.v1
title: Phylogenetic_Trees.zip

description: Ultrametric Trees obtained with BEAST used for species delineation analyses with GMYC model.<br>Maximum Likelihood Trees obtained with metabarcoding (eOTU, SV) analyeses.
DOI: 10.6084/m9.figshare.6252467
title: Data-look-ahead_2.xlsx

description: Detailed data for modified IEEE 39-node power system and Belgian 20-node gas system for stochastic look-ahead dispatch has been included. Please refer to my paper "Stochastic Look-Ahead Dispatch for Coupled Electricity and Natural-gas Networks" if published online.
DOI: 10.6084/m9.figshare.6252467.v1
title: Data-look-ahead_2.xlsx

description: Detailed data for modified IEEE 39-node power system and Belgian 20-node gas system for stochastic look-ahead dispatch has been included. Please refer to my paper "Stochastic Look-Ahead Dispatch for Coupled Electricity and Natural-gas Networks" if published online.
DOI: 10.6084/m9.figshare.6251642
title: Multi-region hierarchical distance sampling - Dataset

description: Distance sampling data for three arboreal primate species, collected along line transects in five forests in the Udzungwa Mountains of Tanzania. Standardized human disturbance and habitat parameters, collected within plots along the same line transects, are available too. Data are formatted in three-dimensional matrices to be used in a multi-region, hierarchical distance sampling analysis.
DOI: 10.6084/m9.figshare.6251642.v1
title: Multi-region hierarchical distance sampling - Dataset

description: Distance sampling data for three arboreal primate species, collected along line transects in five forests in the Udzungwa Mountains of Tanzania. Standardized human disturbance and habitat parameters, collected within plots along the same line transects, are available too. Data are formatted in three-dimensional matrices to be used in a multi-region, hierarchical distance sampling analysis.
DOI: 10.6084/m9.figshare.6249962
title: SDFParser example Cypher outputs

description: SDF parser SDF to Cypher outputs<br><br><b>Used databases</b>* ChEBI complete 3star (ftp://ftp.ebi.ac.uk/pub/databases/chebi/SDF/ChEBI_complete_3star.sdf.gz; downloaded: 2018-05-04; first 4000 compounds)* DrugBank Open Structures (https://www.drugbank.ca/releases/5-1-0/downloads/all-open-structures; version: 5.1.0, released: 2018-04-02; first 7000 compounds)<br><b>Output variants</b><br>| Output variant | Parser additional options || :------------: | :-----------------------: || 1. | - || 2. | `-p` || 3. | `-u` || 4. | `-up` |<br>1. `ChEBI_complete_3star_4000.cypher` and `drugbank_open_structures_7000.cypher` - standard SDF to Cypher,2. `ChEBI_complete_3star_4000-p.cypher` and `drugbank_open_structures_7000-p.cypher` - standard SDF with added additional periodic table data to atoms,3. `ChEBI_complete_3star_4000-u.cypher` and `drugbank_open_structures_7000-u.cypher` - standard SDF with `-u` parser option enabled (try to generate full database URLs instead of IDs),4. `ChEBI_complete_3star_4000-up.cypher` and `drugbank_open_structures_7000-up.cypher` - standard SDF with added additional periodic table data to atoms and `-u` parser option enabled (try to generate full database URLs instead of IDs).
DOI: 10.6084/m9.figshare.6249962.v1
title: SDFParser example Cypher outputs

description: SDF parser SDF to Cypher outputs<br><br><b>Used databases</b>* ChEBI complete 3star (ftp://ftp.ebi.ac.uk/pub/databases/chebi/SDF/ChEBI_complete_3star.sdf.gz; downloaded: 2018-05-04; first 4000 compounds)* DrugBank Open Structures (https://www.drugbank.ca/releases/5-1-0/downloads/all-open-structures; version: 5.1.0, released: 2018-04-02; first 7000 compounds)<br><b>Output variants</b><br>| Output variant | Parser additional options || :------------: | :-----------------------: || 1. | - || 2. | `-p` || 3. | `-u` || 4. | `-up` |<br>1. `ChEBI_complete_3star_4000.cypher` and `drugbank_open_structures_7000.cypher` - standard SDF to Cypher,2. `ChEBI_complete_3star_4000-p.cypher` and `drugbank_open_structures_7000-p.cypher` - standard SDF with added additional periodic table data to atoms,3. `ChEBI_complete_3star_4000-u.cypher` and `drugbank_open_structures_7000-u.cypher` - standard SDF with `-u` parser option enabled (try to generate full database URLs instead of IDs),4. `ChEBI_complete_3star_4000-up.cypher` and `drugbank_open_structures_7000-up.cypher` - standard SDF with added additional periodic table data to atoms and `-u` parser option enabled (try to generate full database URLs instead of IDs).
DOI: 10.6084/m9.figshare.6249884
title: RAAAP Professional Qualifications Other Details

description: The RAAAP project surveyed Research Managers and Administrators from across the world, asking questions about why people became RMAs, why they stayed as RMAs, what skills they need for their jobs (soft and hard), what level of seniority they are, demographic information, and so on - overall up to 222 data points were collected from each respondent. This subsidiary dataset contains (anonymised) free text responses about the professional qualifications that respondents has undertaken. Note that to promote anonymity responses are only linked to the main dataset by AnalysisRegionOfEmployment
DOI: 10.6084/m9.figshare.6249884.v1
title: RAAAP Professional Qualifications Other Details

description: The RAAAP project surveyed Research Managers and Administrators from across the world, asking questions about why people became RMAs, why they stayed as RMAs, what skills they need for their jobs (soft and hard), what level of seniority they are, demographic information, and so on - overall up to 222 data points were collected from each respondent. This subsidiary dataset contains (anonymised) free text responses about the professional qualifications that respondents has undertaken. Note that to promote anonymity responses are only linked to the main dataset by AnalysisRegionOfEmployment
DOI: 10.6084/m9.figshare.6249875
title: RAAAP Professional Development Other Details

description: The RAAAP project surveyed Research Managers and Administrators from across the world, asking questions about why people became RMAs, why they stayed as RMAs, what skills they need for their jobs (soft and hard), what level of seniority they are, demographic information, and so on - overall up to 222 data points were collected from each respondent. This subsidiary dataset contains (anonymised) free text responses about the professional development that respondents has undertaken. Note that to promote anonymity responses are only linked to the main dataset by AnalysisRegionOfEmployment.
DOI: 10.6084/m9.figshare.6249875.v1
title: RAAAP Professional Development Other Details

description: The RAAAP project surveyed Research Managers and Administrators from across the world, asking questions about why people became RMAs, why they stayed as RMAs, what skills they need for their jobs (soft and hard), what level of seniority they are, demographic information, and so on - overall up to 222 data points were collected from each respondent. This subsidiary dataset contains (anonymised) free text responses about the professional development that respondents has undertaken. Note that to promote anonymity responses are only linked to the main dataset by AnalysisRegionOfEmployment.
DOI: 10.6084/m9.figshare.6249815
title: RAAAP OrgDepartmentDetails

description: The RAAAP project surveyed Research Managers and Administrators from across the world, asking questions about why people became RMAs, why they stayed as RMAs, what skills they need for their jobs (soft and hard), what level of seniority they are, demographic information, and so on - overall up to 222 data points were collected from each respondent. This subsidiary dataset contains (anonymised) free text responses about the part of the organisation that respondents work in (eg central, departmental). Note that to promote anonymity responses are only linked to the main dataset by AnalysisRegionOfEmployment
DOI: 10.6084/m9.figshare.6249815.v1
title: RAAAP OrgDepartmentDetails

description: The RAAAP project surveyed Research Managers and Administrators from across the world, asking questions about why people became RMAs, why they stayed as RMAs, what skills they need for their jobs (soft and hard), what level of seniority they are, demographic information, and so on - overall up to 222 data points were collected from each respondent. This subsidiary dataset contains (anonymised) free text responses about the part of the organisation that respondents work in (eg central, departmental). Note that to promote anonymity responses are only linked to the main dataset by AnalysisRegionOfEmployment