cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3459830
title: Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) at 250 m monthly for period 2014-2019 based on COPERNICUS land products

description: Long-term monthly Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) median value at 250 m based on the time-series of COPERNICUS FAPAR. Derived using the data.table package and quantile function in R. Processing steps are available <strong>here</strong>. Antartica is not included. To access and visualize maps use: OpenLandMap.org If you discover a bug, artifact or inconsistency in the LandGIS maps, or if you have a question please use some of the following channels: Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/issues General questions and comments: https://disqus.com/home/forums/landgis/ All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: veg = theme: vegetation, fapar = Fraction of Absorbed Photosynthetically Active Radiation, proba.v.oct = determination method: PROBA-V products, month October, d = median value, 250m = spatial resolution / block support: 250 m, s0..0cm = vertical reference: land surface, 2014..2019 = time reference: from 2014 to 2019, v1.0 = version number: 1.0,
description: {"references": ["S\u00e1nchez, J., Camacho, F., Lacaze, R., & Smets, B. (2015). Early validation of PROBA-V GEOV1 LAI, FAPAR and FCOVER products for the continuity of the Copernicus Global Land Service. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(7), 93."]}
DOI: 10.5281/zenodo.1450336
title: Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) at 250 m monthly for period 2014-2019 based on COPERNICUS land products

description: Long-term monthly Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) median value at 250 m based on the time-series of COPERNICUS FAPAR. Derived using the data.table package and quantile function in R. Processing steps are available <strong>here</strong>. Antartica is not included. To access and visualize maps use: OpenLandMap.org If you discover a bug, artifact or inconsistency in the LandGIS maps, or if you have a question please use some of the following channels: Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/issues General questions and comments: https://disqus.com/home/forums/landgis/ All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: veg = theme: vegetation, fapar = Fraction of Absorbed Photosynthetically Active Radiation, proba.v.oct = determination method: PROBA-V products, month October, d = median value, 250m = spatial resolution / block support: 250 m, s0..0cm = vertical reference: land surface, 2014..2019 = time reference: from 2014 to 2019, v1.0 = version number: 1.0,
description: {"references": ["S\u00e1nchez, J., Camacho, F., Lacaze, R., & Smets, B. (2015). Early validation of PROBA-V GEOV1 LAI, FAPAR and FCOVER products for the continuity of the Copernicus Global Land Service. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(7), 93."]}
DOI: 10.5281/zenodo.1477073
title: Tree-covered and intact forest landscapes BC1000, 1995, 2000, 2005, 2010, 2013, 2016 at 250 m

description: Based on the UNEP historic forest cover map, ESA land cover time series and intact forest landscape (IFL 2000, 2013 and 2016) data. Processing steps are described in detail <strong>here</strong>. Antartica is not included. To access and visualize maps use: OpenLandMap.org If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels: Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/issues General questions and comments: https://disqus.com/home/forums/landgis/ All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: ldg = theme: land degradation, forest.cover = variable: forest / tree cover, esacci.ifl = determination method: combination of ESA land cover and IFL maps, c = factor, 250m = spatial resolution / block support: 250 m, s0..0cm = vertical reference: land surface, 1995 = time reference: year 1995, v0.1 = version number: 0.1,
description: {"references": ["Potapov, P., Hansen, M.C., Laestadius, L., Turubanova, S., Yaroshenko, A., Thies, C., Smith, W., Zhuravleva, I., Komarova, A., Minnemeyer, S. and Esipova, E., 2017. The last frontiers of wilderness: Tracking loss of intact forest landscapes from 2000 to 2013. Science Advances, 3(1), p.e1600821."]}
DOI: 10.5281/zenodo.1476464
title: Tree-covered and intact forest landscapes BC1000, 1995, 2000, 2005, 2010, 2013, 2016 at 250 m

description: Based on the UNEP historic forest cover map, ESA land cover time series and intact forest landscape (IFL 2000, 2013 and 2016) data. Processing steps are described in detail <strong>here</strong>. Antartica is not included. To access and visualize maps use: OpenLandMap.org If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels: Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/issues General questions and comments: https://disqus.com/home/forums/landgis/ All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: ldg = theme: land degradation, forest.cover = variable: forest / tree cover, esacci.ifl = determination method: combination of ESA land cover and IFL maps, c = factor, 250m = spatial resolution / block support: 250 m, s0..0cm = vertical reference: land surface, 1995 = time reference: year 1995, v0.1 = version number: 0.1,
description: {"references": ["Potapov, P., Hansen, M.C., Laestadius, L., Turubanova, S., Yaroshenko, A., Thies, C., Smith, W., Zhuravleva, I., Komarova, A., Minnemeyer, S. and Esipova, E., 2017. The last frontiers of wilderness: Tracking loss of intact forest landscapes from 2000 to 2013. Science Advances, 3(1), p.e1600821."]}
DOI: 10.5281/zenodo.2525675
title: Silt content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution

description: Silt content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. Based on machine learning predictions from global compilation of soil profiles and samples. Processing steps are described in detail <strong>here</strong>. Antarctica is not included. To access and visualize maps use: OpenLandMap.org If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels: Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/issues General questions and comments: https://disqus.com/home/forums/landgis/ All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: sol = theme: soil, silt.wfraction = variable: silt weight fraction, usda.3a1a1a = determination method: laboratory method code, m = mean value, 250m = spatial resolution / block support: 250 m, b10..10cm = vertical reference: 10 cm depth below surface, 1950..2017 = time reference: period 1950-2017, v0.2 = version number: 0.2,
description: {"references": ["Hengl, T., MacMillan, R.A., (2019). Predictive Soil Mapping with R. OpenGeoHub foundation, Wageningen, the Netherlands, 370 pages, www.soilmapper.org, ISBN: 978-0-359-30635-0."]}
DOI: 10.5517/ccdc.csd.cc1ztkdr
title: CCDC 1841815: Experimental Crystal Structure Determination

description: Related Article: Iñaki Galve, Raül Ondoño, Claudi de Rocafiguera, Raimon Puig de la Bellacasa, Xavier Batllori, Cristina Puigjaner, Mercè Font-Bardia, Oriol Vallcorba, Jordi Teixidó, José I. Borrell|2020|Org.Biomol.Chem.|18|9810|doi:10.1039/D0OB01785J
DOI: 10.5517/ccdc.csd.cc1zsznd
title: CCDC 1841265: Experimental Crystal Structure Determination

description: Related Article: Iñaki Galve, Raül Ondoño, Claudi de Rocafiguera, Raimon Puig de la Bellacasa, Xavier Batllori, Cristina Puigjaner, Mercè Font-Bardia, Oriol Vallcorba, Jordi Teixidó, José I. Borrell|2020|Org.Biomol.Chem.|18|9810|doi:10.1039/D0OB01785J
DOI: 10.5517/ccdc.csd.cc25895y
title: CCDC 2003969: Experimental Crystal Structure Determination

description: Related Article: Egor S. Zhilin, Michael S. Polkovnichenko, Ivan V. Ananyev, Leonid Fershtat, Nina N. Makhova|2020|ChemPhotoChem|4|5346|doi:10.1002/cptc.202000157
DOI: 10.5281/zenodo.1458947
title: Nighttime Lights PC1-4 based on the Version 4 DMSP-OLS Nighttime Lights Time Series 1997–2014

description: Nighttime Lights PC1-4 based on the Version 4 DMSP-OLS Nighttime Lights Time Series 1997–2014. Derived using SAGA GIS Principal Component analysis. Image and data processing by NOAA's National Geophysical Data Center. DMSP data collected by US Air Force Weather Agency. To access and visualize maps use: OpenLandMap.org If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels: Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/issues General questions and comments: https://disqus.com/home/forums/landgis/
description: {"references": ["Zhang, Q., & Seto, K. C. (2011). Mapping urbanization dynamics at regional and global scales using multi-temporal DMSP/OLS nighttime light data. Remote Sensing of Environment, 115(9), 2320-2329."]}
DOI: 10.5517/ccdc.csd.cc25896z
title: CCDC 2003970: Experimental Crystal Structure Determination

description: Related Article: Egor S. Zhilin, Michael S. Polkovnichenko, Ivan V. Ananyev, Leonid Fershtat, Nina N. Makhova|2020|ChemPhotoChem|4|5346|doi:10.1002/cptc.202000157
DOI: 10.5517/ccdc.csd.cc25ft6n
title: CCDC 2009271: Experimental Crystal Structure Determination

description: Related Article: Rai-Shung Liu,  Manoj D. Patil,  Balaji Sopanrao Kale|2020|Adv.Synth.Catal.|362|5658|doi:10.1002/adsc.202000879
description: Related Article: Manoj Patil|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc25hndr
title: CCDC 2011044: Experimental Crystal Structure Determination

description: Related Article: Robert J. Ward, Iker Del Rosal, Danielle N. Chirdon, Steven P. Kelley, Michael L. Tarlton, Laurent Maron, Justin R. Walensky|2020|Inorg.Chem.|59|16137|doi:10.1021/acs.inorgchem.0c03036
DOI: 10.5281/zenodo.1458946
title: Nighttime Lights PC1-4 based on the Version 4 DMSP-OLS Nighttime Lights Time Series 1997–2014

description: Nighttime Lights PC1-4 based on the Version 4 DMSP-OLS Nighttime Lights Time Series 1997–2014. Derived using SAGA GIS Principal Component analysis. Image and data processing by NOAA's National Geophysical Data Center. DMSP data collected by US Air Force Weather Agency. To access and visualize maps use: OpenLandMap.org If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels: Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/issues General questions and comments: https://disqus.com/home/forums/landgis/
description: {"references": ["Zhang, Q., & Seto, K. C. (2011). Mapping urbanization dynamics at regional and global scales using multi-temporal DMSP/OLS nighttime light data. Remote Sensing of Environment, 115(9), 2320-2329."]}
DOI: 10.5517/ccdc.csd.cc25hnfs
title: CCDC 2011045: Experimental Crystal Structure Determination

description: Related Article: Robert J. Ward, Iker Del Rosal, Danielle N. Chirdon, Steven P. Kelley, Michael L. Tarlton, Laurent Maron, Justin R. Walensky|2020|Inorg.Chem.|59|16137|doi:10.1021/acs.inorgchem.0c03036
DOI: 10.5517/ccdc.csd.cc25x8bq
title: CCDC 2023163: Experimental Crystal Structure Determination

description: Related Article: Jian Huang|2020|CSD Communication|||
description: Related Article: Jian Huang, Yang Fu, Zhihong Deng, Wei Chen, Zhibin Song, Yiyuan Peng|2020|Org.Biomol.Chem.|18|9863|doi:10.1039/D0OB02060E
DOI: 10.5281/zenodo.2525681
title: Coarse fragments % (volumetric) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution

description: Coarse fragments % (volumetric) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. Based on machine learning predictions from global compilation of soil profiles and samples. Processing steps are described in detail <strong>here</strong>. Antarctica is not included. To access and visualize maps use: OpenLandMap.org If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels: Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/issues General questions and comments: https://disqus.com/home/forums/landgis/ All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: sol = theme: soil, coarsefrag.vfraction = variable: coarse fragments volumetric fraction, usda.3b1 = determination method: laboratory method code, m = mean value, 250m = spatial resolution / block support: 250 m, b10..10cm = vertical reference: 10 cm depth below surface, 1950..2017 = time reference: period 1950-2017, v0.2 = version number: 0.2,
description: {"references": ["Hengl, T., MacMillan, R.A., (2019). Predictive Soil Mapping with R. OpenGeoHub foundation, Wageningen, the Netherlands, 370 pages, www.soilmapper.org, ISBN: 978-0-359-30635-0."]}
DOI: 10.5281/zenodo.1447198
title: Global landform and lithology class at 250 m based on the USGS global ecosystem map

description: Layers include: lithology (15) and landform (7) indicator maps (0-100%). Derived from the USGS Global Ecosystem Map, i.e. the EcoTapestry map. Water bodies masked out. Antarctica is not included. To access and visualize maps use: OpenLandMap.org If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels: Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/issues General questions and comments: https://disqus.com/home/forums/landgis/ All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: dtm = theme: digital terrain models / relief and soil, lithology = variable: lithological class, usgs.ecotapestry = determination method: USGS Global Ecosystem Map, p = probability 0-100%, 250m = spatial resolution / block support: 1 km, s0..0cm = vertical reference: land surface, 2014 = time reference: year 2014, v1.0 = version number: 1.0,
description: {"references": ["Sayre, R., J. Dangermond, C. Frye, R. Vaughan, P. Aniello, S. Breyer, D. Cribbs, D. Hopkins, R. Nauman, W. Derrenbacher, D.Wright, C. Brown, C. Convis, J. Smith, L. Benson, D. Paco VanSistine, H. Warner, J. Cress, J. Danielson, S. Hamann, T. Cecere, A. Reddy, D. Burton, A. Grosse, D. True, M. Metzger, J. Hartmann, N. Moosdorf, H. D\u00fcrr, M. Paganini, P. DeFourny, O. Arino, S. Maynard, M. Anderson, and P. Comer, 2014, A New Map of Global Ecological Land Units \u2013 An Ecophysiographic Stratification Approach. Washington, DC: Association of American Geographers. 46 pages.", "Hartmann, J., & Moosdorf, N. (2012). The new global lithological map database GLiM: A representation of rock properties at the Earth surface. Geochemistry, Geophysics, Geosystems, 13(12)."]}
DOI: 10.25392/leicester.data.13417409
title: Rocking shadows in broken circumbinary discs

description: Contains reference data and scripts for Nealon et al. 2020b. Specifically:<br>- Jupyter notebooks to make all figures in the paper as well as movie (https://youtu.be/jGYLuEx-I78)<br>- First dumpfile for simulation, *.in file and first *.log file for reference<br>- Dumpfiles to make Figure 1<br>- Analysis outputs to make Figure 2<br>- Script to make schematic visualisation used in talks
DOI: 10.25392/leicester.data.13417409.v1
title: Rocking shadows in broken circumbinary discs

description: Contains reference data and scripts for Nealon et al. 2020b. Specifically:<br>- Jupyter notebooks to make all figures in the paper as well as movie (https://youtu.be/jGYLuEx-I78)<br>- First dumpfile for simulation, *.in file and first *.log file for reference<br>- Dumpfiles to make Figure 1<br>- Analysis outputs to make Figure 2<br>- Script to make schematic visualisation used in talks
DOI: 10.25392/leicester.data.13482570
title: Flyby-induced misalignments in planet-hosting discs

description: Data to recreate the published plots/replicate most results. Codes used are publicly available or on request (Phantom). Specifically, files included:<br>- analysis files for Figure 1- relative misalignment files for all simulations (Figure 2 and 3)- relative misalignment files for Figure 4- simulation dump files for Figure 5- analysis files for Figure A1- initial simulation dump files for all 10 simulations (listed in Table 1)- python scripts to make all figures- reference *.log and *.in file from simulation R1
DOI: 10.25392/leicester.data.13482570.v1
title: Flyby-induced misalignments in planet-hosting discs

description: Data to recreate the published plots/replicate most results. Codes used are publicly available or on request (Phantom). Specifically, files included:<br>- analysis files for Figure 1- relative misalignment files for all simulations (Figure 2 and 3)- relative misalignment files for Figure 4- simulation dump files for Figure 5- analysis files for Figure A1- initial simulation dump files for all 10 simulations (listed in Table 1)- python scripts to make all figures- reference *.log and *.in file from simulation R1
DOI: 10.5281/zenodo.1475453
title: Soil organic carbon stock in kg/m2 for 5 standard depth intervals (0–10, 10–30, 30–60, 60–100 and 100–200 cm) at 250 m resolution

description: Soil organic carbon stock in kg/m<sup>2</sup> for 5 standard depth intervals (0–10, 10–30, 30–60, 60–100 and 100–200 cm) at 250 m resolution. To convert to t/ha multiply by 10. Derived using soil organic carbon content (https://doi.org/10.5281/zenodo.1475457), bulk density (https://doi.org/10.5281/zenodo.1475970) and coarse fragments (https://doi.org/10.5281/zenodo.2525681), predicted from point data at 6 standard depths. Depth to bed rock has been ignored, hence total stocks might be about 10–15% lower then reported. Processing steps are described in detail <strong>here</strong>. Antarctica is not included. To access and visualize maps use: <strong>https://openlandmap.org</strong> If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels: Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/issues General questions and comments: https://disqus.com/home/forums/landgis/ All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: sol = theme: soil, organic.carbon.stock = variable: soil organic carbon stock in kg/m2, msa.kgm2 = determination method: derived from organic carbon content, bulk density and coarse fragments, m = mean value, 250m = spatial resolution / block support: 250 m, b0..10cm = vertical reference: 0-10 cm layer below surface, 1950..2017 = time reference: period 1950-2017, v0.2 = version number: 0.2,
description: {"references": ["Sanderman, J., Hengl, T., Fiske, G., (2017). The soil carbon debt of 12,000 years of human land use. PNAS, https://dx.doi.org/10.1073/pnas.1706103114", "Hengl, T., de Jesus, J.M., Heuvelink, G.B., Gonzalez, M.R., Kilibarda, M., Blagoti\u0107, A., Shangguan, W., Wright, M.N., Geng, X., Bauer-Marschallinger, B. and Guevara, M.A., (2017). SoilGrids250m: Global gridded soil information based on machine learning. PLoS one, 12(2), p.e0169748. https://doi.org/10.1371/journal.pone.0169748", "Hengl, T., MacMillan, R.A., (2019). Predictive Soil Mapping with R. OpenGeoHub foundation, Wageningen, the Netherlands, 370 pages, www.soilmapper.org, ISBN: 978-0-359-30635-0."]}
DOI: 10.5281/zenodo.1475449
title: Soil organic carbon stock (0–30 cm) in kg/m2 time-series 2001–2015 based on the land cover changes

description: Estimated SOC loss based on the European Space Agency (ESA) Climate Change Initiative (ESACCI-LC) land cover maps 2001–2015. This only shows estimated SOC loss (in kg/m2) as a result of change in land use / land cover (assuming standard change factors based on the literature and IPCC reports). Methodology produced for the purpose of the Land Degradation Neutrality (UNCCD) project. Processing steps are described in detail <strong>here</strong>. Antartica is not included. To access and visualize maps use: OpenLandMap.org If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels: Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/issues General questions and comments: https://disqus.com/home/forums/landgis/ All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: sol = theme: soil, organic.carbon.stock = variable: soil organic carbon stock in kg/m2, msa.kgm2 = determination method: derived from carbon content, bulk density and coarse fragments, m = mean value, 250m = spatial resolution / block support: 250 m, b0..30cm = vertical reference: standard layer 0-30 cm below surface, 2014 = time reference: year 2014, v0.2 = version number: 0.2,
description: {"references": ["Hengl, T., MacMillan, R.A., (2019). Predictive Soil Mapping with R. OpenGeoHub foundation, Wageningen, the Netherlands, 370 pages, www.soilmapper.org, ISBN: 978-0-359-30635-0."]}
DOI: 10.6084/m9.figshare.13483266
title: Neutral Affordances: Task Conflict in the Affordances Task

description: Experimental data for the experiments described in the manuscript titled 'Neutral Affordances: Task Conflict in the Affordances Task '
DOI: 10.6084/m9.figshare.13483266.v1
title: Neutral Affordances: Task Conflict in the Affordances Task

description: Experimental data for the experiments described in the manuscript titled 'Neutral Affordances: Task Conflict in the Affordances Task '