cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5291/ill-data.easy-488
title: Structural transition in Cs2AgBiCl6materials for photovoltaic applications
DOI: 10.5291/ill-data.inter-378
title: Investigating the pore accessibility in wood using SANS
DOI: 10.5291/ill-data.test-3128
title: Battery operando experiments on D19(test on feasibility)
DOI: 10.5291/ill-data.test-2747
title: SANS characterization of Northern European woods
DOI: 10.5291/ill-data.uga-114
title: Detection of microplastic particles in self-constructed and undistrubed sediment samples by combined neutron and x-ray imaging
DOI: 10.5291/ill-data.uga-115
title: Crystallisation-induced damage in heterogeneous rocks- Apllication to haloclasty
DOI: 10.5291/ill-data.uga-50
title: Fracturing of stone caused by salt weathering
DOI: 10.5291/ill-data.uga-61
title: Cristallisation-induced damage in heterogeneous rocks - Application to haloclasty
DOI: 10.5291/ill-data.uga-90
title: Cristallisation-induced damage in heterogeneous rocks - application to haloclasty
DOI: 10.6084/m9.figshare.13483149
title: Solar database

description: Dataset composed from EPA databases.
DOI: 10.5281/zenodo.4333554
title: Best4Soil_databases_datamining_V1

description: This document contains data on the host plant status of field crops, vegetable crops and green manure crops for soilborne plant parasitic nematodes and soilborne fungal pathogens. The data was used for the construction of two databases in context of the Best4Soil Thematic Network, a Horizon 2020 project (Grant Agreement n°817696). The databases are accessible through the website https://ww.best4soil.eu/database ; they are hosted on https://nematodes.soilhealthtool.eu/
DOI: 10.6084/m9.figshare.13483203
title: Appendix B from Bone need not remain an elephant in the room for radiocarbon dating

description: Raw responses to questionnaire survey (*.xlsx)
DOI: 10.6084/m9.figshare.13483203.v1
title: Appendix B from Bone need not remain an elephant in the room for radiocarbon dating

description: Raw responses to questionnaire survey (*.xlsx)
DOI: 10.6084/m9.figshare.13483206
title: Appendix A from Bone need not remain an elephant in the room for radiocarbon dating

description: Questionnaire survey (layout, *.html)
DOI: 10.6084/m9.figshare.13483206.v1
title: Appendix A from Bone need not remain an elephant in the room for radiocarbon dating

description: Questionnaire survey (layout, *.html)
DOI: 10.15468/7eftff
title: Studies on Neotropical Phasmatodea XXIV: Andeocalynda n. gen., a new genus of Andean stick insects, with the descriptions of nine new species from Colombia and Ecuador (Phasmatodea: “ Anareolatae ”: Diapheromeridae: Diapheromerinae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Hennemann, Frank H., Conle, Oskar V. (2020): Studies on Neotropical Phasmatodea XXIV: Andeocalynda n. gen., a new genus of Andean stick insects, with the descriptions of nine new species from Colombia and Ecuador (Phasmatodea: “ Anareolatae ”: Diapheromeridae: Diapheromerinae). Zootaxa 4896 (3): 301-341, DOI: https://doi.org/10.11646/zootaxa.4896.3.1
DOI: 10.21227/gkwf-bq40
title: Multi-ion-sensing emulator

description: One paramount challenge in multi-ion-sensing arises from ion interference that degrades theaccuracy of sensor calibration. Machine learning models are here proposed to optimize such multivariatecalibration. However, the acquisition of big experimental data is time and resource consuming in practice,necessitating new paradigms and efficient models for these data-limited frameworks. Therefore, a novelapproach is presented in this work, where a multi-ion-sensing emulator is designed to explain the responseof an ion-sensing array in a mixed-ion environment. A case study is performed emulating the concurrentmonitoring of sodium, potassium, lithium, and lead ions, in a medium representative of sweat samples.These analytes are relevant examples of sweat ion-sensing applications for physiology, therapeutic drugmonitoring, and heavy metal contamination. Synthetic training datasets are generated following a factorial design of experiment in the range of interest of the considered ions in artificial sweat samples, while validation and test sets are generated following a random design of experiment with Weibull probability distribution function. Three datasets of scaled size are generated to assess the performance of the machine leaning models with scarse and large datasets.
DOI: 10.21227/5cc4-3d70
title: Multi-ion-sensing emulator

description: One paramount challenge in multi-ion-sensing arises from ion interference that degrades theaccuracy of sensor calibration. Machine learning models are here proposed to optimize such multivariatecalibration. However, the acquisition of big experimental data is time and resource consuming in practice,necessitating new paradigms and efficient models for these data-limited frameworks. Therefore, a novelapproach is presented in this work, where a multi-ion-sensing emulator is designed to explain the responseof an ion-sensing array in a mixed-ion environment. A case study is performed emulating the concurrentmonitoring of sodium, potassium, lithium, and lead ions, in a medium representative of sweat samples.These analytes are relevant examples of sweat ion-sensing applications for physiology, therapeutic drugmonitoring, and heavy metal contamination. Synthetic training datasets are generated following a factorial design of experiment in the range of interest of the considered ions in artificial sweat samples, while validation and test sets are generated following a random design of experiment with Weibull probability distribution function. Three datasets of scaled size are generated to assess the performance of the machine leaning models with scarse and large datasets.
DOI: 10.5281/zenodo.58984
title: IMPETUS: New Cloudy's radiative tables for accretion onto a galaxy black hole. Calculations I-VI. Table 2.

description: These are gzipped/tar files, which contain the results of Calculations I-VI. The main directories contain sub-directories with ascii files. Each column is properly described in the article. The equilibrium temperature for any configuration can be calculated and plot with the new script at https://doi.org/10.5281/zenodo.4381019.
description: This is version 1.0 of the Tables, computed with Cloudy v. 13.03
DOI: 10.5281/zenodo.2657408
title: Predicted USDA soil suborders at 250 m (probabilities)

description: Distribution of the USDA suborders based on machine learning predictions of great groups (https://doi.org/10.5281/zenodo.1476844) from global compilation of soil profiles. To learn more about soil suborders and great groups please refer to the Illustrated Guide to Soil Taxonomy - NRCS - USDA. 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, suborder = variable: USDA suborder, usda.ustolls = determination method: USDA soil taxonomy class Ustolls, p = probability, 250m = spatial resolution / block support: 250 m, s0..0cm = vertical reference: soil surface, 1950..2017 = time reference: period 1950-2017, v0.1 = version number: 0.1,
description: {"references": ["Hengl T, Mendes de Jesus J, Heuvelink GBM, Ruiperez Gonzalez M, Kilibarda M, Blagoti\u0107 A, et al. (2017) SoilGrids250m: Global gridded soil information based on machine learning. PLoS ONE 12(2): e0169748.", "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.", "USDA-NRCS, (2014). Illustrated Guide to Soil Taxonomy: Version 1.0, September 20014, U.S. Department of Agriculture, Natural Resources Conservation Service, National Soil Survey Center."]}
DOI: 10.5281/zenodo.2657407
title: Predicted USDA soil suborders at 250 m (probabilities)

description: Distribution of the USDA suborders based on machine learning predictions of great groups (https://doi.org/10.5281/zenodo.1476844) from global compilation of soil profiles. To learn more about soil suborders and great groups please refer to the Illustrated Guide to Soil Taxonomy - NRCS - USDA. 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, suborder = variable: USDA suborder, usda.ustolls = determination method: USDA soil taxonomy class Ustolls, p = probability, 250m = spatial resolution / block support: 250 m, s0..0cm = vertical reference: soil surface, 1950..2017 = time reference: period 1950-2017, v0.1 = version number: 0.1,
description: {"references": ["Hengl T, Mendes de Jesus J, Heuvelink GBM, Ruiperez Gonzalez M, Kilibarda M, Blagoti\u0107 A, et al. (2017) SoilGrids250m: Global gridded soil information based on machine learning. PLoS ONE 12(2): e0169748.", "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.", "USDA-NRCS, (2014). Illustrated Guide to Soil Taxonomy: Version 1.0, September 20014, U.S. Department of Agriculture, Natural Resources Conservation Service, National Soil Survey Center."]}
DOI: 10.5281/zenodo.2658183
title: Predicted USDA soil orders at 250 m (probabilities)

description: Distribution of the USDA orders (12) based on machine learning predictions of great groups (https://doi.org/10.5281/zenodo.1476844) from global compilation of soil profiles. To learn more about soil orders and great groups please refer to the Illustrated Guide to Soil Taxonomy - NRCS - USDA. 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, order = variable: USDA order, usda.histosols = determination method: USDA soil taxonomy class Histosols, p = probability, 250m = spatial resolution / block support: 250 m, s0..0cm = vertical reference: soil surface, 1950..2017 = time reference: period 1950-2017, v0.1 = version number: 0.1,
description: {"references": ["USDA-NRCS, (2014). Illustrated Guide to Soil Taxonomy: Version 1.0, September 20014, U.S. Department of Agriculture, Natural Resources Conservation Service, National Soil Survey Center."]}
DOI: 10.5281/zenodo.2658182
title: Predicted USDA soil orders at 250 m (probabilities)

description: Distribution of the USDA orders (12) based on machine learning predictions of great groups (https://doi.org/10.5281/zenodo.1476844) from global compilation of soil profiles. To learn more about soil orders and great groups please refer to the Illustrated Guide to Soil Taxonomy - NRCS - USDA. 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, order = variable: USDA order, usda.histosols = determination method: USDA soil taxonomy class Histosols, p = probability, 250m = spatial resolution / block support: 250 m, s0..0cm = vertical reference: soil surface, 1950..2017 = time reference: period 1950-2017, v0.1 = version number: 0.1,
description: {"references": ["USDA-NRCS, (2014). Illustrated Guide to Soil Taxonomy: Version 1.0, September 20014, U.S. Department of Agriculture, Natural Resources Conservation Service, National Soil Survey Center."]}
DOI: 10.5281/zenodo.3355007
title: Global river density, seasonal and surface water occurrence and upstream area at 250 m in the Goode Homolosine projection

description: Several layers describing density of surface water / streams projected to the Good Homolosine projection. List of layers included: hyd_log1p.upstream.area_merit.hydro_m = Upstream Drainage Area based on the MERIT Hydro, hyd_river.density_gloric_p = rasterized Global River Classification (GLORIC) DB, lcv_water.occurance_jrc.surfacewater_p = Surface Water based on the JRC's Global Surface Water, lcv_water.seasonal_probav.glc.lc100_p = Seasonal Inland Water probability based on the Copernicus LC100 map, lcv_wetlands.cw_upmc.wtd_c = composite wetland (CW) map based on Tootchi et al. (2019), Goode_Homolosine_domain_250m.tif = map domain prepared by Luís de Sousa, tiles_GH_100km_land.gpkg = 100 km x 100 km tiling system covering the land mass, Important notes: Processing steps are described in detail <strong>here</strong>. Antartica is not included. Reprojecting maps to Goode Homolosine projection can be cumbersome and small amount of artifacts at the edges of the map can be anticipated. These maps were develop in connection to the OpenLandMap.org initiative. 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: hyd = theme: hydrology and water dynamics, log1p.upstream.area = variable: log(X+1)*10 of the upstream area, merit.hydro = determination method: MERIT Hydro, m = mean value, 250m = spatial resolution / block support: 250 m, b0..0cm = vertical reference: surface, 2017 = time reference: period 2017, v0.1 = version number: 0.1,
description: {"references": ["Pekel, J. F., Cottam, A., Gorelick, N., & Belward, A. S. (2016). High-resolution mapping of global surface water and its long-term changes. Nature, 540(7633), 418.", "Ouellet Dallaire, C., Lehner, B., Sayre, R., Thieme, M. (2018): A multidisciplinary framework to derive global river reach classifications at high spatial resolution. Environmental Research Letters. doi: 10.1088/1748-9326/aad8e9", "Tootchi, A., Jost, A., and Ducharne, A. (2019): Multi-source global wetland maps combining surface water imagery and groundwater constraints, Earth Syst. Sci. Data, 11, 189-220, https://doi.org/10.5194/essd-11-189-2019", "Underwood, E. (2019): A more accurate global river map, Eos, 100, https://doi.org/10.1029/2019EO128033", "Yamazaki D., D. Ikeshima, J. Sosa, P.D. Bates, G.H. Allen, T.M. Pavelsky, (2019): MERIT Hydro: A high-resolution global hydrography map based on latest topography datasets Water Resources Research, ACCEPTED, https://doi.org/10.1029/2019WR024873"]}
DOI: 10.5281/zenodo.3355006
title: Global river density, seasonal and surface water occurrence and upstream area at 250 m in the Goode Homolosine projection

description: Several layers describing density of surface water / streams projected to the Good Homolosine projection. List of layers included: hyd_log1p.upstream.area_merit.hydro_m = Upstream Drainage Area based on the MERIT Hydro, hyd_river.density_gloric_p = rasterized Global River Classification (GLORIC) DB, lcv_water.occurance_jrc.surfacewater_p = Surface Water based on the JRC's Global Surface Water, lcv_water.seasonal_probav.glc.lc100_p = Seasonal Inland Water probability based on the Copernicus LC100 map, lcv_wetlands.cw_upmc.wtd_c = composite wetland (CW) map based on Tootchi et al. (2019), Goode_Homolosine_domain_250m.tif = map domain prepared by Luís de Sousa, tiles_GH_100km_land.gpkg = 100 km x 100 km tiling system covering the land mass, Important notes: Processing steps are described in detail <strong>here</strong>. Antartica is not included. Reprojecting maps to Goode Homolosine projection can be cumbersome and small amount of artifacts at the edges of the map can be anticipated. These maps were develop in connection to the OpenLandMap.org initiative. 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: hyd = theme: hydrology and water dynamics, log1p.upstream.area = variable: log(X+1)*10 of the upstream area, merit.hydro = determination method: MERIT Hydro, m = mean value, 250m = spatial resolution / block support: 250 m, b0..0cm = vertical reference: surface, 2017 = time reference: period 2017, v0.1 = version number: 0.1,
description: {"references": ["Pekel, J. F., Cottam, A., Gorelick, N., & Belward, A. S. (2016). High-resolution mapping of global surface water and its long-term changes. Nature, 540(7633), 418.", "Ouellet Dallaire, C., Lehner, B., Sayre, R., Thieme, M. (2018): A multidisciplinary framework to derive global river reach classifications at high spatial resolution. Environmental Research Letters. doi: 10.1088/1748-9326/aad8e9", "Tootchi, A., Jost, A., and Ducharne, A. (2019): Multi-source global wetland maps combining surface water imagery and groundwater constraints, Earth Syst. Sci. Data, 11, 189-220, https://doi.org/10.5194/essd-11-189-2019", "Underwood, E. (2019): A more accurate global river map, Eos, 100, https://doi.org/10.1029/2019EO128033", "Yamazaki D., D. Ikeshima, J. Sosa, P.D. Bates, G.H. Allen, T.M. Pavelsky, (2019): MERIT Hydro: A high-resolution global hydrography map based on latest topography datasets Water Resources Research, ACCEPTED, https://doi.org/10.1029/2019WR024873"]}
DOI: 10.5281/zenodo.1443569
title: MOD17A2H version 6 Gross Primary Productivity (GPP) global mosaics at 500 m resolution and difference 2000-2017

description: MOD17A2H version 6 Gross Primary Productivity (GPP) global mosaics at 500 m resolution and difference in GPP for the period 2000-2017. Changes in GPP could be used to estimate land degradation or similar. Derived using the data.table package and quantile function in R. For more info about the MODIS LST product see: https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod17a2h_v006. Antartica is not included. 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: veg = theme: vegetation, gpp = variable: gross primary productivity in kg C m<sup>2</sup>, mod17a2h.oct = determination method: MOD17A2H product, GPP values for October, d = median value / difference = difference between periods / u.975 = aggregation/statistics method: 97.5% probability upper quantile, 500m = spatial resolution / block support: 500 m, s0..0cm = vertical reference: land surface, 2000..2017 = time reference: from 2000 to 2017, v1.0 = version number: 1.0,
description: {"references": ["Running, S., Mu, Q., Zhao, M. (2015). MOD17A2H MODIS/Terra Gross Primary Productivity 8-Day L4 Global 500m SIN Grid V006 [Data set]. NASA EOSDIS Land Processes DAAC. doi: 10.5067/MODIS/MOD17A2H.006"]}
DOI: 10.6084/m9.figshare.13483194
title: Does disparity in income and consumption ever incite terrorism in Africa?

description: The datasets are collated from reputable international organization to analyse the links between inequality (in consumption and income) and terrorism in Africa. The data are for 46 African countries. The details of the datasets consist domter - Domestic terrorism; transter - Transnational terrorism; unter - Unclear terrorism; totter - Total terrorism; gini - Gini coefficient; theil - Theil coefficient; atkin - Atkinson coefficient; palma - Palma ratio; totnat - total natural resources; surface - Surface area(log); polreg - political regime; conflicts - Dummy (1 conflicts, 0 no conflict); trade - Trade openness; and gdppc - GDP per capita.<br>
DOI: 10.6084/m9.figshare.13483194.v1
title: Does disparity in income and consumption ever incite terrorism in Africa?

description: The datasets are collated from reputable international organization to analyse the links between inequality (in consumption and income) and terrorism in Africa. The data are for 46 African countries. The details of the datasets consist domter - Domestic terrorism; transter - Transnational terrorism; unter - Unclear terrorism; totter - Total terrorism; gini - Gini coefficient; theil - Theil coefficient; atkin - Atkinson coefficient; palma - Palma ratio; totnat - total natural resources; surface - Surface area(log); polreg - political regime; conflicts - Dummy (1 conflicts, 0 no conflict); trade - Trade openness; and gdppc - GDP per capita.<br>
DOI: 10.15472/djlrfu
title: Lista de especímenes de fauna, flora y hongos del Parque Nacional Natural Serranía de Chiribiquete

description: Desde el año de 1991, Parques Nacionales Naturales (PNN), en convenio con diferentes instituciones, viene desarrollando inventarios de fauna y flora y estudios en arqueología y etnohistoria en el Parque Nacional Natural Serranía de Chiribiquete, con el fin de explorar y conocer la gran diversidad y riqueza natural que alberga la Amazonia colombiana. De esta manera, la presente lista de especies se crea para asegurar el acceso libre y gratuito para las personas interesadas en la Amazonia Colombiana referente al PNN SCh. Es así como, se presenta la lista de los taxones de fauna, flora y hongos identificados al interior del parque. Los cuales fueron seleccionados a partir de la localización de los registró biológicos de la base de datos de múltiples fuentes que se ubican al interior del área del parque actualizada a julio de 2020.