cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3528062
title: Predicted USDA soil great groups at 250 m (probabilities)

description: Distribution of the USDA soil great groups based on machine learning predictions from global compilation of soil profiles (&gt;350,000 training points). To learn more about soil great groups please refer to the Illustrated Guide to Soil Taxonomy - NRCS - USDA. Processing steps are described in detail <strong>here</strong>. Antarctica is not included. To access and visualize maps use: OpenLandMap.org A back-up copy of all predictions (&gt;65GB) can be downloaded from: http://gofile.me/6J25n/mQ3cHOOMr 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, grtgroup = variable: USDA great group, usda.argiustolls = determination method: USDA soil taxonomy class Argiustolls, p = probability, 250m = spatial resolution / block support: 250 m, s0..0cm = vertical reference: soil surface, 1950..2017 = time reference: period 1950-2017, v0.2 = version number: 0.2,
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.", "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.", "Silva, B. P. C., Silva, M. L. N., Avalos, F. A. P., de Menezes, M. D., & Curi, N. (2019). Digital soil mapping including additional point sampling in Posses ecosystem services pilot watershed, southeastern Brazil. Scientific reports, 9(1), 1-12.", "Samuel-Rosa, A., Dalmolin, R. S. D., MOURA-BRUNO, J. M., Teixeira, W. G., & FILIPPINI ALBA, J. M. (2018). Open legacy soil survey data in Brazil: geospatial data quality and how to improve it. Embrapa Clima Temperado-Artigo em peri\u00f3dico indexado (ALICE)."]}
DOI: 10.5281/zenodo.2629149
title: Soil available water capacity in mm derived for 5 standard layers (0-10, 10-30, 30-60, 60-100 and 100-200 cm) at 250 m resolution

description: Available Water Capacity (in mm) derived by calculating Water Retention Difference (difference between the field capacity and wilting point; see NRCS Soil Survey Laboratory Methods Manual), and then summing up WRD for all standard layers (0–200 cm). Soil water content (volumetric) in percent for 33 kPa and 1500 kPa suctions predicted at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution is available <strong>here</strong>. These estimates ignore depth to bedrock i.e. existence of any impenetrable layer (total available capacity over the whole land mass is likely about 10–15% smaller). Antarctica is not included. To access and visualize some of the 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, available.water.capacity = available water capacity in mm, usda.mm = determination method: Water Retention Difference in mm, 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, v0.1 = version number: 0.1,
description: {"references": ["Bleam, W. F. (2016). Soil and environmental chemistry. Academic Press. http://dx.doi.org/10.1016/B978-0-12-804178-9.00002-1", "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.2629148
title: Soil available water capacity in mm derived for 5 standard layers (0-10, 10-30, 30-60, 60-100 and 100-200 cm) at 250 m resolution

description: Available Water Capacity (in mm) derived by calculating Water Retention Difference (difference between the field capacity and wilting point; see NRCS Soil Survey Laboratory Methods Manual), and then summing up WRD for all standard layers (0–200 cm). Soil water content (volumetric) in percent for 33 kPa and 1500 kPa suctions predicted at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution is available <strong>here</strong>. These estimates ignore depth to bedrock i.e. existence of any impenetrable layer (total available capacity over the whole land mass is likely about 10–15% smaller). Antarctica is not included. To access and visualize some of the 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, available.water.capacity = available water capacity in mm, usda.mm = determination method: Water Retention Difference in mm, 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, v0.1 = version number: 0.1,
description: {"references": ["Bleam, W. F. (2016). Soil and environmental chemistry. Academic Press. http://dx.doi.org/10.1016/B978-0-12-804178-9.00002-1", "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.1475970
title: Soil bulk density (fine earth) 10 x kg / m-cubic at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution

description: Soil bulk density (fine earth) 10 x kg / m<sup>3</sup> at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. 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, bulkdens.fineearth = variable: soil bulk density, usda.4a1h = 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., 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", "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., 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: 10.25345/c54f6h
title: MassIVE MSV000086633 - Sec24C, Sec24D and Sec31A O-GlcNac mapping HCD/ETD Boyce Lab
title: Bottom up proteomic analysis of human Sec24C, Sec24D and Sec31A to characterize O-GlcNAc PTMs sites In gel tryptic digestion protoocl followed by LC-MS/MS. Briefly, each sample was subjected to chromatographic separation on a Waters NanoAquity UPLC equipped with a 1.7 um BEH130 C18 75 um I.D. x 250 mm reversed-phase column. The mobile phase consisted of (A) 0.1% formic acid in water and (B) 0.1% formic acid in acetonitrile. Following a 5 uL injection, peptides were trapped for 5 min on a 5 um Symmetry C18 180 um I.D. X 20 mm column at 20 ul/min in 99.9% A. The analytical column was held at 5% B for 5 min then switched in-line and a linear elution gradient of 5% B to 40% B was performed over 60 min at 300 nL/min. The analytical column was connected to a fused silica PicoTip emitter (New Objective, Cambridge, MA) with a 10 um tip orifice and coupled to a Fusion Lumos Orbitrap mass spectrometer through an electrospray interface. The instrument was set to acquire a precursor MS scan from m/z 350-1800 with r = 120,000 at m/z 400 and a target AGC setting of 4e5 ions. In a data-dependent mode of acquisition, MS/MS spectra were acquired at r = 15000 at m/z with a target AGC setting of 5e4 ions. Max fill times were set to 50 ms for full MS scans and 45 ms for MS/MS scans with minimum MS/MS triggering thresholds of 5000 counts. Neutral losses corresponding to loss of GlcNAc were further supplemented to EThcD MS/MS scans at r=30,000 with agc target of 3e5 and max IT of 250 ms. For all experiments, a dynamic exclusion of 60 s was employed for previously fragmented precursor ions. Raw LC-MS/MS data files were processed in Mascot distiller (Matrix Science) and then submitted to independent Mascot database searches (Matrix Science) against SwissProt (homo sapien taxonomy) containing both forward and reverse entries of each protein. Search tolerances were 5 ppm for precursor ions and 0.02 Da for product ions using trypsin specificity with up to two missed cleavages. Carbamidomethylation (+57.0214 Da on C) was set as a fixed modification, whereas oxidation (+15.9949 Da on M) and OGlcNacylation (+203 Da on ST) were considered variable modifications. All searched spectra were imported into Scaffold (Proteome Software) and protein confidence thresholds were set using a Bayesian statistical algorithm based on the PeptideProphet and ProteinProphet algorithms which yielded a peptide and protein false discovery rate of 1%.
DOI: 10.5281/zenodo.1475459
title: Soil pH in H2O at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution

description: Soil pH in H2O in × 10 at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. 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, ph.h2o = variable: soil pH in H2O, usda.4c1a2a = 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": ["USDA-NRCS, (2014) Laboratory Methods Manual (SSIR 42). U.S. Department of Agriculture, Natural Resources Conservation Service, National Soil Survey Center.", "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."]}
DOI: 10.5281/zenodo.1447209
title: Global DEM derivatives at 250 m, 1 km and 2 km based on the MERIT DEM

description: Layers include: various DEM derivatives computed using SAGA GIS at 250 m and using MERIT DEM (Yamazaki et al., 2017) as input. Antartica is not included. MERIT DEM was first reprojected to 6 global tiles based on the Equi7 grid system (Bauer-Marschallinger et al. 2014) and then these were used to derive all DEM derivatives. To access original DEM tiles please refer to MERIT DEM download page. 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, twi = variable: SAGA GIS Topographic Wetness Index, merit.dem = determination method: MERIT DEM, m = mean value, 1km = spatial resolution / block support: 1 km, s0..0cm = vertical reference: land surface, 2017 = time reference: year 2017, v1.0 = version number: 1.0,
description: {"references": ["Yamazaki, D., Ikeshima, D., Tawatari, R., Yamaguchi, T., O'Loughlin, F., Neal, J. C., ... & Bates, P. D. (2017). A high\u2010accuracy map of global terrain elevations. Geophysical Research Letters, 44(11), 5844-5853.", "Bauer-Marschallinger, B., Sabel, D., & Wagner, W. (2014). Optimisation of global grids for high-resolution remote sensing data. Computers & Geosciences, 72, 84-93."]}
DOI: 10.6084/m9.figshare.13483290
title: Investigation of skin microbiota reveals Mycobacterium ulcerans-Aspergillus sp. trans-kingdom communication.

description: Data from culture of the skin microbiota of asymptomatic <i>M. ulcerans</i> carriers and negative control individuals, all living in the region of Sindou, Burkina Faso. Different bacterial and fungal species were isolated from <i>M. ulcerans</i>-negative skin samples, and <i>M. ulcerans</i>-positive samples. More specifically, Actinobacteria, <i>Aspergillus</i> <i>niger</i> and <i>Aspergillus flavus</i> were significantly associated with <i>M. ulcerans</i> skin carriage.
DOI: 10.6084/m9.figshare.13483290.v1
title: Investigation of skin microbiota reveals Mycobacterium ulcerans-Aspergillus sp. trans-kingdom communication.

description: Data from culture of the skin microbiota of asymptomatic <i>M. ulcerans</i> carriers and negative control individuals, all living in the region of Sindou, Burkina Faso. Different bacterial and fungal species were isolated from <i>M. ulcerans</i>-negative skin samples, and <i>M. ulcerans</i>-positive samples. More specifically, Actinobacteria, <i>Aspergillus</i> <i>niger</i> and <i>Aspergillus flavus</i> were significantly associated with <i>M. ulcerans</i> skin carriage.
DOI: 10.5281/zenodo.1439254
title: Landsat bands (cloud free), tree cover (2000, 2010), bare-ground and surface water occurrence at 250 m based on GlobalForestWatch and USGS

description: Landsat bands (cloud free) and tree cover (2000) based on Hansen et al. (2013), global surface water occurrence based on Pekel at al. (2016), and tree cover and bare-ground cover (2010) based the USGS land cover mapping projects (University of Maryland, Department of Geographical Sciences and USGS). All layers resampled to spatial resolution 1/480 d.d. (about 250 m) using gdalwarp with "average" resampling. Antarctica is not included. Original layers are available at 30 m resolution. 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: lcv = theme: land cover, bareground = variable: occurrence of bareground, landsat.usgs = determination method: Landsat landcover at 30 m resolution project (https://landcover.usgs.gov/glc/), p = probability or fraction, 250m = spatial resolution / block support: 250 m, s0..0cm = vertical reference: land surface, 2010..2010 = time reference: year 2010, v1.0 = version number: 1.0,
description: {"references": ["Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. \"High-Resolution Global Maps of 21st-Century Forest Cover Change.\" Science 342 (15 November): 850\u201353. Data available on-line from: http://earthenginepartners.appspot.com/science-2013-global-forest.", "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."]}
DOI: 10.5281/zenodo.1439253
title: Landsat bands (cloud free), tree cover (2000, 2010), bare-ground and surface water occurrence at 250 m based on GlobalForestWatch and USGS

description: Landsat bands (cloud free) and tree cover (2000) based on Hansen et al. (2013), global surface water occurrence based on Pekel at al. (2016), and tree cover and bare-ground cover (2010) based the USGS land cover mapping projects (University of Maryland, Department of Geographical Sciences and USGS). All layers resampled to spatial resolution 1/480 d.d. (about 250 m) using gdalwarp with "average" resampling. Antarctica is not included. Original layers are available at 30 m resolution. 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: lcv = theme: land cover, bareground = variable: occurrence of bareground, landsat.usgs = determination method: Landsat landcover at 30 m resolution project (https://landcover.usgs.gov/glc/), p = probability or fraction, 250m = spatial resolution / block support: 250 m, s0..0cm = vertical reference: land surface, 2010..2010 = time reference: year 2010, v1.0 = version number: 1.0,
description: {"references": ["Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. \"High-Resolution Global Maps of 21st-Century Forest Cover Change.\" Science 342 (15 November): 850\u201353. Data available on-line from: http://earthenginepartners.appspot.com/science-2013-global-forest.", "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."]}
DOI: 10.6084/m9.figshare.13483323.v1
title: Data analysis of The tadpoles and advertisement call of Trachycephalus imitatrix and T. dibernardoi (Anura: Hylidae)

description: Herein, we describe the external larval morphology, internal oral features, and chondrocranium of <i>Trachycephalus imitatrix </i>and<i> T. dibernardoi</i> and the advertisement call of <i>T. imitatrix</i>. To our knowledge, this is the first thorough description of the larval chondrocranium for the genus <i>Trachycephalus</i>.<i> </i>The tadpole of <i>T. imitatrix</i> is overall similar to the other congeneric species, but can be distinguished from them by the following combination of characteristics: presence of two folds in the posterior labium, clumped submarginal papillae, tail ending in a flagellum, buccal roof arena lacking papillae, and seven postnarial papillae. The tadpole of <i>T. dibernardoi</i> can be distinguished from its congeners by the following combination of characteristics: snout rounded in lateral view, absence of interruption in teeth rows, uniseriate and alternated row of marginal papillae, two pairs of infralabial papillae, absence of projection of the dorsal velum. The advertisement call of <i>T. imitatrix</i> is very similar to that of <i>T. nigromaculatus</i> but can be distinguished from it by its lower dominant frequency. Additionally, both spectral and temporal features of the call can distinguish <i>T. imitatrix</i> from other congeneric species. Our results help understand the differentiation of larval and acoustical characteristics within the genus <i>Trachycephalus</i>.
DOI: 10.6084/m9.figshare.13483323
title: Data analysis of The tadpoles and advertisement call of Trachycephalus imitatrix and T. dibernardoi (Anura: Hylidae)

description: Herein, we describe the external larval morphology, internal oral features, and chondrocranium of <i>Trachycephalus imitatrix </i>and<i> T. dibernardoi</i> and the advertisement call of <i>T. imitatrix</i>. To our knowledge, this is the first thorough description of the larval chondrocranium for the genus <i>Trachycephalus</i>.<i> </i>The tadpole of <i>T. imitatrix</i> is overall similar to the other congeneric species, but can be distinguished from them by the following combination of characteristics: presence of two folds in the posterior labium, clumped submarginal papillae, tail ending in a flagellum, buccal roof arena lacking papillae, and seven postnarial papillae. The tadpole of <i>T. dibernardoi</i> can be distinguished from its congeners by the following combination of characteristics: snout rounded in lateral view, absence of interruption in teeth rows, uniseriate and alternated row of marginal papillae, two pairs of infralabial papillae, absence of projection of the dorsal velum. The advertisement call of <i>T. imitatrix</i> is very similar to that of <i>T. nigromaculatus</i> but can be distinguished from it by its lower dominant frequency. Additionally, both spectral and temporal features of the call can distinguish <i>T. imitatrix</i> from other congeneric species. Our results help understand the differentiation of larval and acoustical characteristics within the genus <i>Trachycephalus</i>.
DOI: 10.34740/kaggle/dsv/1773519
title: Tongue Twister Dataset
DOI: 10.5517/ccdc.csd.cc246nvw
title: CCDC 1972618: Experimental Crystal Structure Determination

description: Related Article: Patricia M. Toro, Juan Oyarzo, Rodrigo Arancibia, Shane Wilkinson, Vania Artigas, Mauricio Fuentealba, Mauricio Moncada-Basualto, Claudio OleaAzar, Andrés Vega, A. Hugo Klahn|2021|Polyhedron|193|114872|doi:10.1016/j.poly.2020.114872
DOI: 10.5517/ccdc.csd.cc246nwx
title: CCDC 1972619: Experimental Crystal Structure Determination

description: Related Article: Patricia M. Toro, Juan Oyarzo, Rodrigo Arancibia, Shane Wilkinson, Vania Artigas, Mauricio Fuentealba, Mauricio Moncada-Basualto, Claudio OleaAzar, Andrés Vega, A. Hugo Klahn|2021|Polyhedron|193|114872|doi:10.1016/j.poly.2020.114872
DOI: 10.5517/ccdc.csd.cc246nxy
title: CCDC 1972620: Experimental Crystal Structure Determination

description: Related Article: Patricia M. Toro, Juan Oyarzo, Rodrigo Arancibia, Shane Wilkinson, Vania Artigas, Mauricio Fuentealba, Mauricio Moncada-Basualto, Claudio OleaAzar, Andrés Vega, A. Hugo Klahn|2021|Polyhedron|193|114872|doi:10.1016/j.poly.2020.114872
DOI: 10.5517/ccdc.csd.cc25h1vl
title: CCDC 2010469: Experimental Crystal Structure Determination

description: Related Article: Canjia Zhai, Cynthia L. Schreiber, Sasha Padilla‐Coley, Allen G. Oliver, Bradley D. Smith|2020|Angew.Chem.,Int.Ed.|59|23740|doi:10.1002/anie.202009599