cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2d5qdq
title: CCDC 2210033: Experimental Crystal Structure Determination

description: Related Article: Wenqiang Ma, Zhen-Qi Zhang, Qing Ma, Jie Tang, Wei Yang, Hongwei Yang, Guangbin Cheng, Gui-Juan Fan|2023|Cryst.Growth Des.|23|1127|doi:10.1021/acs.cgd.2c01268
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2d5qfr
title: CCDC 2210034: Experimental Crystal Structure Determination

description: Related Article: Wenqiang Ma, Zhen-Qi Zhang, Qing Ma, Jie Tang, Wei Yang, Hongwei Yang, Guangbin Cheng, Gui-Juan Fan|2023|Cryst.Growth Des.|23|1127|doi:10.1021/acs.cgd.2c01268
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.6084/m9.figshare.21647123.v2
title: Controls on stable oxygen isotopes in monsoonal precipitation across the Bay of Bengal: atmosphere and surface analysis

description: Modeled stable isotope data of precipitation and water vapor from IsoGSM2 are used in the paper titled "Controls on stable oxygen isotopes in monsoonal precipitation across the Bay of Bengal: atmosphere and surface analysis"
DOI: 10.5281/zenodo.7618989
title: Sprenger et al. (2017) cyclone data

description: These data were generated by Sprenger et al., as detailed in their paper: Sprenger, M., Fragkoulidis, G., Binder, H., Croci-Maspoli, M., Graf, P., Grams, C. M., Knippertz, P., Madonna, E., Schemm, S., Škerlak, B., & Wernli, H. (2017). Global Climatologies of Eulerian and Lagrangian Flow Features based on ERA-Interim, Bulletin of the American Meteorological Society, 98(8), 1739-1748. https://doi.org/10.1175/BAMS-D-15-00299.1 , and are being uploaded with their permission to comply with Geophysical Research Letter's FAIR data policy for a separate paper, submitted by Linh Vu and Robin Clancy (uploader). Fields included are: date latitude, longitude, thetamin (minumum potential temperature), amplitude, circulation. Radius data are not present in this file, and so only a fill value is present. Individual cyclones are split by a line that indicates how many 6-hourly records follow for that cyclone.
description: {"references": ["Sprenger, M., Fragkoulidis, G., Binder, H., Croci-Maspoli, M., Graf, P., Grams, C. M., Knippertz, P., Madonna, E., Schemm, S., \u0160kerlak, B., & Wernli, H. (2017). Global Climatologies of Eulerian and Lagrangian Flow Features based on ERA-Interim, Bulletin of the American Meteorological Society, 98(8), 1739-1748."]}
DOI: 10.5281/zenodo.7618988
title: Sprenger et al. (2017) cyclone data

description: These data were generated by Sprenger et al., as detailed in their paper: Sprenger, M., Fragkoulidis, G., Binder, H., Croci-Maspoli, M., Graf, P., Grams, C. M., Knippertz, P., Madonna, E., Schemm, S., Škerlak, B., & Wernli, H. (2017). Global Climatologies of Eulerian and Lagrangian Flow Features based on ERA-Interim, Bulletin of the American Meteorological Society, 98(8), 1739-1748. https://doi.org/10.1175/BAMS-D-15-00299.1 , and are being uploaded with their permission to comply with Geophysical Research Letter's FAIR data policy for a separate paper, submitted by Linh Vu and Robin Clancy (uploader). Fields included are: date latitude, longitude, thetamin (minumum potential temperature), amplitude, circulation. Radius data are not present in this file, and so only a fill value is present. Individual cyclones are split by a line that indicates how many 6-hourly records follow for that cyclone.
description: {"references": ["Sprenger, M., Fragkoulidis, G., Binder, H., Croci-Maspoli, M., Graf, P., Grams, C. M., Knippertz, P., Madonna, E., Schemm, S., \u0160kerlak, B., & Wernli, H. (2017). Global Climatologies of Eulerian and Lagrangian Flow Features based on ERA-Interim, Bulletin of the American Meteorological Society, 98(8), 1739-1748."]}
DOI: 10.6084/m9.figshare.22046813
title: PVCTS2020v2_part.tif

description: completed area for PVCTS2020 map (competed but not yet fully verified. Full map will be released following comprehensive accuracy assessment). Note: unlike other products in series, this product only goes through 2020 (not 2021). Classification key and methods are otherwise the same as previous maps in the series and can be found in accompanying documentation.
DOI: 10.25403/upresearchdata.21940088
title: Thematic analysis of social media's influence on gay men's desire for muscularity

description: Datasets for this study were collected using semi-structured interviews.The study granted the researcher an opportunity to elicit the experiences of gay men regarding the phenomenon being studied. Moreover, by conducting semi-structured interviews, the researcher was able to further explore each participant’s opinions and perceptions in rich detail. The researcher chose to use semi-structured interviews to focus the conversation and solicit the views of the participants that relate to the research question. Thematic analysis was used to interpret the data. The assumption in this study is that the desire for muscularity is collectively created within society, thus there likely exists numerous and varied perceptions and meanings that gay men in South Africa subscribe to in relation to their use of social media and the influence this may have on their desire for muscularity. Recognizing general themes and exploring and dissecting them created space for a thorough discovery of how gay men in South Africa understand their desire for muscularity and how this is influenced by their use of social media.
DOI: 10.5281/zenodo.4052518
title: Dataset for "Attribution of the accelerating increase in atmospheric methane during 2010–2018 by inverse analysis of GOSAT observations"

description: The dataset includes results from a global inversion of GOSAT methane observations documented in Zhang et al. (2021). The inversion is performed with the GEOS-Chem model for the period of 2010-2018. We optimize mean and trend of non-wetland emissions, monthly wetland emissions for 14 subcontinental regions, and annual hemispheric OH concentrations (methane loss rates). Sector attribution of posterior non-wetland emissions in a 4<sup>o</sup>x5<sup>o </sup>grid cell is done on the basis of the relative contribution of a sector to prior emissions in the grid cell. Detailed description of the methodology can be found in Zhang et al. (2021). <strong>Reference</strong> Zhang et al., <strong>Attribution of the accelerating increase in atmospheric methane during 2010–2018 by inverse analysis of GOSAT observations</strong>, <em>Atmos. Chem. Phys.</em>, 21, 3643–3666, https://doi.org/10.5194/acp-21-3643-2021, 2021. <strong>Date files</strong> The dataset includes 4 files: 1. <strong>GlobalInv_v1.nc</strong> is the main data file, which includes (1) prior estimates (prior_value), (2) posterior scaling factor (post_sf), (3) prior error covariance matrix (prior_errcov), (4) posterior error covariance matrix (post_errcov), and (5) averaging kernel matrix (post_ak) for the state vector. All the values are expressed relative to prior_values, except for prior_value which is expressed in absolute magnitude. 2. <strong>State_vec.csv</strong> is a table which indexes the state vector elements, for non-wetland emission (PertA) (1-1009), non-wetland emission trends (PertT) (1010-2018), wetland emissions (PertW) (2019-3530), and hemispheric OH (PertN for Northern Hemisphere and PertS for Southern Hemisphere) (3531-3548). 3. <strong>Clusters_nonwetland.nc</strong> contains a 72x46 matrix (corresponding to GEOS-Chem 4x5 degree grid) that maps grid cells to non-wetland emission grid-cell ID. 4. <strong>Clusters_wetland.nc</strong> contains a 72x46 matrix (corresponding to GEOS-Chem 4x5 degree grid) that maps grid cells to wetland emission region ID. <strong>Derived dataset </strong> We also derive a <strong>sectoral monthly gridded methane flux</strong> dataset based on the inversion results archived here. This gridded flux dataset is deposited in a separate repository (https://doi.org/10.57760/sciencedb.02328) (in Aug 2022). The gridded dataset represents the best estimate from this inversion but includes no information about error metrics (e.g., posterior error covariance).
DOI: 10.5281/zenodo.4052517
title: Dataset for "Attribution of the accelerating increase in atmospheric methane during 2010–2018 by inverse analysis of GOSAT observations"

description: The dataset includes results from a global inversion of GOSAT methane observations documented in Zhang et al. (2021). The inversion is performed with the GEOS-Chem model for the period of 2010-2018. We optimize mean and trend of non-wetland emissions, monthly wetland emissions for 14 subcontinental regions, and annual hemispheric OH concentrations (methane loss rates). Sector attribution of posterior non-wetland emissions in a 4<sup>o</sup>x5<sup>o </sup>grid cell is done on the basis of the relative contribution of a sector to prior emissions in the grid cell. Detailed description of the methodology can be found in Zhang et al. (2021). <strong>Reference</strong> Zhang et al., <strong>Attribution of the accelerating increase in atmospheric methane during 2010–2018 by inverse analysis of GOSAT observations</strong>, <em>Atmos. Chem. Phys.</em>, 21, 3643–3666, https://doi.org/10.5194/acp-21-3643-2021, 2021. <strong>Date files</strong> The dataset includes 4 files: 1. <strong>GlobalInv_v1.nc</strong> is the main data file, which includes (1) prior estimates (prior_value), (2) posterior scaling factor (post_sf), (3) prior error covariance matrix (prior_errcov), (4) posterior error covariance matrix (post_errcov), and (5) averaging kernel matrix (post_ak) for the state vector. All the values are expressed relative to prior_values, except for prior_value which is expressed in absolute magnitude. 2. <strong>State_vec.csv</strong> is a table which indexes the state vector elements, for non-wetland emission (PertA) (1-1009), non-wetland emission trends (PertT) (1010-2018), wetland emissions (PertW) (2019-3530), and hemispheric OH (PertN for Northern Hemisphere and PertS for Southern Hemisphere) (3531-3548). 3. <strong>Clusters_nonwetland.nc</strong> contains a 72x46 matrix (corresponding to GEOS-Chem 4x5 degree grid) that maps grid cells to non-wetland emission grid-cell ID. 4. <strong>Clusters_wetland.nc</strong> contains a 72x46 matrix (corresponding to GEOS-Chem 4x5 degree grid) that maps grid cells to wetland emission region ID. <strong>Derived dataset </strong> We also derive a <strong>sectoral monthly gridded methane flux</strong> dataset based on the inversion results archived here. This gridded flux dataset is deposited in a separate repository (https://doi.org/10.57760/sciencedb.02328) (in Aug 2022). The gridded dataset represents the best estimate from this inversion but includes no information about error metrics (e.g., posterior error covariance).
DOI: 10.34740/kaggle/dsv/4964227
title: Health Workforce Shortage Areas
DOI: 10.34740/kaggle/dsv/4964239
title: AutoXGB
DOI: 10.57745/vv0ugn
title: data_fig1.tab

description: Ions mass spectra detected for the three repeated experiments of irradiation of a N2-CH4 95-5% gas mixture with photons at 73.6 nm and a reactant partial pressure of 32 Pa.
DOI: 10.57745/ptklxg
title: MS_13C.tab

description: Mass spectrum obtained using methane isotopically labelled with 13 C (Figure 3)
DOI: 10.57745/wumyja
title: MS_15N.tab

description: Mass spectrum obtained with nitrogen isotopically labelled (Figure 3)
DOI: 10.57745/zgvree
title: simu_fig2.tab

description: Mass spectrum generated numerically with the opensource code ReactorUI (Github)
DOI: 10.57745/vt9hrp
title: Readme.txt

description: :unav
DOI: 10.5281/zenodo.7593734
title: WorldCereal open global harmonized reference data repository (CC-BY licensed data sets)

description: Within the <strong>ESA funded </strong>WorldCereal project we have built an open harmonized reference data repository at global extent for model training or product validation in support of land cover and crop type mapping. Data from 2017 onwards were collected from many different sources and then harmonized, annotated and evaluated. These steps are explained in the harmonization protocol (10.5281/zenodo.7584463). This protocol also clarifies the naming convention of the shape files and the WorldCereal attributes (LC, CT, IRR, valtime and sampleID) that were added to the original data sets. This publication includes those harmonized data sets of which the original data set was published under the CC-BY license or a license similar to CC-BY. See document "_In-situ-data-World-Cereal - license - CC-BY.pdf" for an overview of the original data sets.
DOI: 10.5281/zenodo.7593733
title: WorldCereal open global harmonized reference data repository (CC-BY licensed data sets)

description: Within the <strong>ESA funded </strong>WorldCereal project we have built an open harmonized reference data repository at global extent for model training or product validation in support of land cover and crop type mapping. Data from 2017 onwards were collected from many different sources and then harmonized, annotated and evaluated. These steps are explained in the harmonization protocol (10.5281/zenodo.7584463). This protocol also clarifies the naming convention of the shape files and the WorldCereal attributes (LC, CT, IRR, valtime and sampleID) that were added to the original data sets. This publication includes those harmonized data sets of which the original data set was published under the CC-BY license or a license similar to CC-BY. See document "_In-situ-data-World-Cereal - license - CC-BY.pdf" for an overview of the original data sets.
DOI: 10.6084/m9.figshare.20167427
title: Increased reliance on coronary perfusion for cardiorespiratory performance in seawater-acclimated rainbow trout

description: Dataset, metadata, figures and table from a study on the influence of coronary perfusion to the heart for cardiorespiratory performance in rainbow trout acclimated to fresh- or seawater.
DOI: 10.6084/m9.figshare.20167427.v1
title: Increased reliance on coronary perfusion for cardiorespiratory performance in seawater-acclimated rainbow trout

description: Dataset, metadata, figures and table from a study on the influence of coronary perfusion to the heart for cardiorespiratory performance in rainbow trout acclimated to fresh- or seawater.
DOI: 10.15151/esrf-es-1018304561
title: The Iberian cryoEM community BAG

description: The Iberian cryoEM BAG is a large consortium (&gt; 30 groups) composed of Portuguese and Spanish groups working in biologically relevant problems including disease related metabolic enzymes, antibiotic resistance, DNA replication and DNA maintenance, protein synthesis, virus structure and mechanisms, and macromolecular complexes involved in the control of key cellular processes like protein folding and degradation.
DOI: 10.34740/kaggle/dsv/4964275
title: Elon Musk Tweets
DOI: 10.17632/h7jxb8frm2.1
title: Avocado root and soil microbiome

description: The project examined the changes to the soil microbiome of Persea americana (avocado) treated with organic mulch, a silicate-based mulch or phosphite, and infected with Phytophthora cinnamomi. 

Firstly, we hypothesised that the abundance and diversity of the microbiome would increase in response to the addition of organic mulch, and there would be further changes with the addition of mineral mulch or spraying plants with phosphite. Secondly, the presence of P. cinnamomi would change the microbial profiles of the soils in similar ways regardless of the soil additives. Thirdly, the abundance and diversity of the microbiome would be greatest in treatments that suppressed Phytophthora root damage. Fourthly, since avocado root damage from Phytophthora is reduced to a similar extent by the silicate-based mineral mulch or by spraying plants with phosphite, both treatments induce similar changes in the soil microbiome. 
 

The data provided here is the metabarcoding data in excel format.  There are six data sheets
1.  OTUs per sample with a description of the identity of each sample in the first 3 rows.
2. sequences of each OTU
3. taxonomic ID of each OTU
4. a combined sheet with all information (1-2)
5. sheet 4 with the plastids removed (chloroplasts and mitochondria)
6. selected OTU's; those contributing at least 40 reads.  This was the datset that was analysed.
DOI: 10.5281/zenodo.7609500
title: WorldCereal open global harmonized reference data repository (CC-BY-NC licensed data sets)

description: Within the <strong>ESA funded</strong> WorldCereal project we have built an open harmonized reference data repository at global extent for model training or product validation in support of land cover and crop type mapping. Data from 2017 onwards were collected from many different sources and then harmonized, annotated and evaluated. These steps are explained in the harmonization protocol (10.5281/zenodo.7584463). This protocol also clarifies the naming convention of the shape files and the WorldCereal attributes (LC, CT, IRR, valtime and sampleID) that were added to the original data sets. This publication includes those harmonized data sets of which the original data set was published under the CC-BY-NC license or a license similar to CC-BY-NC. See document "_In-situ-data-World-Cereal - license - CC-BY-NC.pdf" for an overview of the original data sets. Currently this publication only includes a few small data sets for Tanzania originating from a disease monitoring program of the International Maize and Wheat Improvement Center (CIMMYT). CIMMYT made more data available for countries like Kenya, Ethiopia, Rwanda and, Malawi. However due project contraints these data sets were not yet harmonized.
DOI: 10.5281/zenodo.7609499
title: WorldCereal open global harmonized reference data repository (CC-BY-NC licensed data sets)

description: Within the <strong>ESA funded</strong> WorldCereal project we have built an open harmonized reference data repository at global extent for model training or product validation in support of land cover and crop type mapping. Data from 2017 onwards were collected from many different sources and then harmonized, annotated and evaluated. These steps are explained in the harmonization protocol (10.5281/zenodo.7584463). This protocol also clarifies the naming convention of the shape files and the WorldCereal attributes (LC, CT, IRR, valtime and sampleID) that were added to the original data sets. This publication includes those harmonized data sets of which the original data set was published under the CC-BY-NC license or a license similar to CC-BY-NC. See document "_In-situ-data-World-Cereal - license - CC-BY-NC.pdf" for an overview of the original data sets. Currently this publication only includes a few small data sets for Tanzania originating from a disease monitoring program of the International Maize and Wheat Improvement Center (CIMMYT). CIMMYT made more data available for countries like Kenya, Ethiopia, Rwanda and, Malawi. However due project contraints these data sets were not yet harmonized.
DOI: 10.5281/zenodo.6797328
title: HPRC PanGenie results

description: <strong>HPRC PanGenie results</strong> Input VCF used for PanGenie for the HPRC experiments as well as genotyping results, statistics and filters computed. All experiments are based on the Minigraph-Cactus (MC) graph. Experiments were run at Heinrich-Heine University Düsseldorf by Jana Ebler (ebler@hhu.de). Pipelines used to produce these results are here: http://bitbucket.org/jana_ebler/hprc-experiments/src/master/genotyping-experiments/ <strong>How to run PanGenie on the MC variants</strong> We ran PanGenie using the file "cactus_filtered_ids.vcf.gz" as input (contained in this repository). It was produced from the file "hprc-v1.0-mc-grch38.vcf.gz" generated from the MC graph using vg decompose. The output VCF generated by PanGenie can be converted into a bi-allelic VCF containing a single record for each (nested) variant allele, i.e. after decomposing large bubbles into their nested variants using the script: https://bitbucket.org/jana_ebler/hprc-experiments/src/master/genotyping-experiments/workflow/scripts/convert-to-biallelic.py <pre><code># run PanGenie, produces genotyped VCF "pangenie_genotyping.vcf" PanGenie -i &lt;input-reads&gt; -v cactus_filtered_ids.vcf -r &lt;reference-genome&gt; -o pangenie -j 24 -t 24 # decompose bubbles and produce a bi-allelic VCF with genotypes for each (nested) allele cat pangenie_genotyping.vcf | python3 convert-to-biallelic.py cactus_filtered_ids_biallelic.vcf &gt; pangenie_genotyping_biallelic.vcf</code></pre>
DOI: 10.5281/zenodo.6307416
title: HPRC PanGenie results

description: <strong>HPRC PanGenie results</strong> Input VCF used for PanGenie for the HPRC experiments as well as genotyping results, statistics and filters computed. All experiments are based on the Minigraph-Cactus (MC) graph. Experiments were run at Heinrich-Heine University Düsseldorf by Jana Ebler (ebler@hhu.de). Pipelines used to produce these results are here: http://bitbucket.org/jana_ebler/hprc-experiments/src/master/genotyping-experiments/ <strong>How to run PanGenie on the MC variants</strong> We ran PanGenie using the file "cactus_filtered_ids.vcf.gz" as input (contained in this repository). It was produced from the file "hprc-v1.0-mc-grch38.vcf.gz" generated from the MC graph using vg decompose. The output VCF generated by PanGenie can be converted into a bi-allelic VCF containing a single record for each (nested) variant allele, i.e. after decomposing large bubbles into their nested variants using the script: https://bitbucket.org/jana_ebler/hprc-experiments/src/master/genotyping-experiments/workflow/scripts/convert-to-biallelic.py <pre><code># run PanGenie, produces genotyped VCF "pangenie_genotyping.vcf" PanGenie -i &lt;input-reads&gt; -v cactus_filtered_ids.vcf -r &lt;reference-genome&gt; -o pangenie -j 24 -t 24 # decompose bubbles and produce a bi-allelic VCF with genotypes for each (nested) allele cat pangenie_genotyping.vcf | python3 convert-to-biallelic.py cactus_filtered_ids_biallelic.vcf &gt; pangenie_genotyping_biallelic.vcf</code></pre>