cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8204401
title: Photographs of borehole cores from project STIMTEC and STIMTEC-X

description: Photographs of the cores of the various boreholes drilled in Reiche Zeche (Freiberg, Germany) during the projects STIMTEC and STIMTEC-X
DOI: 10.5281/zenodo.8204400
title: Photographs of borehole cores from project STIMTEC and STIMTEC-X

description: Photographs of the cores of the various boreholes drilled in Reiche Zeche (Freiberg, Germany) during the projects STIMTEC and STIMTEC-X
DOI: 10.5281/zenodo.8193616
title: Allliance of Genome Resources Orthology

description: Tab separated formatted spreadsheet of orthology annotations from the Alliance of Genome Resources. The Alliance provides the results of all methods that have been benchmarked by the Quest for Orthologs Consortium (QfO), as well as curated ortholog inferences from HGNC (for human and mouse genes), Xenbase (for frog genes), and ZFIN (relating zebrafish genes to orthologs in human, mouse, and fly). The ortholog inferences from the different methods have been integrated using the DRSC Integrative Ortholog Prediction Tool (DIOPT). DIOPT integrates a number of existing methods including those used by the Alliance: Ensembl Compara, HGNC, Hieranoid, InParanoid, OMA, OrthoFinder, OrthoInspector, PANTHER, PhylomeDB, SonicParanoid, Xenbase, and ZFIN. See the DIOPT documentation for additional information and references related to the included methods. DIOPT assigns a score/count based on the number of methods that call a specific ortholog. For noncoding RNA genes, currently only HGNC and ZFIN curated orthologs are included. File includes orthology relationships among genes from the following organisms: Homo sapiens (human; NCBI:txid 9606) Caenorhabditis elegans (nematode; NCBI:txid 6239) Danio rerio (zebrafish;NCBI:txid 7955) Drosophila melanogaster (fruit fly; NCBI:txid 7227) Mus musculus (mouse; NCBI:txid10090) Rattus norvegicus (rat; NCBI:txid 10116) Saccharomyces cerevisiae (yeast; NCBI:txid 559292) Xenopus laevis (African clawed frog; NCBI:txid 8355) Xenopus tropicalis (Western clawed frog; NCBI:txid 8364)
DOI: 10.5281/zenodo.8193615
title: Allliance of Genome Resources Orthology

description: Tab separated formatted spreadsheet of orthology annotations from the Alliance of Genome Resources. The Alliance provides the results of all methods that have been benchmarked by the Quest for Orthologs Consortium (QfO), as well as curated ortholog inferences from HGNC (for human and mouse genes), Xenbase (for frog genes), and ZFIN (relating zebrafish genes to orthologs in human, mouse, and fly). The ortholog inferences from the different methods have been integrated using the DRSC Integrative Ortholog Prediction Tool (DIOPT). DIOPT integrates a number of existing methods including those used by the Alliance: Ensembl Compara, HGNC, Hieranoid, InParanoid, OMA, OrthoFinder, OrthoInspector, PANTHER, PhylomeDB, SonicParanoid, Xenbase, and ZFIN. See the DIOPT documentation for additional information and references related to the included methods. DIOPT assigns a score/count based on the number of methods that call a specific ortholog. For noncoding RNA genes, currently only HGNC and ZFIN curated orthologs are included. File includes orthology relationships among genes from the following organisms: Homo sapiens (human; NCBI:txid 9606) Caenorhabditis elegans (nematode; NCBI:txid 6239) Danio rerio (zebrafish;NCBI:txid 7955) Drosophila melanogaster (fruit fly; NCBI:txid 7227) Mus musculus (mouse; NCBI:txid10090) Rattus norvegicus (rat; NCBI:txid 10116) Saccharomyces cerevisiae (yeast; NCBI:txid 559292) Xenopus laevis (African clawed frog; NCBI:txid 8355) Xenopus tropicalis (Western clawed frog; NCBI:txid 8364)
DOI: 10.5281/zenodo.8204325
title: A-Data obs. from the Hanford 300 area

description: A dataset was observed in a semi-arid Hanford area of the Washington State (46°24′32″N, 119°16′30″W) by Gao et al. (2017) which was from https://doi.org/10.1002/2017JD027160. The data includes the ground soil heat flux, temperature and land surface meteorological data. The Sensible heat flux and latent heat flux were quality controlled and gap filling by the author referring to Liu et al. (2014).
description: {"references": ["Gao, Z., E. S., Russell, J. E. C., Missik, M., Huang, X., Chen, C. E., Strickland, R., Clayton, E., Arntzen, Y., Ma, and H. Liu. (2017b). A novel approach to evaluate soil heat flux calculation: An analytical review of nine methods [Dataset]. Journal of Geophysical Research-Atmospheres. https://doi.org/10.1002/2017JD027160.", "Liu, H., Feng, J., Sun, J., Wang, L., & Xu, A.(2014). Eddy covariance measurements of water 911 vapor and CO2 fluxes above the Erhai Lake. Science in China (Earth Sciences), 58(3), 317\u2013328. 912. https://doi.org/10.1007/s11430-014-4828-1"]}
DOI: 10.5281/zenodo.8193254
title: Alliance of Genome Resources Gene Expression Data

description: Tab separated formatted spreadsheets of gene expression annotations from the Alliance of Genome Resources. Gene expression data include temporal and/or spatial localization of transcripts and proteins in a wild-type background. File includes annotations for the following organisms: Caenorhabditis elegans (nematode; NCBI:txid 6239) Danio rerio (zebrafish;NCBI:txid 7955) Drosophila melanogaster (fruit fly; NCBI:txid 7227) Mus musculus (mouse; NCBI:txid10090) Rattus norvegicus (rat; NCBI:txid 10116) Saccharomyces cerevisiae (yeast; NCBI:txid 559292) Xenopus laevis (African clawed frog; NCBI:txid 8355) Xenopus tropicalis (Western clawed frog; NCBI:txid 8364)
description: NIH U24HG010859
DOI: 10.5281/zenodo.8193253
title: Alliance of Genome Resources Gene Expression Data

description: Tab separated formatted spreadsheets of gene expression annotations from the Alliance of Genome Resources. Gene expression data include temporal and/or spatial localization of transcripts and proteins in a wild-type background. File includes annotations for the following organisms: Caenorhabditis elegans (nematode; NCBI:txid 6239) Danio rerio (zebrafish;NCBI:txid 7955) Drosophila melanogaster (fruit fly; NCBI:txid 7227) Mus musculus (mouse; NCBI:txid10090) Rattus norvegicus (rat; NCBI:txid 10116) Saccharomyces cerevisiae (yeast; NCBI:txid 559292) Xenopus laevis (African clawed frog; NCBI:txid 8355) Xenopus tropicalis (Western clawed frog; NCBI:txid 8364)
description: NIH U24HG010859
DOI: 10.26050/wdcc/ar6.c6cmcamcc0
title: IPCC DDC: CAMS CAMS_CSM1.0 model output prepared for CMIP6 CMIP

description: Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.CMIP.CAMS.CAMS-CSM1-0' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The CAMS-CSM 1.0 climate model, released in 2016, includes the following components: atmos: ECHAM5_CAMS (T106; 320 x 160 longitude/latitude; 31 levels; top level 10 mb), land: CoLM 1.0, ocean: MOM4 (tripolar; 360 x 200 longitude/latitude, primarily 1deg latitude/longitude, down to 1/3deg within 30deg of the equatorial tropics; 50 levels; top grid cell 0-10 m), seaIce: SIS 1.0. The model was run by the Chinese Academy of Meteorological Sciences, Beijing 100081, China (CAMS) in native nominal resolutions: atmos: 100 km, land: 100 km, ocean: 100 km, seaIce: 100 km.
DOI: 10.26050/wdcc/ar6.c6spcamcc0
title: IPCC DDC: CAMS CAMS-CSM1.0 model output prepared for CMIP6 ScenarioMIP

description: Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.CAMS.CAMS-CSM1-0' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The CAMS-CSM 1.0 climate model, released in 2016, includes the following components: atmos: ECHAM5_CAMS (T106; 320 x 160 longitude/latitude; 31 levels; top level 10 mb), land: CoLM 1.0, ocean: MOM4 (tripolar; 360 x 200 longitude/latitude, primarily 1deg latitude/longitude, down to 1/3deg within 30deg of the equatorial tropics; 50 levels; top grid cell 0-10 m), seaIce: SIS 1.0. The model was run by the Chinese Academy of Meteorological Sciences, Beijing 100081, China (CAMS) in native nominal resolutions: atmos: 100 km, land: 100 km, ocean: 100 km, seaIce: 100 km.
DOI: 10.26050/wdcc/ar6.c6spcamcc0s119
title: IPCC DDC: CAMS CAMS-CSM1.0 model output prepared for CMIP6 ScenarioMIP ssp119

description: Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.CAMS.CAMS-CSM1-0.ssp119' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The CAMS-CSM 1.0 climate model, released in 2016, includes the following components: atmos: ECHAM5_CAMS (T106; 320 x 160 longitude/latitude; 31 levels; top level 10 mb), land: CoLM 1.0, ocean: MOM4 (tripolar; 360 x 200 longitude/latitude, primarily 1deg latitude/longitude, down to 1/3deg within 30deg of the equatorial tropics; 50 levels; top grid cell 0-10 m), seaIce: SIS 1.0. The model was run by the Chinese Academy of Meteorological Sciences, Beijing 100081, China (CAMS) in native nominal resolutions: atmos: 100 km, land: 100 km, ocean: 100 km, seaIce: 100 km.
DOI: 10.5281/zenodo.8204338
title: Telegram digits dataset

description: This dataset is MNIST-like, containing digitized handwritten characters extracted from electoral telegrams during the General Elections of Santa Fe, Argentina, in the year 2021. The dataset offers a valuable resource for researchers and practitioners in the field of character recognition, particularly in the context of electoral data analysis. Each sample in the dataset represents a single digit, ranging from 0 to 9, handwritten by different individuals participating in the electoral process. The dataset aims to facilitate the development and evaluation of machine learning and computer vision algorithms for character recognition tasks. It contains 170718 images, splitted in train (119502), validation (25608) and test (25608). This dataset is part of master's thesis in data science which aims to build an Optical Character Recognition (OCR) system using domain adaptation techniques titled "Classification of digits written in the telegrams of legislative elections in Santa Fe using Domain Adaptation techniques".
DOI: 10.5281/zenodo.8204337
title: Telegram digits dataset

description: This dataset is MNIST-like, containing digitized handwritten characters extracted from electoral telegrams during the General Elections of Santa Fe, Argentina, in the year 2021. The dataset offers a valuable resource for researchers and practitioners in the field of character recognition, particularly in the context of electoral data analysis. Each sample in the dataset represents a single digit, ranging from 0 to 9, handwritten by different individuals participating in the electoral process. The dataset aims to facilitate the development and evaluation of machine learning and computer vision algorithms for character recognition tasks. It contains 170718 images, splitted in train (119502), validation (25608) and test (25608). This dataset is part of master's thesis in data science which aims to build an Optical Character Recognition (OCR) system using domain adaptation techniques titled "Classification of digits written in the telegrams of legislative elections in Santa Fe using Domain Adaptation techniques".
DOI: 10.5281/zenodo.8193238
title: Alliance of Genome Resources Sequence Variants

description: Variant Call Format (VCF) formatted spreadsheets of sequence variants associated with phenotypic alleles from the Alliance of Genome Resources. Variants are in Human Genome Variation Society (HGVS) nomenclature syntax. Files include variants in VCF format for the following organisms: Caenorhabditis elegans (nematode; NCBI:txid 6239) Danio rerio (zebrafish;NCBI:txid 7955) Drosophila melanogaster (fruit fly; NCBI:txid 7227) Mus musculus (mouse; NCBI:txid10090) Rattus norvegicus (rat; NCBI:txid 10116)
description: NIH U24HG010859
DOI: 10.5281/zenodo.8193237
title: Alliance of Genome Resources Sequence Variants

description: Variant Call Format (VCF) formatted spreadsheets of sequence variants associated with phenotypic alleles from the Alliance of Genome Resources. Variants are in Human Genome Variation Society (HGVS) nomenclature syntax. Files include variants in VCF format for the following organisms: Caenorhabditis elegans (nematode; NCBI:txid 6239) Danio rerio (zebrafish;NCBI:txid 7955) Drosophila melanogaster (fruit fly; NCBI:txid 7227) Mus musculus (mouse; NCBI:txid10090) Rattus norvegicus (rat; NCBI:txid 10116)
description: NIH U24HG010859
DOI: 10.5281/zenodo.8193172
title: Alliance of Genome Resources Disease Annotations

description: Tab separated formatted spreadsheets of disease annotations from the Alliance of Genome Resources. Annotations are to terms in the Disease Ontology (DO), evidence codes from the Evidence Code Ontology (ECO). PubMed ids (PMID) are provided for the source of the annotation assertions. File includes annotations for the following organisms: Homo sapiens (human; NCBI:txid 9606) Caenorhabditis elegans (nematode; NCBI:txid 6239) Danio rerio (zebrafish;NCBI:txid 7955) Drosophila melanogaster (fruit fly; NCBI:txid 7227) Mus musculus (mouse; NCBI:txid10090) Rattus norvegicus (rat; NCBI:txid 10116) Saccharomyces cerevisiae (yeast; NCBI:txid 559292) Xenopus laevis (African clawed frog; NCBI:txid 8355) Xenopus tropicalis (Western clawed frog; NCBI:txid 8364)
description: NIH U24HG010859
DOI: 10.5281/zenodo.8193171
title: Alliance of Genome Resources Disease Annotations

description: Tab separated formatted spreadsheets of disease annotations from the Alliance of Genome Resources. Annotations are to terms in the Disease Ontology (DO), evidence codes from the Evidence Code Ontology (ECO). PubMed ids (PMID) are provided for the source of the annotation assertions. File includes annotations for the following organisms: Homo sapiens (human; NCBI:txid 9606) Caenorhabditis elegans (nematode; NCBI:txid 6239) Danio rerio (zebrafish;NCBI:txid 7955) Drosophila melanogaster (fruit fly; NCBI:txid 7227) Mus musculus (mouse; NCBI:txid10090) Rattus norvegicus (rat; NCBI:txid 10116) Saccharomyces cerevisiae (yeast; NCBI:txid 559292) Xenopus laevis (African clawed frog; NCBI:txid 8355) Xenopus tropicalis (Western clawed frog; NCBI:txid 8364)
description: NIH U24HG010859
DOI: 10.17632/s8ktckvspv.3
title: Mouse embryonic stem cells embody organismal level cold resistance. Suita K et al.

description: Raw data and codes in the following study. The commons folder including the shared codes used throughout the analysis is included in the Figure1.zip. 

During hibernation, some mammals show low body temperatures (< 10 °C). Tissues from such hibernators exhibit cold resistance even when the animal is not hibernating. Mice can also enter hypothermic fasting-induced torpor (FIT), but the cold resistance of FIT has never been related to their tissues. Here, we show that an inbred mouse STM2 exhibits lower body temperature during FIT than C57BL/6J or MYS/Mz. Thus, STM2 resists the cold more than other strains. A strain-specific mouse embryonic stem (ES) cell proved that STM2 ES cells are more cold-resistant than others. Pharmacological intervention and transcriptome analysis suggest that STM2 ES cells rely on the oxidative phosphorylation (OXPHOS) pathway but respire independently of the electron transfer chain complex I in the cold. We also found that the liver of STM2 uses OXPHOS more in cold than other strains. This study demonstrates that an organismal phenotype of torpor can be studied in an in vitro setup using mouse cells.

Figure 1 | STM2 shows lower body temperature at fasting-induced torpor.
Figure 2 | ES cell–establishment from mouse inbred strains.
Figure 3 | The metabolic pathway preference of STM2 ES cells is unchanged under low temperatures.
Figure 4 | STM2 ES cells own a temperature-insensitive complex I–independent respiration.
Figure 5 | Transcriptome analysis of cold-cultured ES cells reveals STM2 has suppressed expression of the CI–related genes.
Figure 6 | STM2 tissues show cold-resistance.
DOI: 10.5258/soton/d2058
title: Dataset for the 'Multicentre survey questionnaire manuscript'

description: Dataset supports the PPE adverse skin reaction article: Abiakam, Worsley et al 2021, International Wound Journal. doi:10.1111/iwj.13534 The data contains answers to survey questionnaires of 307 clinical professionals using PPE during the first peak of the COVID-19 pandemic in UK
DOI: 10.6084/m9.figshare.22117673
title: Within-species variation in the gut microbiome of medaka (<i>Oryzias latipes</i>) is driven by the interaction of light intensity and genetic background

description: Data (ASVs and fish traits) and R codes
DOI: 10.6084/m9.figshare.22117673.v2
title: Within-species variation in the gut microbiome of medaka (<i>Oryzias latipes</i>) is driven by the interaction of light intensity and genetic background

description: Data (ASVs and fish traits) and R codes
DOI: 10.6084/m9.figshare.22117673.v1
title: Within-species variation in the gut microbiome of medaka (<i>Oryzias latipes</i>) is driven by the interaction of light intensity and genetic background

description: Data (ASVs and fish traits) and R codes
DOI: 10.5281/zenodo.8026817
title: Fluxes of the protonated masses from the soil samples collected from two temperate ecosystems detected by PTR-ToF-MS

description: Volatile organic compounds (VOCs) are reactive gaseous compounds with significant impacts on air quality and the Earth's radiative balance. While natural ecosystems are known to be major sources of VOCs, primarily due to vegetation, soils, an important component of these ecosystems, have received relatively less attention as potential sources and sinks of VOCs. In this study, soil samples were collected from two temperate ecosystems: a beech forest and a heather heath, and then sieved, homogenized, and incubated under various controlled conditions such as different temperatures, oxic <em>vs</em>. anoxic conditions, and different ambient VOC levels. A dynamic flow-through system coupled to a proton transfer reaction-time of flight-mass spectrometry (PTR-ToF-MS) was used to measure production and/or uptake rates of selected VOCs, aiming to explore the processes and their controlling mechanisms. This dataset therefore is collected from these experiments. It includes the raw flux data and figure source data associated with a peer-reviewed publication in Soil Biology & Biochemistry at https://doi.org/10.1016/j.soilbio.2023.109153. Overall, our results showed that these soils were natural sources of a variety of VOCs, and the strength and profile of these emissions were influenced by soil biogeochemical properties (e.g. moisture, soil organic matter), oxic/anoxic conditions, and temperature. The soils also acted as sinks for most VOCs when VOC substrates at parts per billions levels (ranging between 0.18-68.65 ppb) were supplied to the headspace of the enclosed soils, and the size of the sink corresponded to the amount of VOCs available in the ambient air. Temperature-controlled incubations and glass bead simulations indicated that the uptake of VOCs by soils was likely driven by microbial metabolism, with a minor contribution from physical adsorption to soil particles. In conclusion, our study suggests that soil uptake of VOCs can mitigate the impact of other significant VOC sources in the near-surface environment and potentially regulate the net exchange of these trace gases in ecosystems. Should you have any questions regarding the dataset, please free feel to contact Yi jiao at yi.jiao@bio.ku.dk or Prof. Rinnan at riikkar@bio.ku.dk