89,635 Works

High-spatial-resolution monthly temperatures dataset over China during 1901–2017

Shouzhang Peng
The dataset with 0.5 arcminute (~1 km) was spatially downscaled from CRU TS v4.02 based on Delta downscaling method, including monthly minimum, maximum, and mean temperatures from 1901.1 to 2017.12. The dataset covers the main land area of China. The dataset was evaluated by 745 national weather stations across China, and the evaluation indicated that the downscaled dataset is reliable for the investigations related to climate change across China.
This data repository is not currently reporting usage information. For information on how your repository can submit usage information, please see our documentation.

High-spatial-resolution monthly temperatures dataset over China during 1901–2017

Shouzhang Peng
The dataset with 0.5 arcminute (~1 km) was spatially downscaled from CRU TS v4.02 based on Delta downscaling method, including monthly minimum, maximum, and mean temperatures from 1901.1 to 2017.12. The dataset covers the main land area of China. The dataset was evaluated by 745 national weather stations across China, and the evaluation indicated that the downscaled dataset is reliable for the investigations related to climate change across China.
This data repository is not currently reporting usage information. For information on how your repository can submit usage information, please see our documentation.

ribosomeprofiling/ribo_manuscript_supplemental: Supplementary material for the Ribo ecosystem manuscript

Hakan Ozadam, Michael Geng & Can Cenik
Supplementary material for the Manuscript RiboFlow, RiboR and RiboPy: An ecosystem for analyzing ribosome profiling data at read length resolution
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ribosomeprofiling/ribo_manuscript_supplemental: Supplementary material for the Ribo ecosystem manuscript

Hakan Ozadam, Michael Geng & Can Cenik
Supplementary material for the Manuscript RiboFlow, RiboR and RiboPy: An ecosystem for analyzing ribosome profiling data at read length resolution
This data repository is not currently reporting usage information. For information on how your repository can submit usage information, please see our documentation.

High-spatial-resolution monthly precipitation dataset over China during 1901–2017

Shouzhang Peng
The dataset with 0.5 arcminute (~1 km) was spatially downscaled from CRU TS v4.02 based on Delta downscaling method, including monthly precipitation from 1901.1 to 2017.12. The dataset covers the main land area of China. The dataset was evaluated by 745 national weather stations across China, and the evaluation indicated that the downscaled dataset is reliable for the investigations related to climate change across China.
This data repository is not currently reporting usage information. For information on how your repository can submit usage information, please see our documentation.

High-spatial-resolution monthly precipitation dataset over China during 1901–2017

Shouzhang Peng
The dataset with 0.5 arcminute (~1 km) was spatially downscaled from CRU TS v4.02 based on Delta downscaling method, including monthly precipitation from 1901.1 to 2017.12. The dataset covers the main land area of China. The dataset was evaluated by 745 national weather stations across China, and the evaluation indicated that the downscaled dataset is reliable for the investigations related to climate change across China.
This data repository is not currently reporting usage information. For information on how your repository can submit usage information, please see our documentation.

Obfuscated code2vec - Java Datasets

Anonymous
These are a collection of Java class classification datasets (i.e., classify a class into one of a set of categories). These are shared for further research in static code analysis tasks (malware classification, author attribution, etc).
This data repository is not currently reporting usage information. For information on how your repository can submit usage information, please see our documentation.

Obfuscated code2vec - Java Datasets

Anonymous
These are a collection of Java class classification datasets (i.e., classify a class into one of a set of categories). These are shared for further research in static code analysis tasks (malware classification, author attribution, etc).
This data repository is not currently reporting usage information. For information on how your repository can submit usage information, please see our documentation.

Quantitative Trait Loci of Solanaceae species

Arnold Kuzniar & Gurnoor Singh
This archive contains experimental data on Quantitative Trait Loci (QTLs) mapped in Solanacea species (tomato and potato). QTLs were extracted from scientific literature using the QTLTableMiner++ tool. The resulting data are distributed in: SQLite database files (.db) CSV files (.csv) RDF/Turle files (gzip-ed .ttl)
This data repository is not currently reporting usage information. For information on how your repository can submit usage information, please see our documentation.

Quantitative Trait Loci of Solanaceae species

Arnold Kuzniar & Gurnoor Singh
This archive contains experimental data on Quantitative Trait Loci (QTLs) mapped in Solanacea species (tomato and potato). QTLs were extracted from scientific literature using the QTLTableMiner++ tool. The resulting data are distributed in: SQLite database files (.db) CSV files (.csv) RDF/Turle files (gzip-ed .ttl)
This data repository is not currently reporting usage information. For information on how your repository can submit usage information, please see our documentation.

Test data for sv-callers workflow

Arnold Kuzniar & Luca Santuari
This distribution includes data analyzed by the sv-callers workflow (v1.1.0) in the single-sample (germline) and paired-sample (somatic) modes: GRCh37 and b37 human reference genomes (in .fasta) excluded genomic regions (in .bed(pe)) ENCODE:ENCFF001TDO Layer et al. (2014) structural variants (SVs) detected by the workflow (in .vcf) SV truth sets (in .bed(pe)) Personalis/1000 Genomes Project data by Parikh et al. (2016) PacBio/Moleculo data by Layer et al. (2014) workflow samples (in .csv) and config files (in .yaml)...
This data repository is not currently reporting usage information. For information on how your repository can submit usage information, please see our documentation.

Test data for sv-callers workflow

Arnold Kuzniar & Luca Santuari
This distribution includes data analyzed by the sv-callers workflow (v1.1.0) in the single-sample (germline) and paired-sample (somatic) modes: GRCh37 and b37 human reference genomes (in .fasta) excluded genomic regions (in .bed(pe)) ENCODE:ENCFF001TDO Layer et al. (2014) structural variants (SVs) detected by the workflow (in .vcf) SV truth sets (in .bed(pe)) Personalis/1000 Genomes Project data by Parikh et al. (2016) PacBio/Moleculo data by Layer et al. (2014) workflow samples (in .csv) and config files (in .yaml)...
This data repository is not currently reporting usage information. For information on how your repository can submit usage information, please see our documentation.

Genome annotations of Solanaceae species

Arnold Kuzniar
This archive contains genome annotations of Solanacea species (i.e., S. lycopersicum, S. pennellii and S. tuberosum). The annotation files include gene modes and genetic markers from the Sol Genomics Network (SGN) resource that were converted to semantically interoperable format using the SIGA.py command-line tool. The data are (re)distributed in: Generic Feature Format files (.gff) SQLite database files (.db) RDF/Turle files (gzip-ed .ttl)
This data repository is not currently reporting usage information. For information on how your repository can submit usage information, please see our documentation.

Genome annotations of Solanaceae species

Arnold Kuzniar
This archive contains genome annotations of Solanacea species (i.e., S. lycopersicum, S. pennellii and S. tuberosum). The annotation files include gene modes and genetic markers from the Sol Genomics Network (SGN) resource that were converted to semantically interoperable format using the SIGA.py command-line tool. The data are (re)distributed in: Generic Feature Format files (.gff) SQLite database files (.db) RDF/Turle files (gzip-ed .ttl)
This data repository is not currently reporting usage information. For information on how your repository can submit usage information, please see our documentation.

BIDS Data for "A Whole-Brain Map and Assay Parameter Analysis of Mouse VTA Dopaminergic Activation"

Horea-Ioan Ioanas, Bechara John Saab & Markus Rudin
Base data package for the “"A Whole-Brain Map and Assay Parameter Analysis of Mouse VTA Dopaminergic Activation” article, formatted corresponding to the Brain Imaging Data Structure.
This data repository is not currently reporting usage information. For information on how your repository can submit usage information, please see our documentation.

BIDS Data for "A Whole-Brain Map and Assay Parameter Analysis of Mouse VTA Dopaminergic Activation"

Horea-Ioan Ioanas, Bechara John Saab & Markus Rudin
Base data package for the “"A Whole-Brain Map and Assay Parameter Analysis of Mouse VTA Dopaminergic Activation” article, formatted corresponding to the Brain Imaging Data Structure.
This data repository is not currently reporting usage information. For information on how your repository can submit usage information, please see our documentation.

Ashmolean Latin Inscriptions Project (AshLI) EpiDoc files

Alison Cooley
The EpiDoc files of the Ashmolean Latin Inscriptions Project (AshLI). https://warwick.ac.uk/fac/arts/classics/research/dept_projects/latininscriptions/ https://latininscriptions.ashmus.ox.ac.uk/
This data repository is not currently reporting usage information. For information on how your repository can submit usage information, please see our documentation.

Ashmolean Latin Inscriptions Project (AshLI) EpiDoc files

Alison Cooley
The EpiDoc files of the Ashmolean Latin Inscriptions Project (AshLI). https://warwick.ac.uk/fac/arts/classics/research/dept_projects/latininscriptions/ https://latininscriptions.ashmus.ox.ac.uk/
This data repository is not currently reporting usage information. For information on how your repository can submit usage information, please see our documentation.

Epigraphic Database Heidelberg EpiDoc files

James M.S. Cowey, Francisca Feraudi-Gruénais, Brigitte Gräf, Frank Grieshaber, Regine Klar & Jonas Osnabrügge
EpiDoc files of Epigraphic Database Heidelberg (EDH: https://edh-www.adw.uni-heidelberg.de). The latest versions of these files can be found on the EDH website https://edh-www.adw.uni-heidelberg.de/data; they are mirrored also to https://github.com/epigraphic-database-heidelberg/data on a daily base. These files can be reused under the CC BY-SA 4.0 licence.
The article "Epigraphic Database Heidelberg – Data Reuse Options" describes the various options of reusing EDH data: https://doi.org/10.11588/heidok.00026599.
This data repository is not currently reporting usage information. For information on how your repository can submit usage information, please see our documentation.

Epigraphic Database Heidelberg EpiDoc files

James M.S. Cowey, Francisca Feraudi-Gruénais, Brigitte Gräf, Frank Grieshaber, Regine Klar & Jonas Osnabrügge
EpiDoc files of Epigraphic Database Heidelberg (EDH: https://edh-www.adw.uni-heidelberg.de). The latest versions of these files can be found on the EDH website https://edh-www.adw.uni-heidelberg.de/data; they are mirrored also to https://github.com/epigraphic-database-heidelberg/data on a daily base. These files can be reused under the CC BY-SA 4.0 licence.
The article "Epigraphic Database Heidelberg – Data Reuse Options" describes the various options of reusing EDH data: https://doi.org/10.11588/heidok.00026599.
This data repository is not currently reporting usage information. For information on how your repository can submit usage information, please see our documentation.

Reliance on Science in Patenting

Matt Marx & Aaron Fuegi
This contains citations from the front pages of worldwide patents to articles in he Microsoft Academic Graph (MAG) from 1800-2018. Questions & feedback to support@relianceonscience.org. If you use the data, please cite these two papers: for the dataset of citations: Marx, Matt and Aaron Fuegi, "Reliance on Science: Worldwide Front-Page Patent Citations to Scientific Articles" (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3331686). for the articles: Sinha, A, et al. 2015. Overview of Microsoft Academic Service (MAS) and Applications. In Proceedings of...
This data repository is not currently reporting usage information. For information on how your repository can submit usage information, please see our documentation.

Reliance on Science in Patenting

Matt Marx & Aaron Fuegi
This contains citations from the front pages of worldwide patents to articles in he Microsoft Academic Graph (MAG) from 1800-2018. Questions & feedback to support@relianceonscience.org. If you use the data, please cite these two papers: for the dataset of citations: Marx, Matt and Aaron Fuegi, "Reliance on Science: Worldwide Front-Page Patent Citations to Scientific Articles" (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3331686). for the articles: Sinha, A, et al. 2015. Overview of Microsoft Academic Service (MAS) and Applications. In Proceedings of...
This data repository is not currently reporting usage information. For information on how your repository can submit usage information, please see our documentation.

IO Islamic 3264. Ma'ârij-alnubuwwah, Biography of the Prophet

Dûst 'Alî Ibn Maulânâ 'Alî Muḥammad
IO Islamic 3264. Ma’ârij-alnubuwwah, Biography of the Prophet
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IO Islamic 3264. Ma'ârij-alnubuwwah, Biography of the Prophet

Dûst 'Alî Ibn Maulânâ 'Alî Muḥammad
IO Islamic 3264. Ma’ârij-alnubuwwah, Biography of the Prophet
This data repository is not currently reporting usage information. For information on how your repository can submit usage information, please see our documentation.

IO Islamic 3266. Ma'ârij-alnubuwwah, Biography of the Prophet

Anon
IO Islamic 3266. Ma’ârij-alnubuwwah, Biography of the Prophet
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Registration Year

  • 2013
    38
  • 2014
    265
  • 2015
    1,425
  • 2016
    1,707
  • 2017
    32,327
  • 2018
    23,924
  • 2019
    29,945

Resource Types

  • Dataset
    89,635