cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.9844349
title: Categorical encoding of decision variables in orbitofrontal cortex

description: Categorical encoding of decision variables in orbitofrontal cortex<br><br>These are the data and code accompanying the PLoS Computational Biology article "Categorical encoding of decision variables in orbitofrontal cortex" by Arno Onken, Jue Xie, Stefano Panzeri and Camillo Padoa-Schioppa.<br><br>Code<br><br>The code in the 'code' folder is written in Python and tested with version 3.6.6. The following additional packages are required:<br>- NumPy tested with version 1.16.5<br>- SciPy tested with version 1.1.0<br>- Scikit-learn tested with version 1.19.1<br>- spherecluster https://pypi.org/project/spherecluster/0.1.2/<br>- diptest https://github.com/alimuldal/diptest<br><br>To reproduce the figures, run the shell script 'plot_figures.sh'. The 'plot_*.py' scripts use the result data in the folders that are specified in the shell script. To reproduce the result data, use the appropriate 'calculate_*.py' script.<br><br>Data<br><br>The data are located in the 'data' folder.<br>- 'ofc_psth' contains the peri-stimulus time histograms for each cell. These are used for the PAIRS analysis only (corresponding script 'calculate_pairs.py'.<br>- 'ofc_rates_all' contains the rates for each cell and trial type in the files 'rates.mat' and a list of the trial types in 'trialtypes.mat'. The other files in the folder are result files generated by the corresponding 'calculate_*.py' scripts.<br>- 'ofc_rates_shuffled' like 'ofc_rates_all' but the rates where shuffled (see supplementary figure S2 Fig).<br>- 'ofc_rates_time_windows' contains sub folders for each time window. Each of these sub folders has the same structure as 'ofc_rates_all'.<br>- 'synthetic_banana' contains the synthetic data used for Figure 3.<br>- 'synthetic_uniform' contains the synthetic non-categorical data sampled uniformly from the hypersphere (see Figures 5 and 6).<br>- 'synthetic_variable' contains the synthetic categorical data (see Figures 4, 5 and 6).
DOI: 10.6084/m9.figshare.9844349.v1
title: Categorical encoding of decision variables in orbitofrontal cortex

description: Categorical encoding of decision variables in orbitofrontal cortex<br><br>These are the data and code accompanying the PLoS Computational Biology article "Categorical encoding of decision variables in orbitofrontal cortex" by Arno Onken, Jue Xie, Stefano Panzeri and Camillo Padoa-Schioppa.<br><br>Code<br><br>The code in the 'code' folder is written in Python and tested with version 3.6.6. The following additional packages are required:<br>- NumPy tested with version 1.16.5<br>- SciPy tested with version 1.1.0<br>- Scikit-learn tested with version 1.19.1<br>- spherecluster https://pypi.org/project/spherecluster/0.1.2/<br>- diptest https://github.com/alimuldal/diptest<br><br>To reproduce the figures, run the shell script 'plot_figures.sh'. The 'plot_*.py' scripts use the result data in the folders that are specified in the shell script. To reproduce the result data, use the appropriate 'calculate_*.py' script.<br><br>Data<br><br>The data are located in the 'data' folder.<br>- 'ofc_psth' contains the peri-stimulus time histograms for each cell. These are used for the PAIRS analysis only (corresponding script 'calculate_pairs.py'.<br>- 'ofc_rates_all' contains the rates for each cell and trial type in the files 'rates.mat' and a list of the trial types in 'trialtypes.mat'. The other files in the folder are result files generated by the corresponding 'calculate_*.py' scripts.<br>- 'ofc_rates_shuffled' like 'ofc_rates_all' but the rates where shuffled (see supplementary figure S2 Fig).<br>- 'ofc_rates_time_windows' contains sub folders for each time window. Each of these sub folders has the same structure as 'ofc_rates_all'.<br>- 'synthetic_banana' contains the synthetic data used for Figure 3.<br>- 'synthetic_uniform' contains the synthetic non-categorical data sampled uniformly from the hypersphere (see Figures 5 and 6).<br>- 'synthetic_variable' contains the synthetic categorical data (see Figures 4, 5 and 6).
DOI: 10.6084/m9.figshare.9843929
title: Eco_evo_papers

description: A full scrape of Web of Science examining eco-evo publications indexed from 1990 to 2019. Query from this resource was completed August 2019.
DOI: 10.6084/m9.figshare.9842702
title: A high-quality hourly, daily and monthly solar irradiance dataset in China during 1981-2014 based on MERRA-2 Reanalysis products

description: <b>A high-quality hourly, daily and monthly solar irradiance dataset in China during 1981-2014</b><b> based on MERRA-2 Reanalysis products</b>Wenmin Qin<sup>1</sup>, Lunche Wang<sup>1</sup>, Ming Zhang<sup>1</sup>, Lan Feng<sup>1</sup>, Yulong Zhong<sup>1</sup>, Qiqi Zhu<sup>1</sup>，Hejin Fang<sup>1</sup>, Hong Cai<sup>1</sup>, Chao Yang<sup>2</sup><sup>1</sup>Hubei Key Laboratory of Critical Zone Evolution, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China;<sup>2</sup> Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan 430077, China;Corresponding author: Wenmin Qin, Hubei Key Laboratory of Critical Zone Evolution, School of Geography and Information Engineering, China University of Geosciences, Lumo road 388, Hongshan District, Wuhan 430074, China; Tel.: +86 18163315797; E-mail: qinwenmin@cug.edu.cn.<b>Abstract: </b>Solar irradiance (SI) is the main driving factor contributing to climate change and energy balance between the land and atmosphere. High-quality records of global solar irradiance (GHI), direct normal irradiance (DNI) and diffuse solar irradiance (DIF) are of vital importance for solar applications, but the solar radiation observations are sparse around the world. As an alternative, numerous SI reanalysis data in grid format have been developed in regional and global scales. Among them, the MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, version 2) products could provide high quality SI records with acceptable accuracy and long temporal ranges. This study attempted to improve the accuracy of GHI records derived from MERRA-2 products, and to generate grid DNI and DIF datasets for all-sky conditions over mainland China during 1981-2014, based on the REST2 model and cloud transmittance estimates combining sunshine observations. The results indicate that the estimated GHI values (GHInew) show higher agreements with GHI measurement at 17 CMA (China meteorological administrations) stations than that for the GHI records derived from MERRA-2 products (MERRA-2 GHI). Then, grid GHI, DNI and DIF datasets (0.50° (lat) *0.625° (lon)) throughout China were constructed. The results indicated that the MERRA-2 GHI records may overestimate the GHI values over mainland China. The grid GHI, DNI and DIF dataset generated in this study can assist in numerous solar studies and applications.<br><br><br>Correspondence to: qin wenmin (qinwenmin@cug.edu.cn)
DOI: 10.6084/m9.figshare.9843929.v2
title: Eco_evo_papers

description: A full scrape of Web of Science examining eco-evo publications indexed from 1990 to 2019. Query from this resource was completed August 2019.
DOI: 10.6084/m9.figshare.9843929.v1
title: Eco_evo_papers

description: A full scrape of Web of Science examining eco-evo publications indexed from 1990 to 2019. Query from this resource was completed August 2019.
DOI: 10.6084/m9.figshare.9842702.v1
title: A high-quality hourly, daily and monthly solar irradiance dataset in China during 1981-2014 based on MERRA-2 Reanalysis products

description: <b>A high-quality hourly, daily and monthly solar irradiance dataset in China during 1981-2014</b><b> based on MERRA-2 Reanalysis products</b>Wenmin Qin<sup>1</sup>, Lunche Wang<sup>1</sup>, Ming Zhang<sup>1</sup>, Lan Feng<sup>1</sup>, Yulong Zhong<sup>1</sup>, Qiqi Zhu<sup>1</sup>，Hejin Fang<sup>1</sup>, Hong Cai<sup>1</sup>, Chao Yang<sup>2</sup><sup>1</sup>Hubei Key Laboratory of Critical Zone Evolution, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China;<sup>2</sup> Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan 430077, China;Corresponding author: Wenmin Qin, Hubei Key Laboratory of Critical Zone Evolution, School of Geography and Information Engineering, China University of Geosciences, Lumo road 388, Hongshan District, Wuhan 430074, China; Tel.: +86 18163315797; E-mail: qinwenmin@cug.edu.cn.<b>Abstract: </b>Solar irradiance (SI) is the main driving factor contributing to climate change and energy balance between the land and atmosphere. High-quality records of global solar irradiance (GHI), direct normal irradiance (DNI) and diffuse solar irradiance (DIF) are of vital importance for solar applications, but the solar radiation observations are sparse around the world. As an alternative, numerous SI reanalysis data in grid format have been developed in regional and global scales. Among them, the MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, version 2) products could provide high quality SI records with acceptable accuracy and long temporal ranges. This study attempted to improve the accuracy of GHI records derived from MERRA-2 products, and to generate grid DNI and DIF datasets for all-sky conditions over mainland China during 1981-2014, based on the REST2 model and cloud transmittance estimates combining sunshine observations. The results indicate that the estimated GHI values (GHInew) show higher agreements with GHI measurement at 17 CMA (China meteorological administrations) stations than that for the GHI records derived from MERRA-2 products (MERRA-2 GHI). Then, grid GHI, DNI and DIF datasets (0.50° (lat) *0.625° (lon)) throughout China were constructed. The results indicated that the MERRA-2 GHI records may overestimate the GHI values over mainland China. The grid GHI, DNI and DIF dataset generated in this study can assist in numerous solar studies and applications.<br><br><br>Correspondence to: qin wenmin (qinwenmin@cug.edu.cn)
DOI: 10.6084/m9.figshare.9841391
title: Weather Data of 308 Cities Worldwide from WWIS (Accessed on 2019)

description: This dataset is about the annual temperature and precipitation of 308 main cities of 75 countries and regions. These data are computed with the 30-year period data (accessed on 2019) published by World Weather Information Service (WWIS). According to the usage permission and requirement of the WWIS at http://worldweather.wmo.int/en/pilot.html (accessed on September 15th, 2019), the National Meteorological and Hydrological Services which provide these data are listed in the document.
DOI: 10.6084/m9.figshare.9841391.v1
title: Weather Data of 308 Cities Worldwide from WWIS (Accessed on 2019)

description: This dataset is about the annual temperature and precipitation of 308 main cities of 75 countries and regions. These data are computed with the 30-year period data (accessed on 2019) published by World Weather Information Service (WWIS). According to the usage permission and requirement of the WWIS at http://worldweather.wmo.int/en/pilot.html (accessed on September 15th, 2019), the National Meteorological and Hydrological Services which provide these data are listed in the document.
DOI: 10.6084/m9.figshare.9840212
title: Global Snapshot of 145 B. pseudomallei isolates

description: Accession numbers and references retrieved from Sarovich et. al. 2016 for the 145 global B. pseudomallei isolate dataset
DOI: 10.6084/m9.figshare.9839594
title: Microbes and Antibiotic Susceptibility

description: The data is about the distribution of microbes and antibiotic susceptibility in patients with cSSTIs of the extremities in north-eastern China.
DOI: 10.6084/m9.figshare.9840212.v1
title: Global Snapshot of 145 B. pseudomallei isolates

description: Accession numbers and references retrieved from Sarovich et. al. 2016 for the 145 global B. pseudomallei isolate dataset
DOI: 10.6084/m9.figshare.9839513
title: Data.sav

description: Sensorized coordination.<br>
DOI: 10.6084/m9.figshare.9839594.v1
title: Microbes and Antibiotic Susceptibility

description: The data is about the distribution of microbes and antibiotic susceptibility in patients with cSSTIs of the extremities in north-eastern China.
DOI: 10.6084/m9.figshare.9839513.v1
title: Data.sav

description: Sensorized coordination.<br>
DOI: 10.6084/m9.figshare.9838823
title: Additional file 3: of Biological variation of immunological blood biomarkers in healthy individuals and quality goals for biomarker tests

description: Figure S1 and Figure S2. There were strong positive correlation coefficients (r) between serum levels of IL-1ra and TNF-α (r = 0.65, p
DOI: 10.6084/m9.figshare.9838823.v1
title: Additional file 3: of Biological variation of immunological blood biomarkers in healthy individuals and quality goals for biomarker tests

description: Figure S1 and Figure S2. There were strong positive correlation coefficients (r) between serum levels of IL-1ra and TNF-α (r = 0.65, p
DOI: 10.6084/m9.figshare.9838796
title: Additional file 2: of Comparative analysis of amplicon and metagenomic sequencing methods reveals key features in the evolution of animal metaorganisms

description: Supplementary Tables. (ZIP 1765 kb)
DOI: 10.6084/m9.figshare.9838796.v1
title: Additional file 2: of Comparative analysis of amplicon and metagenomic sequencing methods reveals key features in the evolution of animal metaorganisms

description: Supplementary Tables. (ZIP 1765 kb)
DOI: 10.6084/m9.figshare.9838757
title: Additional file 5: of Genome wide search to identify reference genes candidates for gene expression analysis in Gossypium hirsutum

description: Stability analysis of the candidate reference genes using different algorithms. (XLSX 69 kb)
DOI: 10.6084/m9.figshare.9838757.v1
title: Additional file 5: of Genome wide search to identify reference genes candidates for gene expression analysis in Gossypium hirsutum

description: Stability analysis of the candidate reference genes using different algorithms. (XLSX 69 kb)
DOI: 10.6084/m9.figshare.9838730
title: Additional file 3: of Genome wide search to identify reference genes candidates for gene expression analysis in Gossypium hirsutum

description: List of Genes selected in the study. The list of genes in cluster 2 (sheet 1) and cluster 1(sheet 2) top 100 Genes in the selected cluster 1, sorted in ascending order based on the calculated Euclidean distance (sheet 3) and the selected 10 (sheet 4) genes are listed. (XLSX 1375 kb)
DOI: 10.6084/m9.figshare.9838730.v1
title: Additional file 3: of Genome wide search to identify reference genes candidates for gene expression analysis in Gossypium hirsutum

description: List of Genes selected in the study. The list of genes in cluster 2 (sheet 1) and cluster 1(sheet 2) top 100 Genes in the selected cluster 1, sorted in ascending order based on the calculated Euclidean distance (sheet 3) and the selected 10 (sheet 4) genes are listed. (XLSX 1375 kb)
DOI: 10.6084/m9.figshare.9838712
title: Additional file 7: of A purely bioinformatic pipeline for the prediction of mammalian odorant receptor gene enhancers

description: Annotation tracks listing all predicted enhancers for genomic architecture in which an element search was performed. BED-formatted text files (.bed) annotating, at threshold distance = 1 Mb, putative elements found within (or nearby) loci of selected genomic architectures. File names are composed by list type (either: main olfactory epithelium, MOE; olfactome; vomeronasal organ, VNO; T-cell receptor genes, “t_cell_receptor”), species and threshold value (invariably “1_0Mb”); “BLAST” marks the MOE putative enhancer list obtained through the BLAST-derived pipeline variant. A “NO-PSEUDO” in the file name indicates that the gene list originating the file is devoid of pseudogene-flagged entries. A “CAND-EL” at the beginning of each file name differentiates these BED files from those found in Additional files 4 and 5. Text files are provided as a single compressed (.zip) file available in the Open Science Framework repository, see paragraph “Availability of data and material”. (ZIP 7 kb)
DOI: 10.6084/m9.figshare.9838706
title: Additional file 6: of A purely bioinformatic pipeline for the prediction of mammalian odorant receptor gene enhancers

description: Position-specific weight matrices (PSWMs) used to predict elements. Matrices are presented as MEME-formatted text files (.txt). Each file name reports the numeric identifier of the corresponding PSWM. Text files are provided as a single compressed (.zip) file available in the Open Science Framework repository, see paragraph â Availability of data and materialâ . (ZIP 37 kb)
DOI: 10.6084/m9.figshare.9838712.v1
title: Additional file 7: of A purely bioinformatic pipeline for the prediction of mammalian odorant receptor gene enhancers

description: Annotation tracks listing all predicted enhancers for genomic architecture in which an element search was performed. BED-formatted text files (.bed) annotating, at threshold distance = 1 Mb, putative elements found within (or nearby) loci of selected genomic architectures. File names are composed by list type (either: main olfactory epithelium, MOE; olfactome; vomeronasal organ, VNO; T-cell receptor genes, “t_cell_receptor”), species and threshold value (invariably “1_0Mb”); “BLAST” marks the MOE putative enhancer list obtained through the BLAST-derived pipeline variant. A “NO-PSEUDO” in the file name indicates that the gene list originating the file is devoid of pseudogene-flagged entries. A “CAND-EL” at the beginning of each file name differentiates these BED files from those found in Additional files 4 and 5. Text files are provided as a single compressed (.zip) file available in the Open Science Framework repository, see paragraph “Availability of data and material”. (ZIP 7 kb)
DOI: 10.6084/m9.figshare.9838706.v1
title: Additional file 6: of A purely bioinformatic pipeline for the prediction of mammalian odorant receptor gene enhancers

description: Position-specific weight matrices (PSWMs) used to predict elements. Matrices are presented as MEME-formatted text files (.txt). Each file name reports the numeric identifier of the corresponding PSWM. Text files are provided as a single compressed (.zip) file available in the Open Science Framework repository, see paragraph â Availability of data and materialâ . (ZIP 37 kb)
DOI: 10.6084/m9.figshare.9838691
title: Additional file 4: of A purely bioinformatic pipeline for the prediction of mammalian odorant receptor gene enhancers

description: Annotation tracks highlighting clusters and solitary genes for selected genomic architectures. BED-formatted text files (.bed) annotating loci, at a given threshold distance (in Mb), for different gene lists. File names are composed by list type (either: main olfactory epithelium, MOE; olfactome; vomeronasal organ, VNO; T-cell receptor genes, “t_cell_receptor”), species and threshold value (periods in numerical values being replaced by underscores, “_”). A “NO-PSEUDO” at the beginning of the file name indicates that the gene list originating the file is devoid of pseudogene-flagged entries. Text files are provided as a single compressed (.zip) file available in the Open Science Framework repository, see paragraph “Availability of data and material”. (ZIP 155 kb)
DOI: 10.6084/m9.figshare.9838697
title: Additional file 5: of A purely bioinformatic pipeline for the prediction of mammalian odorant receptor gene enhancers

description: Annotation tracks containing evolutionarily conserved stretches among loci of selected genomic architectures. BED-formatted text files (.bed) annotating, at threshold distance = 1 Mb, broadened conserved stretches within (or nearby) loci of some notable genomic architectures. File names are composed by list type (either: main olfactory epithelium, MOE; olfactome; vomeronasal organ, VNO; T-cell receptor genes, “t_cell_receptor”), species and threshold value (invariably “1_0Mb”). A “NO-PSEUDO” in the file name indicates that the gene list originating the file is devoid of pseudogene-flagged entries. A “CONSERVED” at the beginning of each file name differentiates these BED files from those found in Additional files 4 and 7. Text files are provided as a single compressed (.zip) file available in the Open Science Framework repository, see paragraph “Availability of data and material”. (ZIP 2843 kb)
DOI: 10.6084/m9.figshare.9838697.v1
title: Additional file 5: of A purely bioinformatic pipeline for the prediction of mammalian odorant receptor gene enhancers

description: Annotation tracks containing evolutionarily conserved stretches among loci of selected genomic architectures. BED-formatted text files (.bed) annotating, at threshold distance = 1 Mb, broadened conserved stretches within (or nearby) loci of some notable genomic architectures. File names are composed by list type (either: main olfactory epithelium, MOE; olfactome; vomeronasal organ, VNO; T-cell receptor genes, “t_cell_receptor”), species and threshold value (invariably “1_0Mb”). A “NO-PSEUDO” in the file name indicates that the gene list originating the file is devoid of pseudogene-flagged entries. A “CONSERVED” at the beginning of each file name differentiates these BED files from those found in Additional files 4 and 7. Text files are provided as a single compressed (.zip) file available in the Open Science Framework repository, see paragraph “Availability of data and material”. (ZIP 2843 kb)
DOI: 10.6084/m9.figshare.9838691.v1
title: Additional file 4: of A purely bioinformatic pipeline for the prediction of mammalian odorant receptor gene enhancers

description: Annotation tracks highlighting clusters and solitary genes for selected genomic architectures. BED-formatted text files (.bed) annotating loci, at a given threshold distance (in Mb), for different gene lists. File names are composed by list type (either: main olfactory epithelium, MOE; olfactome; vomeronasal organ, VNO; T-cell receptor genes, “t_cell_receptor”), species and threshold value (periods in numerical values being replaced by underscores, “_”). A “NO-PSEUDO” at the beginning of the file name indicates that the gene list originating the file is devoid of pseudogene-flagged entries. Text files are provided as a single compressed (.zip) file available in the Open Science Framework repository, see paragraph “Availability of data and material”. (ZIP 155 kb)
DOI: 10.6084/m9.figshare.9838679
title: Additional file 3: of A purely bioinformatic pipeline for the prediction of mammalian odorant receptor gene enhancers

description: Detailed information about locus composition at various threshold distances. Text files (.txt) detailing on locus composition, at a given threshold distance (in Mb), for different gene lists. File names are composed by list type (either: main olfactory epithelium, MOE; olfactome; vomeronasal organ, VNO; T-cell receptor genes, “t_cell_receptor”), species and threshold value (periods in numerical values being replaced by underscores, “_”). A “NO-PSEUDO” at the beginning of the file name indicates that the gene list originating the file is devoid of pseudogene-flagged entries. Clusters are presented from the richest (in terms of number of genes) to miniclusters; for each locus, the average intergenic distance (in Kb) is reported. Solitary genes are listed after clusters. Text files are provided as a single compressed (.zip) file available in the Open Science Framework repository, see paragraph “Availability of data and material”. (ZIP 719 kb)