cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6075/j0r78fdg
title: A dataset of chromosomal instability gene signature scores in normal and cancer cells from the human breast

description: To generate the CIN scores for each cell type, we analyzed data with the Python GSEAPY Library (https://gseapy.readthedocs.io/en/latest/). First, input files were read using Python’s Pandas library and joined with each other using the ID & amp columns before deleting any unnecessary columns. Ensemble Gene IDs were mapped to their HGNC Symbols using Python’s BioMart API https://pypi.org/project/biomart
description: These data show the relative amount of chromosomal instability (CIN) in a diverse array of human breast cell types, including non-transformed mammary epithelial cells as well as cancer cell lines. Additional data is also provided from human embryonic and mesenchymal stem cells. To produce this dataset, we compared a published chromosomal instability gene signature against publicly available datasets containing gene expression information for each cell. We then analyzed these data with the Python GSEAPY software package, providing a CIN enrichment score for each cell. These data are useful for comparing the relative amounts of CIN in different breast cell types. This includes cells representing the major clinical (ER/PR+, HER2+ & Triple-negative) as well as intrinsic breast cancer subtypes (Luminal B, HER2+, Basal-like and Claudin-low). Our dataset has a great potential for re-use given the recent surge in interest surrounding the role of CIN in breast cancer. The large size of the dataset, coupled with the diversity of the cell types represented, provides numerous possibilities for future comparisons.
description: FASTQs were converted to gene-expression matrices and the files were processed to remove all the header information and only retain the data. The CIN gene signature was acquired from Bakhoum et al. The CIN scores were obtained by examining enrichment for the CIN associated gene signature in each cell type represented in the sequencing datasets according to Barbie et al. To generate the CIN scores for each cell type, we analyzed data with the Python GSEAPY Library (https://gseapy.readthedocs.io/en/latest/). First, input files were read using Python’s Pandas library and joined with each other using the ID & amp columns before deleting any unnecessary columns. Ensemble Gene IDs were mapped to their HGNC Symbols using Python’s BioMart API https://pypi.org/project/biomart. Any Ensemble ID which did not have a corresponding HGNC Symbol was dropped. Once we obtained the data frame having HGNC Symbols as rows, samples as columns, and their feature counts as values in all rows, this data frame, along with the CIN gene set was passed to the Single Sample GSEA Python library. The final data comprised 36866 rows and 106 columns before feeding it into GSEAPY. To determine the enrichment scores (ES), we applied Single Sample GSEA to the final data frame. The experiment was repeated with a normalized version of the data frame, but the normalized enrichment scores (NES) were identical to the ES. GSEAPY output was then processed into Excel format and saved as final results files.
DOI: 10.15468/dl.mnm57f
title: Occurrence Download

description: A dataset containing 321 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is South America",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Entodon jamesonii (Taylor) Mitt."
 ]
}

 The dataset includes 321 records from 28 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022811-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/8941529
title: BBC News
DOI: 10.15468/dl.3tuxjf
title: Occurrence Download

description: A dataset containing 362 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is South America",
 "TaxonKey is Entosthodon bonplandii (Hook.) Mitt."
 ]
}

 The dataset includes 362 records from 51 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022812-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6075/j0q240f0
title: Data from: Genome-Wide Association Study in 3,173 Outbred Rats Identifies Multiple Loci for Body Weight, Adiposity, and Fasting Glucose. In The Center for GWAS in Outbred Rats Database (C-GORD)

description: All relevant tools and software used to generate the data in this object are noted below and in the Methods section of the associated publication.
GWAS: GEMMA v0.97
Converting genotype probabilities into dosages: QCTOOL v2
Converting genotype probabilities into Hard-Called Genotypes: PLINK v2.0
LD pruning of the genotype data : PLINK v1.90
Command used to LD-prune the genotype data in PLINK v1.90 : --indep-pairwise 50 5 0.95
The 50 is the number of variants to look at in a window, the 5 is the number of variants to shift the windows side way each step, and the 0.95 is the r2 threshold for trimming SNPs.
description: The data in this object comes from the publication titled “Genome-Wide Association Study in 3,173 Outbred Rats Identifies Multiple Loci for Body Weight, Adiposity, and Fasting Glucose”, authored by Chitre et al. The majority of the object is dedicated to the phenotypic and genetic data presented throughout the publication. However, this object also contains additional information beyond what is presented, including (1) a genetic relatedness matrix generated from LD pruned set of genotypes, (2) Summary files for the GWAS results for all phenotypes, (3) Trait descriptions. Details on how these files were generated can all be found in the Methods section of the associated publication.
DOI: 10.15468/dl.pae63d
title: Occurrence Download

description: A dataset containing 1697 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is South America",
 "TaxonKey is Fissidens asplenioides Hedw."
 ]
}

 The dataset includes 1697 records from 86 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022813-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.26285464
title: A PRISMA-ScR checklist for the article, The application of exponential random graph models to online learning networks: a scoping review

description: This checklist represents the quality assurance for the study, The application of exponential random graph models to online learning networks: a scoping review.
DOI: 10.15468/dl.mzatqz
title: Occurrence Download

description: A dataset containing 1339 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is South America",
 "TaxonKey is Fissidens crispus Mont."
 ]
}

 The dataset includes 1339 records from 74 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022814-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4yszhz
title: Occurrence Download

description: A dataset containing 64 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Xenesthis Simon, 1891"
 ]
}

 The dataset includes 64 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022815-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.26285491
title: Supplemental_Files1-4

description: Supplemental Files 1 - 4 in Excel format for "Revisiting the y-ome of <i>Escherichia coli"</i>
DOI: 10.15468/dl.qdjh95
title: Occurrence Download

description: A dataset containing 6826 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Colombia",
 "TaxonKey is Tremarctos ornatus (F.G.Cuvier, 1825)"
 ]
}

 The dataset includes 6826 records from 65 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022816-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25916/sut.26286118
title: Raw metrics for measuring software evolution in open source software systems

description: The evolution of a software system can be studied in terms of how various properties as reflected by software metrics change over time. We build a release history model by analysing the compiled class files. Our release history model captures metadata and 58 different metrics at a class level. We also build a class dependency graph for each release in the evolution history. The data selection and metric extraction method that we use ensures that we study non-trivial software allowing us to extend our findings to other comparable software systems built in Java. We also analyse compiled binaries that have already gone through the build process improving the accuracy of our measures. Further, as discussed in the previous chapter, we focus on contributions from the core development team ignoring third party libraries ensuring that the metrics that we collect are a better reflection of the development effort. Chapter 4 (Measuring Evolving Software) of the thesis this data accompanies explains the metric extraction process and provides a discussion of the metrics we collect from the Java software systems and provide appropriate motivation for our choices. The metrics for each software system are in a separate data file. The raw metric data files (50 .txt files in a .zip file measuring ~58MB in total) contain the following columns: (1) Fully Qualified Class Name; (2) Metric Name (We use the abbreviations as defined in Chapter 4); (3) a list of metric values for each release of the software. Columns with empty values indicate that the class did not exist in that release.
DOI: 10.25916/sut.26286118.v1
title: Raw metrics for measuring software evolution in open source software systems

description: The evolution of a software system can be studied in terms of how various properties as reflected by software metrics change over time. We build a release history model by analysing the compiled class files. Our release history model captures metadata and 58 different metrics at a class level. We also build a class dependency graph for each release in the evolution history. The data selection and metric extraction method that we use ensures that we study non-trivial software allowing us to extend our findings to other comparable software systems built in Java. We also analyse compiled binaries that have already gone through the build process improving the accuracy of our measures. Further, as discussed in the previous chapter, we focus on contributions from the core development team ignoring third party libraries ensuring that the metrics that we collect are a better reflection of the development effort. Chapter 4 (Measuring Evolving Software) of the thesis this data accompanies explains the metric extraction process and provides a discussion of the metrics we collect from the Java software systems and provide appropriate motivation for our choices. The metrics for each software system are in a separate data file. The raw metric data files (50 .txt files in a .zip file measuring ~58MB in total) contain the following columns: (1) Fully Qualified Class Name; (2) Metric Name (We use the abbreviations as defined in Chapter 4); (3) a list of metric values for each release of the software. Columns with empty values indicate that the class did not exist in that release.
DOI: 10.15468/dl.zqd8dg
title: Occurrence Download

description: A dataset containing 257 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "TaxonKey is Bambusa emeiensis L.C.Chia & H.L.Fung"
 ]
}

 The dataset includes 257 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022817-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.59vk23
title: Occurrence Download

description: A dataset containing 281 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Madagascar",
 "TaxonKey is Delonix regia (Bojer ex Hook.) Raf."
 ]
}

 The dataset includes 281 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022818-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.7485566
title: Shallow tremor list observed by Distributed acoustic sensing (DAS) and DAS data off Cape Muroto, southwest Japan

description: Distributed Acoustic Sensing (DAS) data (hdf5 data) with the Muroto Cable, southwest Japan, when shallow tremors are observed (period: January 30–February 8, 2022).

I added a list of shallow tremors observed by DAS and shallow very low frequency earthquakes (VLFEs) corresponding to these tremors in the newest version.

The DAS measurement is conducted by AP Sensing with a sensing length of 55 km along the Muroto Cable. Gauge length is 40.8 m. The waveforms were stacked in successive 10 channels to downsample to 51-m channel spacing and decimated to 100 Hz sampling.

The tremor analysis with this DAS data is presented in AGU Fall Meeting 2022 (https://agu.confex.com/agu/fm22/meetingapp.cgi/Paper/1101461) and written in a submitted paper.

The filename of hdf5 DAS data show the start time (year-month-day_hour.minute.second) in UT. The data length of each file is one minute. The values of matrices in hdf5 files are converted to differential phase by multiplying pi/32768. After integrating the differential phases, phases can be converted to strain based on equation (10) of SEAFOM (2018) (https://seafom.com/?mdocs-file=1270). The optical wavelength of DAS system is 1550 nm, the effective index of refraction in sensing fiber is 1.4682, the photo-elastic scaling factor is 0.78.

Data Set S1: Tremor list observed by DAS located from DAS and DONET data

Data Set S2: Tremor list located from only DONET data

Data Set S3: VLFE list which temporally correspond to tremors observed by DAS

channelinfo.txt: DAS channel position information that are used for locating tremors. The channel position was determined by using the air gun signals.

DONET broadband seismograms data can be downloaded from the website of National Research Institute for Earth Science and Disaster Resilience Hi-net (https://hinetwww11.bosai.go.jp/auth/download/cont/?LANG=en).
DOI: 10.5281/zenodo.7485567
title: Distributed acoustic sensing (DAS) data of shallow tremors off Cape Muroto, southwest Japan

description: Distributed Acoustic Sensing (DAS) strain rate data (hdf5 data) with the Muroto Cable, southwest Japan, when shallow tremors occurred (period: January 30–February 8, 2022). The DAS measurement is conducted by AP Sensing (model N5200A) with a sensing length of 55 km along the Muroto Cable. Gauge length is 40.4 m. The waveforms were stacked in successive 10 channels to downsample to 51-m channel spacing and decimated to 100 Hz sampling. The tremor analysis with this DAS data is presented in AGU Fall Meeting 2022 (https://agu.confex.com/agu/fm22/meetingapp.cgi/Paper/1101461) and written in a submitted paper. The filename show the start time (year-month-day_hour.minute.second) in UT. The data length of each file is one minute. The unit of the data is nstrain/s.
DOI: 10.5281/zenodo.7504867
title: Shallow tremor catalog observed by Distributed acoustic sensing (DAS) and DAS data off Cape Muroto, southwest Japan

description: Distributed Acoustic Sensing (DAS) strain rate data (hdf5 data) with the Muroto Cable, southwest Japan, when shallow tremors are observed (period: January 30–February 8, 2022). I added a catalog of shallow tremors observed by DAS and shallow very low frequency earthquakes (VLFEs) corresponding to these tremors in version 2. The DAS measurement is conducted by AP Sensing (model N5200A) with a sensing length of 55 km along the Muroto Cable. Gauge length is 40.4 m. The waveforms were stacked in successive 10 channels to downsample to 51-m channel spacing and decimated to 100 Hz sampling. The tremor analysis with this DAS data is presented in AGU Fall Meeting 2022 (https://agu.confex.com/agu/fm22/meetingapp.cgi/Paper/1101461) and written in a submitted paper. The filename of hdf5 DAS data show the start time (year-month-day_hour.minute.second) in UT. The data length of each file is one minute. The unit of the data is nstrain/s. Data Set S1: Tremor catalog observed by DAS located from DAS and DONET data Data Set S2: Tremor catalog located from only DONET data Data Set S3: VLFE catalog which temporally correspond to tremors observed by DAS DONET broadband seismograms data can be downloaded from the website of National Research Institute for Earth Science and Disaster Resilience Hi-net (https://hinetwww11.bosai.go.jp/auth/download/cont/?LANG=en).
DOI: 10.5281/zenodo.7776512
title: Shallow tremor list observed by Distributed acoustic sensing (DAS) and DAS data off Cape Muroto, southwest Japan

description: Distributed Acoustic Sensing (DAS) data (hdf5 data) with the Muroto Cable, southwest Japan, when shallow tremors are observed (period: January 30–February 8, 2022). I added a list of shallow tremors observed by DAS and shallow very low frequency earthquakes (VLFEs) corresponding to these tremors in the newest version. The DAS measurement is conducted by AP Sensing (model N5200A) with a sensing length of 55 km along the Muroto Cable. Gauge length is 40.8 m. The waveforms were stacked in successive 10 channels to downsample to 51-m channel spacing and decimated to 100 Hz sampling. The tremor analysis with this DAS data is presented in AGU Fall Meeting 2022 (https://agu.confex.com/agu/fm22/meetingapp.cgi/Paper/1101461) and written in a submitted paper. The filename of hdf5 DAS data show the start time (year-month-day_hour.minute.second) in UT. The data length of each file is one minute. By multiplying 0.5057, the values in the data are converted to nstrain/s. Data Set S1: Tremor list observed by DAS located from DAS and DONET data Data Set S2: Tremor list located from only DONET data Data Set S3: VLFE list which temporally correspond to tremors observed by DAS DONET broadband seismograms data can be downloaded from the website of National Research Institute for Earth Science and Disaster Resilience Hi-net (https://hinetwww11.bosai.go.jp/auth/download/cont/?LANG=en).
DOI: 10.5281/zenodo.7935235
title: Shallow tremor list observed by Distributed acoustic sensing (DAS) and DAS data off Cape Muroto, southwest Japan

description: Distributed Acoustic Sensing (DAS) data (hdf5 data) with the Muroto Cable, southwest Japan, when shallow tremors are observed (period: January 30–February 8, 2022).

I added a list of shallow tremors observed by DAS and shallow very low frequency earthquakes (VLFEs) corresponding to these tremors in the newest version.

The DAS measurement is conducted by AP Sensing with a sensing length of 55 km along the Muroto Cable. Gauge length is 40.8 m. The waveforms were stacked in successive 10 channels to downsample to 51-m channel spacing and decimated to 100 Hz sampling.

The tremor analysis with this DAS data is presented in AGU Fall Meeting 2022 (https://agu.confex.com/agu/fm22/meetingapp.cgi/Paper/1101461) and written in a submitted paper.

The filename of hdf5 DAS data show the start time (year-month-day_hour.minute.second) in UT. The data length of each file is one minute. The values of matrices in hdf5 files are converted to differential phase by multiplying pi/32768. After integrating the differential phases, phases can be converted to strain based on equation (10) of SEAFOM (2018) (https://seafom.com/?mdocs-file=1270). The optical wavelength of DAS system is 1550 nm, the effective index of refraction in sensing fiber is 1.4682, the photo-elastic scaling factor is 0.78.

Data Set S1: Tremor list observed by DAS located from DAS and DONET data

Data Set S2: Tremor list located from only DONET data

Data Set S3: VLFE list which temporally correspond to tremors observed by DAS

channelinfo.txt: DAS channel position information that are used for locating tremors. The channel position was determined by using the air gun signals.

DONET broadband seismograms data can be downloaded from the website of National Research Institute for Earth Science and Disaster Resilience Hi-net (https://hinetwww11.bosai.go.jp/auth/download/cont/?LANG=en).
DOI: 10.15468/dl.pjsjvr
title: Occurrence Download

description: A dataset containing 43108 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Galeocerdo cuvier (Péron & Lesueur, 1822)"
 ]
}

 The dataset includes 43108 records from 211 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022819-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34945/f5dg6d
title: Part I: A CLIMBER Meta-analysis, recovery time measured by behavioral outcome tests after contusive injuries on various spinal levels segments in rats and mice of both sexes from Literature-Extracted Data (LED)

description: STUDY PURPOSE: The purpose of the study was to analyze recovery after spinal cord injury through various different behavioral outcome tests. The study compared effect sizes from literature sourced (literature-extracted data (LED)) to the literatures’ corresponding publicly available raw data (individual animal data (IAD)). Random effect models and regression analyses were applied to evaluate predictors of neuro-conversion in LED versus IAD. Subgroup analyses were performed on animal sex, animal type, animal species, injury severity, injury segment and sample sizes. Publications with common injury models (contusive injuries) and standardized endpoints (open field assessments) were included in the meta-analyses. Studies that recorded open field assessments at 0-3 and 28-56 days past operation were included. This dataset includes the literature-extracted data (LED) (part 1) that was collected for the study. The code to replicate our study can be found on github (https://github.com/ucsf-ferguson-lab/climber_meta_analysis2024.git). This dataset corresponds with another dataset in ODC-SCI (10.34945/F5J59P) which contains raw data, individual animal data (IAD), that directly corresponds to the literature extracted data in this dataset. DATA COLLECTED: The literature-extracted data (LED) contained in this dataset was pulled from numerical and graphical outcomes reported in published literature. This dataset includes data extracted from 7 different published articles. Unlike other datasets in odc-sci, each row represents an experimental group rather than an individual subject. The values are summarized for each experimental group. Each study from the published articles includes contusion injuries with various severities and different locations, which are indicated in this dataset. Different mice and rat species are included in the dataset with both sexes. Outcome scores at different days-post operation from BBB, BMS, Grooming and Forelimb Open Field tests are also included. The behavioral outcome scores over days post operation were used to calculate effect sizes. DATA USAGE NOTES: