cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10113601
title: genbank-2022.03-archaea-k51_0.80_pretrained.zip

description: genbank-2022.03-archaea-k51_0.80_pretrained.zip
DOI: 10.5281/zenodo.10113604
title: genbank-2022.03-archaea-k21_0.995_pretrained.zip

description: genbank-2022.03-archaea-k21_0.995_pretrained.zip
DOI: 10.5281/zenodo.10113603
title: genbank-2022.03-archaea-k21_0.995_pretrained.zip

description: genbank-2022.03-archaea-k21_0.995_pretrained.zip
DOI: 10.5281/zenodo.10113606
title: genbank-2022.03-fungi-k21_0.995_pretrained.zip

description: genbank-2022.03-fungi-k21_0.995_pretrained.zip
DOI: 10.5281/zenodo.10113605
title: genbank-2022.03-fungi-k21_0.995_pretrained.zip

description: genbank-2022.03-fungi-k21_0.995_pretrained.zip
DOI: 10.5281/zenodo.10113608
title: genbank-2022.03-archaea-k31_0.95_pretrained.zip

description: genbank-2022.03-archaea-k31_0.95_pretrained.zip
DOI: 10.5281/zenodo.10113607
title: genbank-2022.03-archaea-k31_0.95_pretrained.zip

description: genbank-2022.03-archaea-k31_0.95_pretrained.zip
DOI: 10.5281/zenodo.10113609
title: genbank-2022.03-protozoa-k31_0.80_pretrained.zip

description: genbank-2022.03-protozoa-k31_0.80_pretrained.zip
DOI: 10.5281/zenodo.10113610
title: genbank-2022.03-protozoa-k31_0.80_pretrained.zip

description: genbank-2022.03-protozoa-k31_0.80_pretrained.zip
DOI: 10.5281/zenodo.10113612
title: genbank-2022.03-archaea-k21_0.80_pretrained.zip

description: genbank-2022.03-archaea-k21_0.80_pretrained.zip
DOI: 10.5281/zenodo.10113611
title: genbank-2022.03-archaea-k21_0.80_pretrained.zip

description: genbank-2022.03-archaea-k21_0.80_pretrained.zip
DOI: 10.5281/zenodo.10113614
title: gtdb-rs214-reps.k51_0.995_pretrained.zip

description: gtdb-rs214-reps.k51_0.995_pretrained.zip
DOI: 10.5281/zenodo.10113613
title: gtdb-rs214-reps.k51_0.995_pretrained.zip

description: gtdb-rs214-reps.k51_0.995_pretrained.zip
DOI: 10.5281/zenodo.10113615
title: genbank-2022.03-fungi-k21_0.95_pretrained.zip

description: genbank-2022.03-fungi-k21_0.95_pretrained.zip
DOI: 10.5281/zenodo.10113616
title: genbank-2022.03-fungi-k21_0.95_pretrained.zip

description: genbank-2022.03-fungi-k21_0.95_pretrained.zip
DOI: 10.5281/zenodo.10072072
title: Champions League Knockout 22-23
DOI: 10.5281/zenodo.10072073
title: Champions League Knockout 22-23
DOI: 10.5281/zenodo.10113942
title: Champions League Knockout 22-23
DOI: 10.5281/zenodo.10114546
title: Champions League Knockout 22-23
DOI: 10.5281/zenodo.10009430
title: GTDB Representative Genome with ANI=0.95 and k=31 (Deprecated)

description: This is a GTDB Representative Dataset pretrained on YACHT with parameters as ANI=0.95 and k=31. It contains 402,709 genomes organized into 85,205 species clusters.
DOI: 10.5281/zenodo.10009431
title: GTDB Representative Genome with ANI=0.95 and k=31 (Deprecated)

description: This is a GTDB Representative Dataset pretrained on YACHT with parameters as ANI=0.95 and k=31. It contains 402,709 genomes organized into 85,205 species clusters.
DOI: 10.34740/kaggle/dsv/6946594
title: white wine quality
DOI: 10.34740/kaggle/dsv/6946574
title: Real GDP per capita Union Europe
DOI: 10.6084/m9.figshare.24416323
title: Utility and limitation of universal PCR screening for SARS-CoV-2 on hospital admission

description: Three files include the statistical data and graph made by graph pad prism.<br>Figure 1 describes the correlation of two PCR targets in COBAS SARS-CoV-2 PCR. The Ct values of target 1 (ORF1ab gene) and target 2 (E gene) from 111 PCR tests are plotted on the graph. Ct value of target 1 and target 2 were plotted on the graph to show the correlation of two targets. Negative PCR result was shown as 40. Red solid line shows the linear regression of 111 PCR results with the equation of regression line and R-square value.6 PCR tests showed negative result in target 1, but showed positive result in target 2. 14 PCR tests showed positive result in target 1, but showed negative result in target 2.<br>Figure 2 describes the transition of the number of PCR screening tests and positive results during the study period.The black line (a) shows the number of monthly PCR screening tests upon admission to our department. The red line shows the number of positive PCR results. For comparing our PCR positive results and local COVID-19 spread, the monthly COVID-19 cases from public data base was shown in (b). The monthly patients number in Fukuoka prefecture (approximately 5 million population) was calculated by the daily cases available at “https://www.mhlw.go.jp/stf/covid-19/open-data.html”<br>Figure 3 describes the PCR positivity after COVID-19 onset.All Ct value of target 1 from 66 cases with known COVID-19 onset date were grouped according to the date after COVID-19 onset and plotted on the graph (a). Negative PCR result was shown as Ct value of 40. Each red dot shows the individual test results with the mean and 95% CI in the groups. Based on the tests results shown in (a), PCR positivity was calculated in (b). There was no PCR test conducted at 91 - 100 days after COVID-19 onset.
DOI: 10.6084/m9.figshare.24416323.v1
title: Utility and limitation of universal PCR screening for SARS-CoV-2 on hospital admission

description: Three files include the statistical data and graph made by graph pad prism.<br>Figure 1 describes the correlation of two PCR targets in COBAS SARS-CoV-2 PCR. The Ct values of target 1 (ORF1ab gene) and target 2 (E gene) from 111 PCR tests are plotted on the graph. Ct value of target 1 and target 2 were plotted on the graph to show the correlation of two targets. Negative PCR result was shown as 40. Red solid line shows the linear regression of 111 PCR results with the equation of regression line and R-square value.6 PCR tests showed negative result in target 1, but showed positive result in target 2. 14 PCR tests showed positive result in target 1, but showed negative result in target 2.<br>Figure 2 describes the transition of the number of PCR screening tests and positive results during the study period.The black line (a) shows the number of monthly PCR screening tests upon admission to our department. The red line shows the number of positive PCR results. For comparing our PCR positive results and local COVID-19 spread, the monthly COVID-19 cases from public data base was shown in (b). The monthly patients number in Fukuoka prefecture (approximately 5 million population) was calculated by the daily cases available at “https://www.mhlw.go.jp/stf/covid-19/open-data.html”<br>Figure 3 describes the PCR positivity after COVID-19 onset.All Ct value of target 1 from 66 cases with known COVID-19 onset date were grouped according to the date after COVID-19 onset and plotted on the graph (a). Negative PCR result was shown as Ct value of 40. Each red dot shows the individual test results with the mean and 95% CI in the groups. Based on the tests results shown in (a), PCR positivity was calculated in (b). There was no PCR test conducted at 91 - 100 days after COVID-19 onset.
DOI: 10.34740/kaggle/dsv/6946470
title: Human Motion Captured by FMCW Radar
DOI: 10.6084/m9.figshare.24268753.v1
title: CETD, a global compound events detection and visualisation toolbox and dataset

description: Compound events (CEs) are attracting increased attention due to their significant societal and ecological impacts. However, their inherent complexity can pose challenges for climate scientists and practitioners, highlighting the need for a more approachable and intuitive framework for detecting and visualising CEs. Here we introduce a Compound Events Toolbox and Dataset (CETD), which provides the first integrated, interactive and extensible platform for CEs detection and visualisation. Employing observations, reanalysis, and model simulations, CETD can quantify the frequency, duration, severity and start/end date of multiple climatic CE types: multivariate, sequential and concurrent events. It can analyse CEs often linked to severe impacts on human health, wildfires, and air pollution, such as hot-dry, wet-windy, and hot-dry-stagnation. To validate the performance of CETD, we conduct statistics for several historical high-impact events, such as the 2019 Australian wildfires and the 2022 European heatwaves. The accessibility and extensibility of CETD will benefit the broader community through better understanding and preparedness of the risks and challenges of CEs in the warming world.
DOI: 10.5519/qd.72b6wkgg
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (16 records)
DOI: 10.5281/zenodo.8294148
title: Mapped data: Transcription factor stoichiometry, motif affinity and syntax regulate single cell chromatin dynamics during fibroblast reprogramming to pluripotency

description: This record contains mapped sequencing data for the paper "Transcription factor stoichiometry, motif affinity and syntax regulate single cell chromatin dynamics during fibroblast reprogramming to pluripotency" by Nair, Ameen et al. It contains single-cell RNA-seq (scRNA) and single-cell ATAC-seq (scATAC) data from a time course of human dermal fibroblasts induced with Yamanaka factors OSKM using a Sendai virus based delivery system. The scRNA and scATAC data is performed at days 0, 2, 4, 6, 8, 10, 12, 14 and the final iPSCs. The experiment was re-performed and single-nucleus multiome (ATAC+RNA) was collected on days 1 and 2. 


The data is as follows:


scATAC: We used Chromap (commit https://github.com/haowenz/chromap/tree/6e97125b9, https://doi.org/10.1038/s41467-021-26865-w) to perform barcode correction, alignment and filtering for each of our samples. The corresponding fragment files (tab separated file containing mapped fragments with columns: chr, start, end, barcode, number of reads) and their tabix indices are available for each sample.


scRNA: We used cellranger v6.0.2 for read mapping and quantification to obtain the counts matrix. We used the GRCh38 2020-A reference. For each sample, the raw and filtered counts matrices are provided. E.g. `D0/raw_feature_bc_matrix.h5` contains an HDF5 object containing gene counts for each barcode and associated metadata for the Day 0 sample. Similarly, the files in `D0/raw_feature_bc_matrix/` contain the same gene x barcode matrix, with the counts matrix in Matrix Market format (`matrix.mtx.gz`), and gene (`features.tsv.gz`) and barcode names (`barcodes.tsv.gz`). 


multiome: The ATAC and RNA components are separately processed using the same tools as mentioned above for scATAC and scRNA. Outputs are in the `snATAC` and `snRNA` subdirectories respectively. In addition, the `ATAC.RNA.bc.map.tsv` file contains a map to link snATAC barcodes to snRNA barcodes.
DOI: 10.5281/zenodo.8294147
title: Mapped data: Transcription factor stoichiometry, motif affinity and syntax regulate single cell chromatin dynamics during fibroblast reprogramming to pluripotency

description: This record contains mapped sequencing data for the paper "Transcription factor stoichiometry, motif affinity and syntax regulate single cell chromatin dynamics during fibroblast reprogramming to pluripotency" by Nair, Ameen et al. It contains single-cell RNA-seq (scRNA) and single-cell ATAC-seq (scATAC) data from a time course of human dermal fibroblasts induced with Yamanaka factors OSKM using a Sendai virus based delivery system. The scRNA and scATAC data is performed at days 0, 2, 4, 6, 8, 10, 12, 14 and the final iPSCs. The experiment was re-performed and single-nucleus multiome (ATAC+RNA) was collected on days 1 and 2. 


The data is as follows:


scATAC: We used Chromap (commit https://github.com/haowenz/chromap/tree/6e97125b9, https://doi.org/10.1038/s41467-021-26865-w) to perform barcode correction, alignment and filtering for each of our samples. The corresponding fragment files (tab separated file containing mapped fragments with columns: chr, start, end, barcode, number of reads) and their tabix indices are available for each sample.


scRNA: We used cellranger v6.0.2 for read mapping and quantification to obtain the counts matrix. We used the GRCh38 2020-A reference. For each sample, the raw and filtered counts matrices are provided. E.g. `D0/raw_feature_bc_matrix.h5` contains an HDF5 object containing gene counts for each barcode and associated metadata for the Day 0 sample. Similarly, the files in `D0/raw_feature_bc_matrix/` contain the same gene x barcode matrix, with the counts matrix in Matrix Market format (`matrix.mtx.gz`), and gene (`features.tsv.gz`) and barcode names (`barcodes.tsv.gz`). 


multiome: The ATAC and RNA components are separately processed using the same tools as mentioned above for scATAC and scRNA. Outputs are in the `snATAC` and `snRNA` subdirectories respectively. In addition, the `ATAC.RNA.bc.map.tsv` file contains a map to link snATAC barcodes to snRNA barcodes.
DOI: 10.7303/syn52331899
title: Transcription factor stoichiometry, motif affinity and syntax regulate single-cell chromatin dynamics during fibroblast reprogramming to pluripotency
DOI: 10.5281/zenodo.10113364
title: Protocol selection process - Literature Review creating the Integrated List of Agile Practices
DOI: 10.5281/zenodo.10113363
title: Protocol selection process - Literature Review creating the Integrated List of Agile Practices
DOI: 10.5281/zenodo.10114654
title: Protocol data extraction - Literature Review creating the Integrated List of Agile Practices
DOI: 10.5281/zenodo.10114653
title: Protocol data extraction - Literature Review creating the Integrated List of Agile Practices
DOI: 10.6084/m9.figshare.24547873.v1
title: Results of Continious-time generation trajectories

description: The attachment.
DOI: 10.6084/m9.figshare.24236257.v1
title: Experimental oxygen drawdown curves of four marine invertebrate species at varying temperatures.

description: The dataset is supplement to the manuscript 'Physiological causes and biogeographic consequences of thermal optima in the hypoxia tolerance of marine ectotherms' currently under review at PLOS Biology (manuscript number 'PBIOLOGY-D-22-02537R1').Each file contains all experimental respirometry results of the respective species, i.e., measurements of oxygen concentration over time under different temperature conditions. All experimental details are provided in the manuscript and its supporting material.
DOI: 10.25345/c5df6kd69
title: MassIVE MSV000093373 - Glycosylation of immortalized cells (N-glycomics)
title: N-glycomics and proteomics of immortalized vs primary lung cancer cell lines. N-glycomics results.
DOI: 10.6084/m9.figshare.24547270
title: Biogeographic occurrence records of 14 marine species obtained from the Ocean Biodiversity Information System.

description: The dataset is supplement to the manuscript 'Physiological causes and biogeographic consequences of thermal optima in the hypoxia tolerance of marine ectotherms' currently under review at PLOS Biology (manuscript number 'PBIOLOGY-D-22-02537R1').Each file contains the Ocean Biodiversity Information System (OBIS) occurrence data of the respective species used for generating oxygen-temperature state-space diagrams in the manuscript.All further details are provided in the manuscript and its supporting material.
DOI: 10.6084/m9.figshare.24547270.v1
title: Biogeographic occurrence records of 14 marine species obtained from the Ocean Biodiversity Information System.

description: The dataset is supplement to the manuscript 'Physiological causes and biogeographic consequences of thermal optima in the hypoxia tolerance of marine ectotherms' currently under review at PLOS Biology (manuscript number 'PBIOLOGY-D-22-02537R1').Each file contains the Ocean Biodiversity Information System (OBIS) occurrence data of the respective species used for generating oxygen-temperature state-space diagrams in the manuscript.All further details are provided in the manuscript and its supporting material.
DOI: 10.5281/zenodo.10114759
title: Protocol synthesizing agile practices - Literature Review creating the Integrated List of Agile Practices
DOI: 10.5281/zenodo.10114760
title: Protocol synthesizing agile practices - Literature Review creating the Integrated List of Agile Practices
DOI: 10.25345/c58p5vm44
title: MassIVE MSV000093374 - Glycosylation of immortalized cells (proteomics)
title: N-glycomics and proteomics of immortalized vs primary lung cancer cell lines. Proteomics results.
DOI: 10.5281/zenodo.10114719
title: GNSS-IR data for "Real-time water levels using GNSS-IR: a potential tool for flood monitoring"

description: Organised SNR data used for GNSS-IR analysis in the article "Real-time water levels using GNSS-IR: a potential tool for flood monitoring" by David Purnell, Natalya Gomez, William Minarik and Gregory Langston.
 
The directories 'rv3s' and 'sjdlr' contain SNR data corresponding to sites Trois-Rivières and Saint-Joseph-de-la-Rive, respectively.
 
Software for processing the data can be found at: https://github.com/purnelldj/gnssir_rt
 
SNR data is given as text files in the format specified here except for columns 4+:
https://gnssrefl.readthedocs.io/en/latest/pages/file_structure.html#the-snr-data-format
columns:
1. sat PRN with an offset such that GLONASS satellites are between 100-200 and Galileo are between 200-300
2. satellite elevation (degrees)
3. azi is satellite azimuth (degrees)
4. GPS time (seconds since 1980)
5. L1 SNR
DOI: 10.5281/zenodo.8120958
title: GNSS-IR data for "Real-time water levels using GNSS-IR: a potential tool for flood monitoring"

description: Organised SNR data used for GNSS-IR analysis in the article "Real-time water levels using GNSS-IR: a potential tool for flood monitoring" by David Purnell, Natalya Gomez, William Minarik and Gregory Langston.
 
The directories 'rv3s' and 'sjdlr' contain SNR data corresponding to sites Trois-Rivières and Saint-Joseph-de-la-Rive, respectively.
 
Software for processing the data can be found at: https://github.com/purnelldj/gnssir_rt
 
SNR data is given as text files in the format specified here except for columns 4+:
https://gnssrefl.readthedocs.io/en/latest/pages/file_structure.html#the-snr-data-format
columns:
1. sat PRN with an offset such that GLONASS satellites are between 100-200 and Galileo are between 200-300
2. satellite elevation (degrees)
3. azi is satellite azimuth (degrees)
4. GPS time (seconds since 1980)
5. L1 SNR
DOI: 10.5281/zenodo.8120959
title: GNSS-IR data for "Real-time water levels using GNSS-IR: a potential tool for flood monitoring"

description: Organised SNR data used for GNSS-IR analysis in the article "Real-time water levels using GNSS-IR: a potential tool for flood monitoring" by David Purnell, Natalya Gomez, William Minarik and Gregory Langston. Data is organised by sites:<br> 'rv3x' is Trois-Rivières<br> 'sjrx' is Saint-Joseph-de-la-Rive<br> The 'x' at the end signifies that there is data from multiple co-located antennas at these sites (see details about naming format below**) SNR data is given as text files in the format specified here:<br> https://gnssrefl.readthedocs.io/en/latest/pages/file_structure.html#the-snr-data-format<br> Each line is coded in Python using:<br> "{0:3.0f} {1:10.4f} {2:10.4f} {3:10.0f} {4:7.2f} {5:7.2f} {6:7.2f} {7:7.2f} {8:7.2f} \n".format(sat, elv, azi, sod, 0, 0, snr, 0, 0)<br> where 'sat' is the satellite PRN identifier, with an offset such that GLONASS satellites are between 100-200 and Galileo are between 200-300<br> elv is satellite elevation (degrees)<br> azi is satellite azimuth (degrees)<br> sod is seconds of day<br> snr is L1 SNR The key difference from the gnssrefl snr data format is the naming of the files.<br> Each file is of the format:<br> sssxYYMMDDHH.snr<br> **where sssx is the station ID ('sjr' or 'rv3') + the letter a, b, c or d to indicate the four co-located antennas<br> For example,<br> 'rv3a20090917.snr' comprises of data from antenna 'a' at Trois-Rivières on 9th September 2020 at 17h (UTC)<br> 'sjrd21111302.snr' comprises of data from antenna 'd' at Saint-Joseph-de-la-Rive on 13th November 2021 at 02h (UTC)<br> Each file contains one hour of data (as opposed to 24 hours in the gnssrefl format).
DOI: 10.5281/zenodo.10114814
title: Top selling games in Steam platform

description: The dataset is extracted from the list of best-selling games available in Spanish on the Steam platform. It contains information related to the content of the game, to its development and marketing (price and discounts), as well as different metrics derived from user opinions.
DOI: 10.5281/zenodo.10114815
title: Top selling games in Steam platform

description: The dataset is extracted from the list of best-selling games available in Spanish on the Steam platform. It contains information related to the content of the game, to its development and marketing (price and discounts), as well as different metrics derived from user opinions.
DOI: 10.25345/c5513v631
title: MassIVE MSV000093375 - Organelle proteomic profiling reveals lysosomal heterogeneity in association with longevity
title: Lysosomes are active sites to integrate cellular metabolism and signal transduction. A collection of proteins enriched at lysosomes mediate these metabolic and signaling functions. Both lysosomal metabolism and lysosomal signaling have been linked with longevity regulation; however, how lysosomes adjust their protein composition to accommodate this regulation remains unclear. Using large-scale proteomic profiling, we systemically profiled lysosome-enriched proteomes in association with different longevity mechanisms. We further discovered the lysosomal recruitment of AMPK and nucleoporin proteins and their requirements for longevity in response to increased lysosomal lipolysis. Through comparative proteomic analyses of lysosomes from different tissues and labeled with different markers, we discovered lysosomal heterogeneity across tissues as well as the specific enrichment of the Ragulator complex on Cystinosin positive lysosomes. Together, this work uncovers lysosomal proteome heterogeneity at different levels and provides resources for understanding the contribution of lysosomal proteome dynamics in modulating signal transduction, organelle crosstalk and organism longevity.
DOI: 10.5281/zenodo.10114875
title: Lakedat
DOI: 10.5281/zenodo.10114876
title: Lakedat
DOI: 10.3886/e195082
title: Replication Code for: The Effects of Advertising Disclosure Regulations on Social Media: Evidence From Instagram

description: This is the replication package for "The Effects of Advertising Disclosure Regulations on Social Media: Evidence From Instagram," accepted in 2023 by the RAND Journal of Economics.
DOI: 10.3886/e195082v1
title: Replication Code for: The Effects of Advertising Disclosure Regulations on Social Media: Evidence From Instagram

description: This is the replication package for "The Effects of Advertising Disclosure Regulations on Social Media: Evidence From Instagram," accepted in 2023 by the RAND Journal of Economics.
DOI: 10.23719/1529825
title: Using Geospatial Data and Random Forest To Predict PFAS Contamination in Fish Tissue in the Columbia River Basin, United States

description: Publicly available data about potential PFAS sources and PFAS measurements in fish tissue
DOI: 10.5281/zenodo.10114888
title: ormyrus_labotus_zanfona_1.fasta.gz

description: ormyrus_labotus_zanfona_1.fasta.gz
DOI: 10.5281/zenodo.10114887
title: ormyrus_labotus_zanfona_1.fasta.gz

description: ormyrus_labotus_zanfona_1.fasta.gz
DOI: 10.5281/zenodo.10114899
title: torymus_tubicola_zanfona_1.fasta.gz

description: torymus_tubicola_zanfona_1.fasta.gz
DOI: 10.5281/zenodo.10114898
title: torymus_tubicola_zanfona_1.fasta.gz

description: torymus_tubicola_zanfona_1.fasta.gz
DOI: 10.5281/zenodo.10114907
title: galeopsomyia_nigrocyanea_zanfona_1.fasta.gz

description: galeopsomyia_nigrocyanea_zanfona_1.fasta.gz
DOI: 10.5281/zenodo.10114908
title: galeopsomyia_nigrocyanea_zanfona_1.fasta.gz

description: galeopsomyia_nigrocyanea_zanfona_1.fasta.gz
DOI: 10.6084/m9.figshare.24542695
title: Material Suplementario

description: Documentary analysis of 37 instruments published up to the year 2021, on health actions aimed at the migrant population in Chile. Through content analysis, specific elements of the rights-based approach were identified.