cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7612564
title: GRAND-SLAM analysis of mESCs treated with 4sU for 0, 0.5, 1, 3, 6, 12, and 24h from Herzog et al., Nature Methods 2017 (https://doi.org/10.1038/nmeth.4435)

description: This is the processed SLAM-seq data from the 0, 0.5, 1, 3, 6, 12, and 24h 4sU data set published in Herzog et al., Nature Methods 2017 (https://doi.org/10.1038/nmeth.4435). The zip file contains the full output from the processing pipeline (including the mapped reads, the scripts to run the pipeline and the output). The json file is required if you want to start from scratch. The GRAND-SLAM output was generated using fixed conversion rates (conv 0.025, err 0.00035). The file pulse-chase.tsv.gz is the GRAND-SLAM output table. The halflives.tsv contains the half-lives from Herzog et al. To generate the GRAND-SLAM output, first download the mus musculus genome (ensembl v90 FASTA). https://ftp.ensembl.org/pub/release-102/fasta/mus_musculus/dna/Mus_musculus.GRCm38.dna.primary_assembly.fa.gz Next index the mouse genome using the provided mESC-end.gtf file: gedi -e IndexGenome -s mus_musculus.90.fasta -a mESC-end.gtf -n mESC-ends Finally call grandslam: gedi -e Slam -trim5p 15 -chase_notrescued.cit -genomic mESC-ends -prefix grandslam_t15_fixedparam/chase_notrescued-plot -conv 0.025 -err 0.00035 -D -modelall -allGenes To generate the cit file you have to modify the first lines in start.bash to match the paths on your file system, and then run it You can also start from scratch (i.e., the json file): Prepare the mouse genome (ensembl v90) and the murine rRNA sequence<br> Run: gedi -e Pipeline -r parallel -j chase_notrescued.json rnaseq_mapping.sh report.sh grandslam.sh To create the grandslam output with fixed parameters follow the steps above. gedi toolkit 1.0.4 GRAND-SLAM 2.0.7 STAR version 2.5.3a
DOI: 10.5281/zenodo.7612563
title: GRAND-SLAM analysis of mESCs treated with 4sU for 0, 0.5, 1, 3, 6, 12, and 24h from Herzog et al., Nature Methods 2017 (https://doi.org/10.1038/nmeth.4435)

description: This is the processed SLAM-seq data from the 0, 0.5, 1, 3, 6, 12, and 24h 4sU data set published in Herzog et al., Nature Methods 2017 (https://doi.org/10.1038/nmeth.4435). The zip file contains the full output from the processing pipeline (including the mapped reads, the scripts to run the pipeline and the output). The json file is required if you want to start from scratch. The GRAND-SLAM output was generated using fixed conversion rates (conv 0.025, err 0.00035). The file pulse-chase.tsv.gz is the GRAND-SLAM output table. The halflives.tsv contains the half-lives from Herzog et al. To generate the GRAND-SLAM output, first download the mus musculus genome (ensembl v90 FASTA). https://ftp.ensembl.org/pub/release-102/fasta/mus_musculus/dna/Mus_musculus.GRCm38.dna.primary_assembly.fa.gz Next index the mouse genome using the provided mESC-end.gtf file: gedi -e IndexGenome -s mus_musculus.90.fasta -a mESC-end.gtf -n mESC-ends Finally call grandslam: gedi -e Slam -trim5p 15 -chase_notrescued.cit -genomic mESC-ends -prefix grandslam_t15_fixedparam/chase_notrescued-plot -conv 0.025 -err 0.00035 -D -modelall -allGenes To generate the cit file you have to modify the first lines in start.bash to match the paths on your file system, and then run it You can also start from scratch (i.e., the json file): Prepare the mouse genome (ensembl v90) and the murine rRNA sequence<br> Run: gedi -e Pipeline -r parallel -j chase_notrescued.json rnaseq_mapping.sh report.sh grandslam.sh To create the grandslam output with fixed parameters follow the steps above. gedi toolkit 1.0.4 GRAND-SLAM 2.0.7 STAR version 2.5.3a
DOI: 10.5281/zenodo.7630887
title: GRAND-SLAM analysis of mESCs treated with 4sU for 0, 0.75, 3, 12 h from Herzog et al., Nature Methods 2017 (https://doi.org/10.1038/nmeth.4435)

description: This is the processed SLAM-seq data from the progressive labeling data set 0, 0.75, 3, 12 h published in Herzog et al., Nature Methods 2017 (https://doi.org/10.1038/nmeth.4435). The zip file contains the full output from the processing pipeline (including the mapped reads, the scripts to run the pipeline and the output). The json file is required if you want to start from scratch. The GRAND-SLAM output was generated using fixed conversion rates (conv 0.025, err 0.00035). The file progressive.tsv.gz is the GRAND-SLAM output table. <br> To generate the GRAND-SLAM output, first download the mus musculus genome (ensembl v90 FASTA). https://ftp.ensembl.org/pub/release-102/fasta/mus_musculus/dna/Mus_musculus.GRCm38.dna.primary_assembly.fa.gz Next index the mouse genome using the here provided mESC-end.gtf file: gedi -e IndexGenome -s mus_musculus.90.fasta -a mESC-end.gtf -n mESC-ends Finally call grandslam: gedi -e Slam -trim5p 15 -reads progressive.cit -genomic mESC-ends -prefix grandslam_t15_fixedparam/progressive -plot -conv 0.025 -err 0.00035 -D -modelall -allGenes To generate the cit file you have to modify the first lines in start.bash to match the paths on your file system, and then run it. You can also start from scratch (i.e., the json file): Prepare the mouse genome (ensembl v90) and the murine rRNA sequence<br> Run: gedi -e Pipeline -r parallel -j progressive.json rnaseq_mapping.sh report.sh grandslam.sh To create the grandslam output with fixed parameters follow the steps above. gedi toolkit 1.0.4 GRAND-SLAM 2.0.7 STAR version 2.5.3a
DOI: 10.5281/zenodo.7630886
title: GRAND-SLAM analysis of mESCs treated with 4sU for 0, 0.75, 3, 12 h from Herzog et al., Nature Methods 2017 (https://doi.org/10.1038/nmeth.4435)

description: This is the processed SLAM-seq data from the progressive labeling data set 0, 0.75, 3, 12 h published in Herzog et al., Nature Methods 2017 (https://doi.org/10.1038/nmeth.4435). The zip file contains the full output from the processing pipeline (including the mapped reads, the scripts to run the pipeline and the output). The json file is required if you want to start from scratch. The GRAND-SLAM output was generated using fixed conversion rates (conv 0.025, err 0.00035). The file progressive.tsv.gz is the GRAND-SLAM output table. <br> To generate the GRAND-SLAM output, first download the mus musculus genome (ensembl v90 FASTA). https://ftp.ensembl.org/pub/release-102/fasta/mus_musculus/dna/Mus_musculus.GRCm38.dna.primary_assembly.fa.gz Next index the mouse genome using the here provided mESC-end.gtf file: gedi -e IndexGenome -s mus_musculus.90.fasta -a mESC-end.gtf -n mESC-ends Finally call grandslam: gedi -e Slam -trim5p 15 -reads progressive.cit -genomic mESC-ends -prefix grandslam_t15_fixedparam/progressive -plot -conv 0.025 -err 0.00035 -D -modelall -allGenes To generate the cit file you have to modify the first lines in start.bash to match the paths on your file system, and then run it. You can also start from scratch (i.e., the json file): Prepare the mouse genome (ensembl v90) and the murine rRNA sequence<br> Run: gedi -e Pipeline -r parallel -j progressive.json rnaseq_mapping.sh report.sh grandslam.sh To create the grandslam output with fixed parameters follow the steps above. gedi toolkit 1.0.4 GRAND-SLAM 2.0.7 STAR version 2.5.3a
DOI: 10.5281/zenodo.7667084
title: The Daily Fact Pile: A Tool for Mutual Microlearning in Neurology Residency

description: Description of mutual microlearning tool in Neurology residency
DOI: 10.5281/zenodo.7667083
title: The Daily Fact Pile: A Tool for Mutual Microlearning in Neurology Residency

description: Description of mutual microlearning tool in Neurology residency
DOI: 10.7931/0z1q-nh09
title: Capture-recapture of ship rat (Rattus rattus) in North Island, New Zealand
DOI: 10.7931/tzr8-r108
title: Capture-recapture of ship rat (Rattus rattus) in North Island, New Zealand
DOI: 10.25376/hra.22144160
title: S-DKO mouse protein expression (pctp, them2) validation

description: Western Blotting dataset in validating the expression of proteins, PCTP and Them2, of S-DKO (Myf5Cre+/pctp<sup>flox/flox</sup> /Them2<sup>flox/flox</sup>) mice.
DOI: 10.25376/hra.22144160.v1
title: S-DKO mouse protein expression (pctp, them2) validation

description: Western Blotting dataset in validating the expression of proteins, PCTP and Them2, of S-DKO (Myf5Cre+/pctp<sup>flox/flox</sup> /Them2<sup>flox/flox</sup>) mice.
DOI: 10.5066/f7kk9b09
title: Datasets of the hydrogeologic framework of the Red River alluvial aquifer and Carrizo-Wilcox aquifer in northwestern Louisiana

description: Datasets include hydrogeologic cross section wells within the Red River alluvial aquifer surficial extent, Red River alluvial aquifer base contours and wells, isopach map showing combined thickness of the Red River alluvial aquifer sand and gravel units, wells used to construct the isopach map of the Red River alluvial aquifer, wells used to construct 25-foot contours showing altitude of the base of the Red River alluvial aquifer, cross-section wells within the Carrizo-Wilcox aquifer, thickness of the Carrizo-Wilcox aquifer contours and well points, contours showing the altitude of the base of freshwater of the Carrizo-Wilcox aquifer, outcrop extent of the Carrizo-Wilcox aquifer outcrop, and the approximate down-dip limit of freshwater. Well data were obtained from previous reports, geophysical logs, and driller's lithologic logs. Maps generated from these datasets are presented in "Hydrogeologic framework of the Red River alluvial aquifer and Carrizo-Wilcox aquifer in northwestern Louisiana" (Hays and others, 2021).
DOI: 10.5519/qd.z0fhua4a
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1247 records)
DOI: 10.21227/pagk-a077
title: Bistatic MIMO Radar Sensing of Specularly Reflecting Surfaces for Wireless Power Transfer

description: Bistatic MIMO Radar Sensing This dataset includes the synthetic aperture measurement data accompanying the publication "Bistatic MIMO Radar Sensing of Specularly Reflecting Surfaces for Wireless Power Transfer" [1]. Description: The measurement data VNA_20220722_232002_XETS_reduced.mat includes a channel matrix R acquired with a synthetic aperture measurement testbed described in [2] and [3]. Measured were Nf=1000 frequency steps in a band from 3-10GHz of the scattering parameter S21 between a synthetic 51-ULA with antenna positions saved in the file ULA.mat and a synthetic (13x13)-URA with antenna positions saved in the file URA.mat. The file XETSantennaCharacterization.mat holds antenna gains of an XETS antenna [4] characterized in an anechoic chamber. These data have been used to perform ultra-wideband (UWB) bistatic radar imaging and wireless power transfer (WPT). We invite readers to look at the tasks we have solved with the given dataset in [1] and encourage them to implement their own methods. References (unpublished work will appear under https://reindeer-project.eu/results-downloads/ ) [1] B. J.B. Deutschmann, M. Graber, T. wilding, K. Witrisal, "Bistatic MIMO Radar Sensing of Specularly Reflecting Surfaces for Wireless Power Transfer", 2023 (unpublished). [2] REINDEER Project, “Propagation characteristics and channel models for RadioWeaves including reflectarrays,” Deliverable ICT-52-2020 / D1.2, 2023 (unpublished). [3] REINDEER Project, “System design study for energy-neutral devices interacting with the RadioWeaves infrastructure,” Deliverable ICT-52-2020 / D4.1, 2023 (unpublished). [4] J. R. Costa, C. R. Medeiros, and C. A. Fernandes, “Performance of a crossed exponentially tapered slot antenna for UWB systems,” IEEE Trans. Antennas Propag., vol. 57, no. 5, pp. 1345–1352, May 2009.
DOI: 10.6075/j01g0jhx
title: EPPL01WT East Pacific Longline Expedition Leg 01 - Cruise Report. In Data from Scripps Research Expeditions 1953-2005

description: East pacific longline - seismic reflection/refraction work
DOI: 10.3886/e185381
title: Replication File For The Physical and Mental Health Returns of Head Start 25 Years After Participation: Evidence from Income Eligibility Cutoffs

description: Head Start, the largest early childhood intervention to date, has affected the health<br>and welfare of many children. Most studies that look at long-term effects of Head Start<br>exploit its rollout in the 1960s, missing the effects of many changes to Head Start in<br>the 1980s and 1990s: full-year programming, added nutritional components, integra-<br>tion of children with disabilities, and addition of educational and social services for<br>families. I study the effects of Head Start on long-term physical and mental health for<br>children who attended Head Start during this critical period of changes and are now<br>labor market entrants in their late 20s. I find large improvements in physical and men-<br>tal health for labor market entrants, resulting in a 0.15 standard deviation decrease in<br>the incidence of poor health. While I find small impacts on economic outcomes, there<br>are large decreases in food stamp usage later in life. Ultimately, Head Start improves<br>health outcomes up to 25 years after participation in the program.
DOI: 10.5066/p9fv6nxz
title: USGS Gulf Coast Petroleum Systems and National and Global Oil and Gas Assessment Projects - U.S. Gulf Coast Downdip Paleogene Formations 2017 Assessment Unit Boundaries and Assessment Input Forms

description: The Assessment Unit is the fundamental unit used in the National Assessment Project for the assessment of undiscovered oil and gas resources. The Assessment Unit is defined within the context of the higher-level Total Petroleum System. The Assessment Unit is shown herein as a geographic boundary interpreted, defined, and mapped by the geologist responsible for the province and incorporates a set of known or postulated oil and (or) gas accumulations sharing similar geologic, geographic, and temporal properties within the Total Petroleum System, such as source rock, timing, migration pathways, trapping mechanism, and hydrocarbon type. The Assessment Unit boundary is defined geologically as the limits of the geologic elements that define the Assessment Unit, such as limits of reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are Assessment Units that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the Assessment Unit boundary.
DOI: 10.5281/zenodo.7048470
title: Skydiving Salamander CFD

description: The following is a written description of all data and files related to the manuscript submitted for publication titled: How Salamanders Skydive: Characterizing Lift, Drag, and Pressure Differences across Wandering Salamanders (Aneides vagrans) with Computational Fluid Dynamics. This data was collected by Christian E. Brown and Alexander M. Kirk at the University of South Florida using the Access 3D Lab between January and August of 2022.
description: Please contact @cbrown43@usf.edu with any questions or requests for additional data.
DOI: 10.5281/zenodo.7048471
title: Skydiving Salamander CFD

description: The following is a written description of all data and files related to the manuscript submitted for publication titled: How Salamanders Skydive: Characterizing Lift, Drag, and Pressure Differences across Wandering Salamanders (Aneides vagrans) with Computational Fluid Dynamics. This data was collected by Christian E. Brown and Alexander M. Kirk at the University of South Florida using the Access 3D Lab between January and August of 2022.
description: Please contact @cbrown43@usf.edu with any questions or requests for additional data.
DOI: 10.5281/zenodo.7666889
title: Skydiving Salamander CFD

description: The following is a written description of all data and files related to the manuscript submitted for publication titled: How Salamanders Skydive: Characterizing Lift, Drag, and Pressure Differences across Wandering Salamanders (Aneides vagrans) with Computational Fluid Dynamics. This data was collected by Christian E. Brown and Alexander M. Kirk at the University of South Florida using the Access 3D Lab between January and August of 2022.
description: Please contact @cbrown43@usf.edu with any questions or requests for additional data.
DOI: 10.5519/qd.5znmd7l5
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (8845 records)
DOI: 10.5281/zenodo.7667044
title: Gaussian Process Model and Sensor Placement for Detroit Green Infrastructure: Datasets and Code

description: <strong>code.zip: </strong>Zip folder containing a folder titled "code" which holds: csv file titled "MonitoredRainGardens.csv" containing the 14 monitored green infrastructure (GI) sites with their design and physiographic features; csv file titled "storm_constants.csv" which contain the computed decay constants for every storm in every GI during the measurement period; csv file titled "newGIsites_AllData.csv" which contain the other 130 GI sites in Detroit and their design and physiographic features; csv file titled "Detroit_Data_MeanDesignFeatures.csv" which contain the design and physiographic features for all of Detroit; Jupyter notebook titled "GI_GP_SensorPlacement.ipynb" which provides the code for training the GP models and displaying the sensor placement results; a folder titled "MATLAB" which contains the following: folder titled "SFO" which contains the SFO toolbox for the sensor placement work file titled "sensor_placement.mlx" that contains the code for the sensor placement work several .mat files created in Python for importing into Matlab for the sensor placement work: "constants_sigma.mat", "constants_coords.mat", "GInew_sigma.mat", "GInew_coords.mat", and "R1_sensor.mat" through "R6_sensor.mat" several .mat files created in Matalb for importing into Python for visualizing the results: "MI_DETselectedGI.mat" and "DETselectedGI.mat"
description: This project is a continuation of the project described here: https://zenodo.org/record/7636149#.Y_ZvES-B1hF .
DOI: 10.5281/zenodo.7667043
title: Gaussian Process Model and Sensor Placement for Detroit Green Infrastructure: Datasets and Code

description: <strong>code.zip: </strong>Zip folder containing a folder titled "code" which holds: csv file titled "MonitoredRainGardens.csv" containing the 14 monitored green infrastructure (GI) sites with their design and physiographic features; csv file titled "storm_constants.csv" which contain the computed decay constants for every storm in every GI during the measurement period; csv file titled "newGIsites_AllData.csv" which contain the other 130 GI sites in Detroit and their design and physiographic features; csv file titled "Detroit_Data_MeanDesignFeatures.csv" which contain the design and physiographic features for all of Detroit; Jupyter notebook titled "GI_GP_SensorPlacement.ipynb" which provides the code for training the GP models and displaying the sensor placement results; a folder titled "MATLAB" which contains the following: folder titled "SFO" which contains the SFO toolbox for the sensor placement work file titled "sensor_placement.mlx" that contains the code for the sensor placement work several .mat files created in Python for importing into Matlab for the sensor placement work: "constants_sigma.mat", "constants_coords.mat", "GInew_sigma.mat", "GInew_coords.mat", and "R1_sensor.mat" through "R6_sensor.mat" several .mat files created in Matalb for importing into Python for visualizing the results: "MI_DETselectedGI.mat" and "DETselectedGI.mat"
description: This project is a continuation of the project described here: https://zenodo.org/record/7636149#.Y_ZvES-B1hF .
DOI: 10.5281/zenodo.7301648
title: Social signal learning of referential communication in a social insect

description: This is the dataset for a paper showing that honey bees can use social learning to improve their waggle dancing.
description: Honey bees use social signal learning to improve their ability to waggle dance, the most complex example of non-human spatial referential communication known.
DOI: 10.5281/zenodo.7301647
title: Social signal learning of referential communication in a social insect

description: This is the dataset for a paper showing that honey bees can use social learning to improve their waggle dancing.
description: Honey bees use social signal learning to improve their ability to waggle dance, the most complex example of non-human spatial referential communication known.
DOI: 10.6084/m9.figshare.22144241
title: ENCFF889EMW.bigWig

description: rara
DOI: 10.6084/m9.figshare.22144241.v1
title: ENCFF889EMW.bigWig

description: rara
DOI: 10.25345/c5mk65j6c
title: MassIVE MSV000091346 - Raw Data for: LipidOz Enables Automated Elucidation of Lipid Carbon-Carbon Double Bond Positions from Ozone-Induced Dissociation Mass Spectrometry Data
title: All raw data for: LipidOz Enables Automated Elucidation of Lipid Carbon-Carbon Double Bond Positions from Ozone-Induced Dissociation Mass Spectrometry Data. This is a collection of LC-OzID-IMS-MS/MS data for deuterated lipid standards (SPLASH and Ultimate SPLASH, Avanti), as well as porcine brain total lipid extract and a set of human tissue lipid extracts.
DOI: 10.3886/e104781v2-132922
title: Identifying Extensions of Protection in Prescription Drugs: Navigating the Data Landscape for Large-Scale Analysis

description: Patents and exclusivities have become an increasingly important component in the “life cycle management” strategies that are employed by pharmaceutical companies to prolong the period of market monopoly for their drugs.<br><br>Presenting the first comprehensive study of evergreening, this study examines all pharmaceuticals in the U.S. the market between 2005 and 2015, collecting 160,000 data points to note every instance in which a company added a new patent or exclusivity. This study gathers patent and exclusivity information published in the “Orange Book,” which is a regular FDA publication containing information on approved drugs, their therapeutically equivalent generics, and the patents and exclusivities attached to approved drugs, among other drug-related data.<br>
DOI: 10.6084/m9.figshare.24181044.v1
title: Additional file 5 of Different states of stemness of glioblastoma stem cells sustain glioblastoma subtypes indicating novel clinical biomarkers and high-efficacy customized therapies

description: Additional file 5.
DOI: 10.6084/m9.figshare.24181038
title: Additional file 3 of Different states of stemness of glioblastoma stem cells sustain glioblastoma subtypes indicating novel clinical biomarkers and high-efficacy customized therapies

description: Additional file 3.
DOI: 10.5281/zenodo.10155286
title: NorSand4AI: A Comprehensive Triaxial Test Simulation Database for NorSand Constitutive Model Materials

description: Dataset of the homonymous paper: a first-of-its-kind database that addresses the size and complexity issues of creating synthetic datasets for nonlinear constitutive modeling of soils by simulating both drained and undrained triaxial tests. Two datasets are provided: the first one considers a nested Latin Hypercube Sampling of input parameters encompassing 2000 soil types, each subjected to 40 initial test configurations, resulting in a total of 160000 triaxial test results (NorSandTXL_H5.zip). The second one considers nested quasi-Monte Carlos sampling techniques (Sobol and Halton) of input parameters encompassing 2048 soil types, each subjected to 42 initial test configurations, resulting in a total of 172032 triaxial test results (NorSand_2048_42_Drained.zip and NorSand_2048_42_Undrained.zip). Each simulation dataset comprises a 4000 by 10 matrix that can be used for general multivariate forecasting benchmarks, in addition to direct geotechnical and soil science applications.