cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3886/icpsr38820
title: Annual Survey of Jails in Indian Country, 2022
title: Archival Version

description: The purpose of the Survey of Jails in Indian Country is an enumeration of all known adult and juvenile facilities -- jails, confinement facilities, detention centers, and other correctional facilities operated by tribal authorities or the Bureau of Indian Affairs (BIA), U.S. Department of the Interior. For the purpose of this collection, Indian country includes reservations, pueblos, rancherias, and other Native American and Alaska Native communities throughout the United States. The survey collects data on the number of adults and juveniles held on the last weekday in June 2022, type of offense, average daily population in June, most crowded day in June, admissions and releases in June, number of inmate deaths and suicide attempts from July 1, 2021 through June 30, 2022, rated capacity on June 30, and jail staffing on June 30, 2022.
description: None.
description: Datasets: DS0: Study-Level Files DS1: Public-Use Data DS2: Restricted-Use Data
description: Adult and juvenile facilities -- jails, confinement facilities, detention centers, and other correctional facilities in Indian country operated by tribal authorities or the Bureau of Indian Affairs (BIA), U.S. Department of the Interior in the United States in 2022. Smallest Geographic Unit: City
description: self-enumerated questionnaire; telephone interview
DOI: 10.34740/kaggle/dsv/5718132
title: USGS New Jersey Streamgages May 2023
DOI: 10.6084/m9.figshare.22939415
title: Tools to Automate Refactoring to Microservices

description: This spreadsheet contains the rapid review results and tool inspection details for the study "Tools to Automate Refactoring to Microservices: A Preliminary Usability Report".
DOI: 10.6084/m9.figshare.22939415.v1
title: Tools to Automate Refactoring to Microservices

description: This spreadsheet contains the rapid review results and tool inspection details for the study "Tools to Automate Refactoring to Microservices: A Preliminary Usability Report".
DOI: 10.57766/ipen/8d5qhh
title: Desenvolvimento de um procedimento analítico-comportamental baseado em estudos de persuasão em mídias sociais visando sua aplicação na promoção de Cultura de Segurança Física Nuclear - Experimento 2

description: Dados coletados no segundo experimento da tese - "Experimento 2 - Coerência em Implicações Combinatórias de Alta Derivação".
DOI: 10.34740/kaggle/dsv/5718148
title: USGS New Mexico Streamgages May 2023
DOI: 10.25740/ss311gz1992
title: David Rumsey Map Collection: Georeferenced Maps Metadata

description: This subset of the metadata from the David Rumsey Map Collection was created for the collaboration between David Rumsey and Machines Reading Maps. luna_omo_metadata_56628_20220724.zip contains metadata sourced from both Luna Imaging and Old Maps Online on 24 July 2022 for all georeferenced maps in the David Rumsey Map Collection (~57k records). normalized_luna_omo_metadata_20230405.zip contains a more user friendly version of the same metadata, unpacking some of the nested fields in the previous file. Please consult the README for details about the transformations used for the normalized file.
DOI: 10.34740/kaggle/dsv/5718164
title: USGS Nevada Streamgages May 2023
DOI: 10.25740/vn901vj0926
title: David Rumsey Map Collection Text on Maps Data [Version 1]

description: This data package contains datasets of text on maps recognized from a georeferenced subset of ~57k historical maps from the David Rumsey Map Collection. It includes files for different versions of the dataset accompanied by Data Sheets and this Cover Sheet. Each dataset was created with the \textit{mapKurator} software library and contains all instances of text as it was automatically identified on a single, physical map sheet, or a composite of sheets. Each text identified by \textit{mapKurator} is saved as a polygon json `Feature` with geospatial coordinates for 16 vertices and has the following properties: `text` (the \textit{mapKurator} predicted transcription of the text within the polygon), `score` (the confidence score for the text detection), `postocr\_label` (a prediction for a normalized version of `text` that is based on a post-processing step), and `img\_coordinates` (the pixel coordinates of the polygon). This data has not been verified or evaluated in any way and will contain significant errors, both in terms of false positives (map features recognized as text, but which are not in fact text), (missing) false negatives (text that \textit{mapKurator} has failed to recognize), incorrect detection (bounding polygons that do not correctly enclose a word), and incorrect recognition (the transcription of the text contains errors). Please see the Data Sheet for each version for more details.
DOI: 10.34740/kaggle/dsv/5718174
title: USGS Ohio Streamgages May 2023
DOI: 10.34740/kaggle/dsv/5718191
title: USGS Oklahoma Streamgages May 2023
DOI: 10.34740/kaggle/dsv/5718201
title: USGS Oregon Streamgages May 2023
DOI: 10.34740/kaggle/dsv/5718215
title: USGS Pennsylvania Streamgages May 2023
DOI: 10.34740/kaggle/dsv/5718228
title: USGS Rhode Island Streamgages May 2023
DOI: 10.34740/kaggle/dsv/5718243
title: USGS South Carolina Streamgages May 2023
DOI: 10.6084/m9.figshare.14569007
title: Transcriptomics for radiation biodosimetry: progress and challenges

description: Transcriptomic-based approaches are being developed to meet the needs for large-scale radiation dose and injury assessment and provide population triage following a radiological or nuclear event. This review provides background and definition of the need for new biodosimetry approaches, and summarizes the major advances in this field. It discusses some of the major model systems used in gene signature development, and highlights some of the remaining challenges, including individual variation in gene expression, potential confounding factors, and accounting for the complexity of realistic exposure scenarios. Transcriptomic approaches show great promise for both dose reconstruction and for prediction of individual radiological injury. However, further work will be needed to ensure that gene expression signatures will be robust and appropriate for their intended use in radiological or nuclear emergencies.
DOI: 10.6084/m9.figshare.14569007.v2
title: Transcriptomics for radiation biodosimetry: progress and challenges

description: Transcriptomic-based approaches are being developed to meet the needs for large-scale radiation dose and injury assessment and provide population triage following a radiological or nuclear event. This review provides background and definition of the need for new biodosimetry approaches, and summarizes the major advances in this field. It discusses some of the major model systems used in gene signature development, and highlights some of the remaining challenges, including individual variation in gene expression, potential confounding factors, and accounting for the complexity of realistic exposure scenarios. Transcriptomic approaches show great promise for both dose reconstruction and for prediction of individual radiological injury. However, further work will be needed to ensure that gene expression signatures will be robust and appropriate for their intended use in radiological or nuclear emergencies.
DOI: 10.5281/zenodo.7949150
title: forpus_modestus_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.7949149
title: forpus_modestus_spades_01.fasta.gz

description: .
DOI: 10.34740/kaggle/dsv/5718253
title: USGS South Dakota Streamgages May 2023
DOI: 10.5066/p9vfdid0
title: Drilling, construction, geophysical, water quality, and aquifer test data for well SEP 16, Butte County, Idaho

description: In 2021 and 2022, the U.S. Geological Survey (USGS), in cooperation with the Idaho Department of Water Resources (IDWR), drilled and constructed well SEP 16 (431900112593601) approximately 6 miles south-southeast of Big Southern Butte in Butte County, Idaho. The purpose of the well installation was to collect geologic, geophysical, and hydrologic data. On July 13, 2022, the USGS Idaho National Laboratory Project Office (INLPO) collected select geophysical logs within the cased well including natural gamma, neutron, neutron porosity, and gamma-gamma dual density logs. Gyroscopic deviation data were also collected during geophysical surveying to account for the horizontal and vertical displacement of the well. On November 8, 2022, a single well aquifer test was conducted on one well, SEP 16, completed within the eastern Snake River Plain (ESRP) aquifer. Results from this test were used to determine monitor well production capacity and average transmissivity of rocks within the vicinity of the well. Immediately following the aquifer test, groundwater samples were collected and processed following standard USGS protocol (Bartholomay and others, 2021; U.S. Geological Survey, variously dated) and analyzed for specific conductance, pH, dissolved oxygen, alkalinity, tubidity, nutrients, anions, cations, volatile organic compounds (VOCs), trace elements, mercury, deuterium/protium, select radionuclides, and gross alpha and beta radioactivity. Citations: Bartholomay, R.C., Maimer, N.V., Wehnke, A.J., and Helmuth, S.L., 2021, Field methods, quality-assurance, and data management plan for water-quality activities and water-level measurements, Idaho National Laboratory, Idaho: U.S. Geological Survey Open-File Report 2021-1004, 76 p., https://doi.org/10.3133/ofr20211004. U.S Geological Survey, [variously dated], National field manual for the collection of water-quality data: U.S. Geological Survey Techniques of Water-Resources Investigations, book 9, chaps. A1-A9, https://10.3133/twri09A5.
DOI: 10.34740/kaggle/dsv/5718291
title: USGS Tennessee Streamgages May 2023
DOI: 10.5281/zenodo.7949172
title: Underlying data for Evaluation of the effect of Modern Sulfonylureas on Oxidative Stress and Hepatorenal Function among Type 2 Diabetic Patients beyond Glycemic Control: an observational study

description: Underlying data for Evaluation of the effect of Modern Sulfonylureas on Oxidative Stress and Hepatorenal Function among Type 2 Diabetic Patients beyond Glycemic Control: an observational study
DOI: 10.5281/zenodo.7949171
title: Underlying data for Evaluation of the effect of Modern Sulfonylureas on Oxidative Stress and Hepatorenal Function among Type 2 Diabetic Patients beyond Glycemic Control: an observational study

description: Underlying data for Evaluation of the effect of Modern Sulfonylureas on Oxidative Stress and Hepatorenal Function among Type 2 Diabetic Patients beyond Glycemic Control: an observational study
DOI: 10.34740/kaggle/dsv/5718297
title: USGS Utah Streamgages May 2023
DOI: 10.34740/kaggle/dsv/5718310
title: USGS Virginia Streamgages May 2023
DOI: 10.6084/m9.figshare.22940771
title: Tabla.png

description: Tabla para demostrar los resultados de busqueda.
DOI: 10.6084/m9.figshare.22940771.v2
title: Tabla.png

description: Tabla para demostrar los resultados de busqueda.
DOI: 10.6084/m9.figshare.22940771.v1
title: Tabla 1.png

description: Tabla para demostrar los resultados de busqueda.
DOI: 10.5281/zenodo.7926126
title: SpliceAI_rocksdb_hg38_chr5

description: SpliceAI RocksDB for chromosome 5 of hg38 as used in the AbSplice publication: https://www.nature.com/articles/s41588-023-01373-3 Precomputed SpliceAI scores for all SNVs and indels up to 4 nucleotides are stored in this database. To use this database for fast computation of SpliceAI predictions see: https://github.com/gagneurlab/spliceai_rocksdb This is also implemented in the AbSplice package: https://github.com/gagneurlab/absplice This dataset includes SpliceAI scores. The scores are free for academic and not-for-profit use; other use requires a commercial license from Illumina, Inc., see the GitHub repository of SpliceAI: https://github.com/Illumina/SpliceAI/tree/master
DOI: 10.5281/zenodo.7926125
title: SpliceAI_rocksdb_hg38_chr5

description: SpliceAI RocksDB for chromosome 5 of hg38 as used in the AbSplice publication: https://www.nature.com/articles/s41588-023-01373-3 Precomputed SpliceAI scores for all SNVs and indels up to 4 nucleotides are stored in this database. To use this database for fast computation of SpliceAI predictions see: https://github.com/gagneurlab/spliceai_rocksdb This is also implemented in the AbSplice package: https://github.com/gagneurlab/absplice This dataset includes SpliceAI scores. The scores are free for academic and not-for-profit use; other use requires a commercial license from Illumina, Inc., see the GitHub repository of SpliceAI: https://github.com/Illumina/SpliceAI/tree/master
DOI: 10.5281/zenodo.7926149
title: SpliceAI_rocksdb_hg38_chr9

description: SpliceAI RocksDB for chromosome 9 of hg38 as used in the AbSplice publication: https://www.nature.com/articles/s41588-023-01373-3 Precomputed SpliceAI scores for all SNVs and indels up to 4 nucleotides are stored in this database. To use this database for fast computation of SpliceAI predictions see: https://github.com/gagneurlab/spliceai_rocksdb This is also implemented in the AbSplice package: https://github.com/gagneurlab/absplice This dataset includes SpliceAI scores. The scores are free for academic and not-for-profit use; other use requires a commercial license from Illumina, Inc., see the GitHub repository of SpliceAI: https://github.com/Illumina/SpliceAI/tree/master
DOI: 10.5281/zenodo.7926148
title: SpliceAI_rocksdb_hg38_chr9

description: SpliceAI RocksDB for chromosome 9 of hg38 as used in the AbSplice publication: https://www.nature.com/articles/s41588-023-01373-3 Precomputed SpliceAI scores for all SNVs and indels up to 4 nucleotides are stored in this database. To use this database for fast computation of SpliceAI predictions see: https://github.com/gagneurlab/spliceai_rocksdb This is also implemented in the AbSplice package: https://github.com/gagneurlab/absplice This dataset includes SpliceAI scores. The scores are free for academic and not-for-profit use; other use requires a commercial license from Illumina, Inc., see the GitHub repository of SpliceAI: https://github.com/Illumina/SpliceAI/tree/master
DOI: 10.34740/kaggle/dsv/5718328
title: USGS Vermont Streamgages May 2023
DOI: 10.5281/zenodo.7926145
title: SpliceAI_rocksdb_hg38_chr8

description: SpliceAI RocksDB for chromosome 8 of hg38 as used in the AbSplice publication: https://www.nature.com/articles/s41588-023-01373-3 Precomputed SpliceAI scores for all SNVs and indels up to 4 nucleotides are stored in this database. To use this database for fast computation of SpliceAI predictions see: https://github.com/gagneurlab/spliceai_rocksdb This is also implemented in the AbSplice package: https://github.com/gagneurlab/absplice This dataset includes SpliceAI scores. The scores are free for academic and not-for-profit use; other use requires a commercial license from Illumina, Inc., see the GitHub repository of SpliceAI: https://github.com/Illumina/SpliceAI/tree/master
DOI: 10.5281/zenodo.7926144
title: SpliceAI_rocksdb_hg38_chr8

description: SpliceAI RocksDB for chromosome 8 of hg38 as used in the AbSplice publication: https://www.nature.com/articles/s41588-023-01373-3 Precomputed SpliceAI scores for all SNVs and indels up to 4 nucleotides are stored in this database. To use this database for fast computation of SpliceAI predictions see: https://github.com/gagneurlab/spliceai_rocksdb This is also implemented in the AbSplice package: https://github.com/gagneurlab/absplice This dataset includes SpliceAI scores. The scores are free for academic and not-for-profit use; other use requires a commercial license from Illumina, Inc., see the GitHub repository of SpliceAI: https://github.com/Illumina/SpliceAI/tree/master
DOI: 10.26208/6y59-0r80
title: The Convection Initiation Nowcasting Deep Learning Dataset v1.0

description: The GOES-16 Convective Initiation dataset version 1.0 contains channel 8-14 infrared brightness temperatures at lead times from 60 minutes to 10 minutes every 10 minutes over the Great Plains area. These data are used to train and test a physically explainable deep learning neural network to predict convection initiation. The data is stored in local patches surrounding each convection initiation (CI), near miss (NM), and randomly selected (RM) event. The 'train' directory contains the training dataset from June and July in 2020 while the 'test' directory contains the testing dataset from June in 2021. This dataset is downloadable as netCDF files, and is documented in a companion paper: Fan, D., S. J. Greybush, D. J. Gagne, and E. E. Clothiaux, 2023: Physically Explainable Deep Learning for Convective Initiation Nowcasting Using GOES-16 Satellite Observations.
DOI: 10.34740/kaggle/dsv/5718343
title: USGS Washington Streamgages May 2023
DOI: 10.34740/kaggle/dsv/5718357
title: USGS Wisconsin Streamgages May 2023
DOI: 10.25919/xq0b-4f72
title: Parkes observations for project P1101 semester 2023APRS_02

description: We propose to carry out fortnightly monitoring of a very active repeater, FRB~190520, with the Parkes UWL system. Our current observations show that FRB~190520 is not only the most active FRB that can be observed with Parkes, but also shows extreme rotation measure (RM) variations and unique behavior at 2 to 3 GHz frequency. This makes UWL the ideal instrument to carry out in-depth studies of this repeating FRB. With this program, we expect to detect a large sample of bursts from FRB~190520, and in collaboration with the FAST telescope, we will study the local environment, emission properties, fluence distributions, and polarisation properties over an extremely wide bandwidth with high sensitivity.
DOI: 10.5281/zenodo.7949130
title: Change in H2O levels of a milliQ ice sample as a result of laser ablation (50kHz, up to 43uJ)

description: The change in measured water vapour corresponding to 19 and 30 laser ablated craters in a milliQ ice sample. Relevant time period is 2021-05-13 from 16:07 to 16:52.<br> <br> File LA_data_2021-05-13 10.29.58.456138++0200.csv includes data of the laser used for ablation as well as position and speed data of the linear stage that holds the ice sample. <br> <br> File HIDS2159-20210513-150816Z-DataLog_User.dat includes H2O data.
DOI: 10.5281/zenodo.7949129
title: Change in H2O levels of a milliQ ice sample as a result of laser ablation (50kHz, up to 43uJ)

description: The change in measured water vapour corresponding to 19 and 30 laser ablated craters in a milliQ ice sample. Relevant time period is 2021-05-13 from 16:07 to 16:52.<br> <br> File LA_data_2021-05-13 10.29.58.456138++0200.csv includes data of the laser used for ablation as well as position and speed data of the linear stage that holds the ice sample. <br> <br> File HIDS2159-20210513-150816Z-DataLog_User.dat includes H2O data.
DOI: 10.34740/kaggle/dsv/5718363
title: USGS West Virginia Streamgages May 2023
DOI: 10.5281/zenodo.7946527
title: Change in H2O levels of a milliQ ice sample as a result of laser ablation (100kHz, 50uJ)

description: This data includes the measured change in water vapour level corresponding to three different laser ablation durations applied to milliQ ice sample. Pulse energy of the laser is 50 uJ and repetition rate is 100kHz. Datetime Laser Ablation Duration (s) 2019-12-09 11:28 20 2019-12-09 11:33 40 2019-12-09 11:40 60
DOI: 10.5281/zenodo.7946526
title: Change in H2O levels of a milliQ ice sample as a result of laser ablation (100kHz, 50uJ)

description: This data includes the measured change in water vapour level corresponding to three different laser ablation durations applied to milliQ ice sample. Pulse energy of the laser is 50 uJ and repetition rate is 100kHz. Datetime Laser Ablation Duration (s) 2019-12-09 11:28 20 2019-12-09 11:33 40 2019-12-09 11:40 60
DOI: 10.5066/p9ekzr4c
title: Growth, Drought Response, and Genomic Structure Data for Whitebark Pine in the Sierra Nevada of California (ver. 2.0, May 2023)

description: These data represent stem growth from whitebark pine at 27 sites in the Sierra Nevada of California. Values for stem growth were derived from increment cores, processed following standard methods. Samples were also compared against a genomic data collected at the same trees. These data support the following publication: van Mantgem, P.J., Milano, E.R., Dudney, J., Nesmith, J.C.B, Vandergast, A.G., and Zald, H.S.J., 2023. Growth, drought response, and climate-associated genomic structure in whitebark pine in the Sierra Nevada of California. Ecology and Evolution. https://doi.org/10.1002/ece3.10072.
DOI: 10.34740/kaggle/dsv/5718370
title: USGS Wyoming Streamgages May 2023
DOI: 10.14272/umkrdqfxyjcilq-uhfffaoysa-n/chmo0001075
title: elemental analysis (EA) ()
title: InChI=1S/C19H22N2O2S/c24-19-21(9-3-8-20-10-12-22-13-11-20)14-17-16-5-2-1-4-15(16)6-7-18(17)23-19/h1-2,4-7H,3,8-14H2

description: dataset for elemental analysis (EA) calcd.: C, 66.64; H, 6.48 ; N, 8.18; S, 9.36; found: C, 66.54; H, 6.07; N, 8.47; S, 9,27.
DOI: 10.14272/umkrdqfxyjcilq-uhfffaoysa-n.1
title: C19H22N2O2S
title: InChI=1S/C19H22N2O2S/c24-19-21(9-3-8-20-10-12-22-13-11-20)14-17-16-5-2-1-4-15(16)6-7-18(17)23-19/h1-2,4-7H,3,8-14H2

description: This is a physical chemical entity[CHEBI_24431] associated with a molecule[CHEBI_25367]. The molecule[CHEBI_25367] can be described by the following structural desciptors[cheminf_000085]: InChI descriptor[cheminf_000113]: InChI=1S/C19H22N2O2S/c24-19-21(9-3-8-20-10-12-22-13-11-20)14-17-16-5-2-1-4-15(16)6-7-18(17)23-19/h1-2,4-7H,3,8-14H2, and canonical SMILES descriptor[cheminf_000007]: S=C1Oc2ccc3c(c2CN1CCCN1CCOCC1)cccc3, and by the IUPAC name[cheminf_000107]: . The physical chemical entity[CHEBI_24431] has a component solvent[CHEBI_46787] which is described by the canonical SMILES descriptor[cheminf_000007]: S=C1Oc2ccc3c(c2CN1CCCN1CCOCC1)cccc3 The physical chemical entity[CHEBI_24431] has the following Sample ID as registered in the research data repository chemotion (www.chemotion-repository.net, https://doi.org/10.25504/FAIRsharing.iagXcR): CRS-33449 The physical chemical entity[CHEBI_24431] can be described by the physical descriptors [CHEMINF_000025]: Melting point descriptor[CHEMINF_000256]: 144.0 (°C) Boiling point descriptor[CHEMINF_000257]: Refractive index descriptor[CHEMINF_000253]: The physical chemical entity[CHEBI_24431] can be further described by the following assays[OBI:0000070][CHMO:0001133]: CHMO:0000636 | Fourier transform infrared spectroscopy (FTIR) CHMO:0000593 | 1H nuclear magnetic resonance spectroscopy (1H NMR) CHMO:0000595 | 13C nuclear magnetic resonance spectroscopy (13C NMR) CHMO:0001075 | elemental analysis (EA) The physical chemical entity[CHEBI_24431] was deposited to the Molecule Archive of the Karlsruhe Insitute of Technology (KIT) with the following Sample ID: Used ontologies: CHEBI - Chemical Entities of Biological Interest CHEMINF - chemical information ontology (information entities about chemical entities) CHMO - Chemical Methods Ontology OBI - Ontology for Biomedical Investigations
DOI: 10.14272/umkrdqfxyjcilq-uhfffaoysa-n/chmo0000636
title: Fourier transform infrared spectroscopy (FTIR) ()
title: InChI=1S/C19H22N2O2S/c24-19-21(9-3-8-20-10-12-22-13-11-20)14-17-16-5-2-1-4-15(16)6-7-18(17)23-19/h1-2,4-7H,3,8-14H2

description: dataset for Fourier transform infrared spectroscopy (FTIR) IR (ATR, ṽ) = 2935 (w), 2854 (w), 2819 (w), 2797 (w), 1642 (w), 1533 (m), 1515 (m), 1469 (w), 1451 (w), 1436 (w), 1391 (w), 1379 (w), 1356 (m), 1327 (s), 1309 (w), 1278 (w), 1269 (w), 1232 (s), 1199 (vs), 1160 (vs), 1127 (s), 1109 (vs), 1070 (vs), 1041 (s), 1032 (s), 1022 (s), 1004 (s), 958 (m), 944 (w), 917 (s), 874 (m), 863 (s), 853 (m), 812 (vs), 765 (s), 744 (vs), 692 (m), 641 (w), 624 (s), 556 (s), 528 (s), 519 (s), 503 (m), 472 (w), 464 (w), 451 (w), 439 (w), 427 (m), 414 (m) cm–1.