cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7016244
title: Dataset of CCFs, dispersion data, synthetic ambient noise data, and rupture simulation data in the Anninghe study

description: This is the main dataset supporting the Anninghe paper. sta.loc represents the station list file.<br> 3DVelocityModel folder contains the final velocity model file Anninghe_Vs3D.dat.<br> CCFs_Original folder contains the original CCFs obtained from the Anninghe array.<br> CCFs_Separated folder contains the CCFs after model separation. In CCFs_Separated folder, the merge folder is the final average CCFs used for later tomography.<br> DisperData folder contains the seimiautopicked dispersion data from the separated CCFs.<br> RuptureSimulation folder contains the rupture simulation data in the paper.<br> SynNoiseData folder contains the CCFs calculated from synthetic ambient noise data.<br>
DOI: 10.5281/zenodo.7513262
title: Dataset of CCFs, dispersion data, synthetic ambient noise data, and rupture simulation data in the Anninghe study

description: This is the main dataset supporting the Anninghe paper. sta.loc represents the station list file.<br> 3DVelocityModel folder contains the final velocity model file Anninghe_Vs3D.dat.<br> CCFs_Original folder contains the original CCFs obtained from the Anninghe array.<br> CCFs_Separated folder contains the CCFs after model separation. In CCFs_Separated folder, the merge folder is the final average CCFs used for later tomography.<br> DisperData folder contains the seimiautopicked dispersion data from the separated CCFs.<br> RuptureSimulation folder contains the rupture simulation data in the paper.<br> SynNoiseData folder contains the CCFs calculated from synthetic ambient noise data.<br>
DOI: 10.5066/p9rp12rq
title: Longitudinal profiles of water temperature in Mill Creek, Mason County, Washington, measured using fiber-optic distributed temperature sensing (FO-DTS)

description: This data release contains fiber-optic distributed temperature sensor (FO-DTS) data for three reaches of Mill Creek collected during two deployments between August 20, 2020, and September 4, 2020. Spatially continuous profiles of water temperature were collected with a Sensornet® Oryx DTS FO-DTS. At each of the three reaches, water temperature was spatially averaged over 1.015-meter intervals and collected at one-hour intervals. Independent water-temperature data collected at four locations per reach by Hobo® Water Temp Pro v2 data-logging thermistors and thermocouples integrated with the FO-DTS are also included. Water-temperature data from both thermistors and thermocouples were used to dynamically calibrate FO-DTS data using the [...]
DOI: 10.5066/p98gfp93
title: Channel mapping Glen Canyon Dam to Lees Ferry in Glen Canyon National Recreation Area, Arizona - Data

description: Bathymetric, topographic, and grain-size data were collected from May 2013 to October 2016 along a 15-mi reach of the Colorado River in Glen Canyon National Recreation Area, Arizona by the U.S. Geological Survey Grand Canyon Monitoring and Research Center. The study reach is located from river miles -15 at the base of Glen Canyon Dam to 0 at Lees Ferry. Channel bathymetry was mapped using multibeam bathymetry collected in November 2014 and single beam bathymetry collected in February 2015, August 2015, February 2016, and June 2016. Subaerial topography was mapped using photogrammetry derived from aerial Imagery collected in May 2013 and ground-based total-stations collected in February 2015, October 2015, and October 2016. Bed-sediment grain-size data were collected in November 2014 using an underwater digital microscope system. These data were combined to produce digital elevation models, spatially variable estimates of digital elevation model uncertainty, georeferenced grain-size data, and bed-sediment distribution maps.
DOI: 10.5066/f7z60nc5
title: Water quality data from the Providence Water Supply Board for tributary streams to the Scituate Reservoir, water year 2016

description: Water-quality data were collected by the Providence Water Supply Board (PWSB) from tributaries in the Scituate Reservoir drainage area, October 2015 through September 2016 (water year 2016). These data were used to calculate instantaneous loads and yields of constituents in a report by the U.S. Geological Survey. Water-quality samples were collected by the PWSB either monthly or quarterly at fixed stations on 34 tributaries in the Scituate Reservoir drainage area. Water-quality samples were collected following a strict sampling schedule so that water-quality samples would be representative of various weather conditions. Samples were analyzed at the PWSB water-quality laboratory at the P.J. Holton Water Purification Plant in Scituate, Rhode Island. Water-quality properties and constituent concentrations were measured by using unfiltered water samples. These water-quality properties included pH, alkalinity, color, turbidity, and concentrations of chloride, nitrite, nitrate, orthophosphate, and bacteria (Escherichia coli [E. coli] and total coliform).
DOI: 10.5066/p9zjtzuv
title: Contour Dataset of the Potentiometric Surfaces of Shallow and Deep Groundwater-Level Altitudes in Harney Basin, Oregon, February-March 2018

description: This dataset contains manually developed 5-, 10-, and 500-ft contours for the Harney Basin, Oregon aquifer system shallow and deep potentiometric-surface maps. The potentiometric-surfaces show altitude at the water-table surface (shallow) and at which the water level would have risen in tightly-cased wells deeper than 100 ft (deep) and generally represents synoptic conditions during February-March of 2018. The water-table map was developed using groundwater-level measurements from shallow wells (generally less than 100 ft deep in the lowlands) and the altitudes of springs and gaining stream reaches and constrained by the altitude of the land surface. The deeper potentiometric-surface map was developed using measurements from wells generally more than 100 ft deep in the lowlands. The hydraulic-head distributions depicted are generalizations. The large study area, availability of water-level measurements, the distribution of wells across Harney Basin, and resource limitations precluded mapping all the complexities of the head distribution. Contours are most detailed and have 10-ft intervals (with the exception of the 4,095-ft contour) in the Harney Basin lowlands where data are more abundant, and the land surface is relatively flat. Groundwater heads of 4,200 ft or more were mapped using 500-ft contour intervals and generally coincide with upland areas where wells are sparse and groundwater head is strongly controlled by topography.
DOI: 10.5066/p9r7ujyv
title: Geochemical and Geospatial Data for Per- and Polyfluoroalkyl Substances (PFAS) in Groundwater Used As a Source of Drinking Water in the Eastern United States

description: Concentrations of inorganic constituents, dissolved organic carbon (DOC), tritium, per- and polyfluoroalkyl substances (PFAS), volatile organic compounds (VOCs), and pharmaceuticals were measured in groundwater samples collected from 254 wells in 2019 and 2020. Concentrations of inorganic constituents, DOC, VOCs, and pharmaceuticals were measured at the U.S. Geological Survey (USGS) National Water Quality Laboratory in Lakewood, Colorado. Concentrations of tritium were measured at the USGS Tritium Laboratory in Menlo Park, California. Concentrations of PFAS were measured at SGS Laboratory in Orlando, Florida. In addition, several geospatial parameters were determined, including: percentages of selected land uses within 500-meter buffers around sampled wells, nitrogen loading from septic systems within 500-meter buffers around sampled wells, distance to nearest wastewater treatment plant, and distance to selected industry and other potential point sources that could be sources of PFAS to the environment. The data were collected as part of the USGS National Water-Quality (NAWQA) project. This data release contains 12 tables of well information, laboratory results, geospatial output, and a data dictionary, including: 1) PFAS_Data_Dictionary.csv - Parameters in each table are defined in this table. 2) PFAS_BLANK.csv - Concentrations of PFAS in equipment, field, and source-solution blank samples. 3) PFAS_ENV.csv - Concentrations of PFAS in environmental samples. 4) PFAS_GEOSPATIAL.csv - Percentages of selected land uses within 500-meter buffers around sampled wells; nitrogen loading from septic systems within 500-meter buffers around sampled wells; distance to nearest wastewater treatment plant; distance to selected industry and other potential point sources that could be sources of PFAS to the environment. 5) PFAS_INORGANICS_AND_OTHER.csv - Concentration of inorganic constituents, DOC, and tritium. 6) PFAS_LAB_BLANK.csv - Concentrations of PFAS in laboratory blank samples. 7) PFAS_PHARMA.csv - Concentrations of pharmaceutical compounds in environmental samples. 8) PFAS_REP.csv - Concentrations of PFAS in replicate samples. 9) PFAS_SPIKE_FIELD.csv - Percent recovery for PFAS in field matrix-spike samples. 10) PFAS_SPIKE_LAB.csv - Percent recovery for PFAS in laboratory reagent-spike and matrix-spike samples. 11) PFAS_VOCs.csv - Concentrations of VOCs in environmental samples. 12) PFAS_WELLS.csv - Site characteristics of the sampled wells.
DOI: 10.6084/m9.figshare.21836220
title: HUAXI0017871590.bw

description: 111111
DOI: 10.6084/m9.figshare.21836220.v1
title: HUAXI0017871590.bw

description: 111111
DOI: 10.6084/m9.figshare.21836226
title: zebrafish_CellChatDB

description: Manually curated database for zebrafish LRI. Jin, S., Guerrero-Juarez, C.F., Zhang, L. et al. Inference and analysis of cell-cell communication using CellChat. Nat Commun 12, 1088 (2021). https://doi.org/10.1038/s41467-021-21246-9.
DOI: 10.5066/p92mxmm5
title: Uncrewed aerial system aeromagnetic test survey at the Boulder Magnetic Observatory

description: This data release contains two related datasets acquired for the uncrewed aerial system aeromagnetic test survey at the Boulder Magnetic Observatory. The first contains the raw magnetic data for the set of four test flights, and the second contains the compensated magnetic data for the same four flights. Details of the data are recorded in the metadata files associated with each dataset.
DOI: 10.5066/p982kk4d
title: Zircon U-Pb data for ash-fall tuffs and sandstones of the Cenozoic Amargosa Valley and Bat Mountain Formations exposed on Bat Mountain, southern Funeral Mountains, California, USA

description: This document includes two suites of zircon U-Pb data that establish a detailed chronostratigraphy for Cenozoic strata exposed on Bat Mountain of the southern Funeral Mountains, California, USA. The first suite of data includes detrital zircon U-Pb age distributions that characterize the maximum depositional age and provenance of five sandstone samples from the Oligocene-Miocene Amargosa Valley Formation (N=4) and the Miocene Bat Mountain Formation (N=1). The second suite of data includes zircon U-Pb data for five ash-fall tuffs that are interbedded with strata of the Amargosa Valley and Bat Mountain Formations. Together, interpreted eruption ages of tuffs and maximum depositional ages of sandstones constrain deposition of the Amargosa Valley Formation to have occurred between ca. 28 and 19 Ma, followed by deposition of the Bat Mountain Formation between ca. 15.5 and 13 Ma.
DOI: 10.6084/m9.figshare.21836229.v1
title: The tectono-magmatic formation of the Japan Sea: Constraints from in-situ trace elements and Sr isotopes of plagioclase and clinopyroxene

description: Figure and Table Captions, Geochemical Data, and Supplementary Figures generated in this study are included in the Supplementary I, Tables S1-S5, and Figures S1-S4.
DOI: 10.6084/m9.figshare.16902892.v2
title: Genome assembly and Annotation of <i>Populus alba</i> '<i>Berolinensis</i>' L.

description: <i>Populus alba </i>'<i>Berolinensis</i>' L. is a fast-growing poplar hybrid in the northeast of China. This hybrid poplar is widely used as landscape ornamentals and in forestation of urban areas (e.g., Shenyang city, a heavy industrial area) for good adaptation to the adverse environments. In this project, a high-quality assembly of <i>Populus alba</i> '<i>Berolinensis</i>' L. is established, aiming to uncover the genetic effects on the disease and traits of <i>Populus </i>deeper.<br>The sequencing raw data are publicly available in the NCBI database under project PRJNA635615.
DOI: 10.34740/kaggle/dsv/4824091
title: 💥📰 Daily Breaking News Twitter Dataset
DOI: 10.6084/m9.figshare.21836292
title: survival_HR_Fig3A.xlsx

description: Raw tabulated data from the systematic review and meta-analysis (Figure 3A)
DOI: 10.6084/m9.figshare.21836292.v1
title: survival_HR_Fig3A.xlsx

description: Raw tabulated data from the systematic review and meta-analysis (Figure 3A)
DOI: 10.6084/m9.figshare.21836292.v2
title: survival_HR_Fig3A.xlsx

description: Raw tabulated data from the systematic review and meta-analysis (Figure 3A)
DOI: 10.6084/m9.figshare.21679409
title: Data and analysis code of "Regional and tele-connected impacts of the Tibetan Plateau surface darkening"

description: Attached please find the data and analysis code of "Regional and tele-connected impacts of the Tibetan Plateau surface darkening" (https://www.nature.com/articles/s41467-022-35672-w) Please cite the article if you want to use these data and analysis code ( Tang, S., Vlug, A., Piao, S. <em>et al.</em> Regional and tele-connected impacts of the Tibetan Plateau surface darkening. <em>Nat Commun</em> <strong>14</strong>, 32 (2023). https://doi.org/10.1038/s41467-022-35672-w ) <br> Above data and analysis codes include: 1) AnalysisData_Climate.m: Analysis code for climate experiments. Use Matlab to open and run this file. Note: some pathways in this code file are absolute pathways and should be changed, otherwise errors will be reported. 2) AnalysisData_Glacier.m: Analysis code for glacier simulations. Use Matlab to open and run this file. Results of the analysis part have been stored at Glacier_difference.mat. Note: some pathways in this code file are absolute pathways and should be changed, otherwise errors will be reported. 3) Basedata.mat: Store the base dataset used for the analysis in AnalysisData_Climate.m. 4) Climate_difference.mat: All the variables in this mat are derived from the climate experiments. GlobalX means the annual mean results, SummerX means the boreal summer results. The size of these variables: longitude*latitude*(atmospheric layer)*3 (CTL results, SCE results, significant). This data is used for AnalysisData_Climate.m. 5) Glacier_difference,mat: The meaning of each variables in this data is list in AnalysisData_Glacier.m. This data is used for AnalysisData_Glacier.m. 6) AnalysisData.zip: store the 99-year simulation results of some variables used in the AnalysisData_Climate.m. 7) Wilcoxon.m: Judge whether the difference is significant ( non-parametric Wilcoxon signed-rank test). 8) TPPolygon.shp: the shp file of the Tibetan Plateau. 9) brewermap.m: generate the colormap of each figure. 10) 00_rgi60_regions_xxx: shp file of the 13 (or 15) second-order regions over the Tibetan Plateau and its surrounding regions (Pfeffer et al., 2014; RGI Consortium et al., 2017). 11) draw_radar2.m: this file is used to draw figure 2(b). 12) p50_degree_glacier_volume_km3.tif: This tif is provided by Farinotti et al., 2019. 13) suppl_GlacierMIP_results.nc: this nc file is provided by Marzeion et al., 2020 <br> If you have any questions or suggestions, please contact shuchangtang@pku.edu.cn
DOI: 10.5281/zenodo.7513367
title: Decoding MIEV car in motion

description: The data represent the internal network progressive codesd for a test MIEV car in motion. The motion includes forward, stop, change gear, reverse, and stop conditions. The duration of the movement was about 4 minutes.
DOI: 10.6084/m9.figshare.21679409.v2
title: Data and analysis code of "Regional and tele-connected impacts of the Tibetan Plateau surface darkening"

description: Above data and analysis codes include: 1) AnalysisData_Climate.m: Analysis code for climate experiments. Use Matlab to open and run this file. Note: some pathways in this code file are absolute pathways and should be changed, otherwise errors will be reported. 2) AnalysisData_Glacier.m: Analysis code for glacier simulations. Use Matlab to open and run this file. Results of the analysis part have been stored at Glacier_difference.mat. Note: some pathways in this code file are absolute pathways and should be changed, otherwise errors will be reported. 3) Basedata.mat: Store the base dataset used for the analysis in AnalysisData_Climate.m. 4) Climate_difference.mat: All the variables in this mat are derived from the climate experiments. GlobalX means the annual mean results, SummerX means the boreal summer results. The size of these variables: longitude*latitude*(atmospheric layer)*3 (CTL results, SCE results, significant). This data is used for AnalysisData_Climate.m. 5) Glacier_difference,mat: The meaning of each variables in this data is list in AnalysisData_Glacier.m. This data is used for AnalysisData_Glacier.m. 6) AnalysisData.zip: store the 99-year simulation results of some variables used in the AnalysisData_Climate.m. 7) Wilcoxon.m: Judge whether the difference is significant ( non-parametric Wilcoxon signed-rank test). 8) TPPolygon.shp: the shp file of the Tibetan Plateau. 9) brewermap.m: generate the colormap of each figure. 10) 00_rgi60_regions_xxx: shp file of the 13 (or 15) second-order regions over the Tibetan Plateau and its surrounding regions (Pfeffer et al., 2014; RGI Consortium et al., 2017). 11) draw_radar2.m: this file is used to draw figure 2(b). 12) p50_degree_glacier_volume_km3.tif: This tif is provided by Farinotti et al., 2019. 13) suppl_GlacierMIP_results.nc: this nc file is provided by Marzeion et al., 2020 <br> If you have any questions or suggestions, please contact shuchangtang@pku.edu.cn
DOI: 10.5281/zenodo.7513366
title: Decoding MIEV car in motion

description: The data represent the internal network progressive codesd for a test MIEV car in motion. The motion includes forward, stop, change gear, reverse, and stop conditions. The duration of the movement was about 4 minutes.
DOI: 10.5281/zenodo.7513373
title: Dataset literature review digital forensic and male

description: data yang digunakan untuk membuat penelitian berdasarkan tinjauan dengan kata kunci digital forensic and male
DOI: 10.5281/zenodo.7513372
title: Dataset literature review digital forensic and male

description: data yang digunakan untuk membuat penelitian berdasarkan tinjauan dengan kata kunci digital forensic and male
DOI: 10.6084/m9.figshare.21767678.v3
title: 20221222_starrydata2

description: All data on Starrydata2, a database of experimental properties in the field of inorganic materials
DOI: 10.6084/m9.figshare.21767678.v4
title: 20221222

description: All data on Starrydata2, a database of experimental properties in the field of inorganic materials
DOI: 10.6084/m9.figshare.21836352
title: HUAXI0017987458.bw

description: 1111 <br>
DOI: 10.6084/m9.figshare.21836352.v1
title: HUAXI0017987458.bw

description: 1111 <br>
DOI: 10.6084/m9.figshare.21836367
title: HUAXI0018030292.bw

description: 1111
DOI: 10.6084/m9.figshare.21836367.v1
title: HUAXI0018030292.bw

description: 1111
DOI: 10.6084/m9.figshare.21836376
title: HUAXI0017318401.bw

description: 1111
DOI: 10.6084/m9.figshare.21836376.v1
title: HUAXI0017318401.bw

description: 1111
DOI: 10.6084/m9.figshare.21836379.v1
title: HUAXI0017808121.bw

description: 11111
DOI: 10.6084/m9.figshare.21836379
title: HUAXI0017808121.bw

description: 11111
DOI: 10.34740/kaggle/dsv/4824539
title: 🇺🇦 Ukraine Conflict Twitter Dataset
DOI: 10.6084/m9.figshare.21836385
title: HUAXI0017442398.bw

description: 11111
DOI: 10.6084/m9.figshare.21836385.v1
title: HUAXI0017442398.bw

description: 11111
DOI: 10.5281/zenodo.7513431
title: Predicting Burnout in Australian Mental Health Professionals

description: Data set associated with the study: 'Predicting Burnout in Australian Mental Health Professionals: Uncertainty tolerance, impostorism and psychological inflexibility'. <br> Access by request
DOI: 10.5281/zenodo.7513430
title: Predicting Burnout in Australian Mental Health Professionals

description: Data set associated with the study: 'Predicting Burnout in Australian Mental Health Professionals: Uncertainty tolerance, impostorism and psychological inflexibility'. <br> Access by request
DOI: 10.5281/zenodo.7212528
title: datasets of 2016 Nanjing extreme rainfall

description: The uploaded files are the datasets of radar, disdrometer, ERA5, gauges, lightning, radiosound, and city lights from 2016 Nanjing extreme rainfall event
DOI: 10.6084/m9.figshare.21758039
title: Supplementary Data S10 - Figure 4c, Figure 5d and c and Supplementary Figures S4 and S5

description: Junction expression across all samples - Figure 3 C, Figure 4 C and D, and Supplementary Figures S4 and S5
DOI: 10.6084/m9.figshare.21758063
title: Supplementary Data S9 - Figure 4B

description: Example of D.melanogaster PRO-seq sample coverage per position - Figure 4B
DOI: 10.6084/m9.figshare.21758072
title: Supplementary Data S11 - Figure 6

description: Enriched motifs in AGB Insecta species- Figure 6
DOI: 10.6084/m9.figshare.21758096
title: Supplementary Data S12 - Supplementary Figure S2 and S3

description: Per-species base compositions of all species used. Related to Supplementary Figure S3
DOI: 10.6084/m9.figshare.21758072.v2
title: Supplementary Data S11 - Figure 6

description: Enriched motifs in AGB Insecta species- Figure 6
DOI: 10.6084/m9.figshare.21758096.v2
title: Supplementary Data S12 - Supplementary Figure S2 and S3

description: Per-species base compositions of all species used. Related to Supplementary Figure S3
DOI: 10.6084/m9.figshare.21758024
title: Supplementary Data S8 - Figure 3E

description: Prevalence of gene neighbors and same strand. Used to generate Figure 3E
DOI: 10.6084/m9.figshare.21758024.v2
title: Supplementary Data S8 - Figure 3E

description: Prevalence of gene neighbors and same strand. Used to generate Figure 3E
DOI: 10.6084/m9.figshare.21758033
title: Supplementary Data S7 - Figure 3B and C

description: Per-phylum prevalence of all gene groups. Used to generate Figure 3B and C
DOI: 10.6084/m9.figshare.21758033.v2
title: Supplementary Data S7 - Figure 3B and C

description: Per-phylum prevalence of all gene groups. Used to generate Figure 3B and C
DOI: 10.6084/m9.figshare.21758039.v2
title: Supplementary Data S10 - Figure 4c, Figure 5d and c and Supplementary Figures S4 and S5

description: Junction expression across all samples - Figure 3 C, Figure 4 C and D, and Supplementary Figures S4 and S5
DOI: 10.6084/m9.figshare.21758072.v1
title: Supplementary Data 9 - Figure 6

description: Enriched motifs in AGB Insecta species- Figure 6
DOI: 10.6084/m9.figshare.21758096.v1
title: Supplementary Data 10 - Supplementary Figure S2 and S3

description: Per-species base compositions of all species used. Related to Supplementary Figure S3
DOI: 10.6084/m9.figshare.21758003
title: Supplementary Data S6 - Figure 2C and D

description: A table AR rates per class. Used to generate Figure 2 C and D.
DOI: 10.6084/m9.figshare.21758063.v2
title: Supplementary Data S9 - Figure 4B

description: Example of D.melanogaster PRO-seq sample coverage per position - Figure 4B
DOI: 10.6084/m9.figshare.21757646
title: Shtolz_et_al_Supplementary_Table_S4 (Not referenced in text)

description: Summary of RNA-seq library selection methods used in the datasets we used for our RNA-seq analyses (datasets listed in Supplementary Table S3.
DOI: 10.6084/m9.figshare.21757940
title: Supplementary Data S4 - Figures 1E and Supplementary Figures S1 and S3

description: Data on all tRNA genes and codon usage. used to generate Figures 1E and Supplementary Figures S1 and S2.
DOI: 10.6084/m9.figshare.21757940.v2
title: Supplementary Data S4 - Figures 1E and Supplementary Figures S1 and S3

description: Data on all tRNA genes and codon usage. used to generate Figures 1E and Supplementary Figures S1 and S2.