cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.48567/2btg-y403
title: Sabrina Automatic Weather Station, 2012 Reader format three-hour observational data.
DOI: 10.48567/wz0d-jm82
title: Schwerdtfeger Automatic Weather Station, 2012 Reader format three-hour observational data.
DOI: 10.34740/kaggle/dsv/4049367
title: USCrimesDataset
DOI: 10.48567/cryz-b421
title: Siple Dome Automatic Weather Station, 2012 Reader format three-hour observational data.
DOI: 10.48567/gsng-as64
title: Sky Blu Automatic Weather Station, 2012 Reader format three-hour observational data.
DOI: 10.48567/1g9g-yb35
title: Theresa Automatic Weather Station, 2012 Reader format three-hour observational data.
DOI: 10.48567/2ypm-s938
title: Thurston Island Automatic Weather Station, 2012 Reader format three-hour observational data.
DOI: 10.48567/b694-n885
title: Tom Automatic Weather Station, 2012 Reader format three-hour observational data.
DOI: 10.48567/kxjq-7d08
title: Vito Automatic Weather Station, 2012 Reader format three-hour observational data.
DOI: 10.48567/rsyj-g161
title: Whitlock Automatic Weather Station, 2012 Reader format three-hour observational data.
DOI: 10.48567/z9ra-vn53
title: Windless Bight Automatic Weather Station, 2012 Reader format three-hour observational data.
DOI: 10.7910/dvn/zlhqqp
title: CI Technology Data Set

description: CiTDB includes two types of technology datasets: (i) hardware expenditures and (ii) product installs. Hardware expenditure data is constructed through a combination of surveys and modeling. A survey is administered to a number of companies and the data from surveys is used to develop a prediction model of expenditures as a function of firm characteristics. CiTDB uses this model to predict the expenditures of non-surveyed firms and reports them in the dataset. In contrast, CiTDB does not do any imputation for product install data, which comes entirely from web scraping and surveys. A confidence score between 1-3 is assigned to indicate how much the source of information can be trusted. A 3 corresponds to 90-100 percent install likelihood, 2 corresponds to 75-90 percent install likelihood and 1 corresponds to 65-75 percent install likelihood. CiTDB reports technology adoption at the site level with a unique DUNS identifier. One of these sites is identified as an “enterprise,” corresponding to the firm that owns the sites. Therefore, it is possible to analyze technology adoption both at the site (establishment) and enterprise (firm) levels. CiTDB sources the site population from Dun and Bradstreet every year and drops sites that are not relevant to their clients. Due to this sample selection, there is quite a bit of variation in the number of sites from year to year, where on average, 10-15 percent of sites enter and exit every year in the US data. This number is higher in the EU data. We observe similar turnover year-to-year in the products included in the dataset. Some products have become absolute, and some new products are added every year. There are two versions of the data: (i) version 3, which covers 2016-2020, and (ii) version 4, which covers 2020-2021. The quality of version 4 is significantly better regarding the information included about the technology products. In version 3, product categories have missing values, and they are abbreviated in a way that are sometimes difficult to interpret. Version 4 does not have any major issues. Since both versions of the data are available in 2020, CiTDB provides a crosswalk between the versions. This makes it possible to use information about products in Version 4 for the products in Version 3, with the caveats that there will be no crosswalk for the products that exist in 2016-2019 but not in 2020. Finally, special attention should be paid to data from 2016, where the coverage is significantly different from 2017. From 2017 onwards, coverage is more consistent.
DOI: 10.5067/measures/dmspf10/ssmi/data201
title: NASA MEASURES Precipitation Ensemble based on SSM/I DMSP F10 NASA PPS L1C V06 Tbs
DOI: 10.5067/measures/nimbus7/smmr/data201
title: NASA MEASURES Precipitation Ensemble based on SMMR Nimbus-7 NSIDC L1B Tbs
DOI: 10.5067/measures/dmspf08/ssmi/data201
title: NASA MEASURES Precipitation Ensemble based on SSM/I DMSP F08 NASA PPS L1C V06 Tbs
DOI: 10.5067/measures/dmspf11/ssmi/data201
title: NASA MEASURES Precipitation Ensemble based on SSM/I DMSP F11 NASA PPS L1C V06 Tbs
DOI: 10.5067/measures/dmspf13/ssmi/data201
title: NASA MEASURES Precipitation Ensemble based on SSM/I DMSP F13 NASA PPS L1C V06 Tbs
DOI: 10.5067/measures/dmspf14/ssmi/data201
title: NASA MEASURES Precipitation Ensemble based on SSM/I DMSP F14 NASA PPS L1C V06 Tbs
DOI: 10.5067/measures/dmspf15/ssmi/data201
title: NASA MEASURES Precipitation Ensemble based on SSM/I DMSP F15 NASA PPS L1C V06 Tbs
DOI: 10.5067/measures/dmspf16/ssmis/data201
title: NASA MEASURES Precipitation Ensemble based on SSMIS DMSP F16 NASA PPS L1C V05 Tbs
DOI: 10.5067/measures/dmspf17/ssmis/data201
title: NASA MEASURES Precipitation Ensemble based on SSMIS DMSP F17 NASA PPS L1C V05 Tbs
DOI: 10.5067/measures/dmspf18/ssmis/data201
title: NASA MEASURES Precipitation Ensemble based on SSMIS DMSP F18 NASA PPS L1C V05 Tbs
DOI: 10.5067/measures/dmspf19/ssmis/data201
title: NASA MEASURES Precipitation Ensemble based on SSMIS DMSP F19 NASA PPS L1C V05 Tbs
DOI: 10.5067/measures/trmm/tmi/data201
title: NASA MEASURES Precipitation Ensemble based on TMI TRMM NASA PPS L1C V05 Tbs
DOI: 10.5067/measures/gpm/gmi/data201
title: NASA MEASURES Precipitation Ensemble based on GMI GPM NASA PPS L1C V05 Tbs
DOI: 10.5067/measures/aqua/amsre/data201
title: NASA MEASURES Precipitation Ensemble based on AMSRE AQUA NASA PPS L1C V05 Tbs
DOI: 10.5067/measures/gcomw1/amsr2/data201
title: NASA MEASURES Precipitation Ensemble based on AMSR2 GCOMW1 NASA PPS L1C V05 Tbs
DOI: 10.48567/83s8-9t77
title: AGO-4 Automatic Weather Station, 2013 Reader format three-hour observational data.
DOI: 10.48567/twav-ct21
title: Baldrick Automatic Weather Station, 2013 Reader format three-hour observational data.
DOI: 10.48567/yne3-4174
title: Bear Peninsula Automatic Weather Station, 2013 Reader format three-hour observational data.
DOI: 10.48567/sgya-pk22
title: Bonaparte Point Automatic Weather Station, 2013 Reader format three-hour observational data.
DOI: 10.48567/5bkk-8r45
title: Brianna Automatic Weather Station, 2013 Reader format three-hour observational data.
DOI: 10.48567/6r4g-ds77
title: Byrd Automatic Weather Station, 2013 Reader format three-hour observational data.
DOI: 10.48567/fy0q-2871
title: Cape Hallett Automatic Weather Station, 2013 Reader format three-hour observational data.
DOI: 10.48567/x4p5-y026
title: Carolyn Automatic Weather Station, 2013 Reader format three-hour observational data.
DOI: 10.48567/a88y-py77
title: D-10 Automatic Weather Station, 2013 Reader format three-hour observational data.
DOI: 10.48567/qr2p-kv69
title: D-47 Automatic Weather Station, 2013 Reader format three-hour observational data.
DOI: 10.48567/fv44-a638
title: D-85 Automatic Weather Station, 2013 Reader format three-hour observational data.
DOI: 10.48567/q92k-8507
title: Dismal Island Automatic Weather Station, 2013 Reader format three-hour observational data.
DOI: 10.48567/drvz-8x78
title: Dome C II Automatic Weather Station, 2013 Reader format three-hour observational data.
DOI: 10.48567/9cfp-mr40
title: Dome Fuji Automatic Weather Station, 2013 Reader format three-hour observational data.
DOI: 10.48567/nakv-6p31
title: Elaine Automatic Weather Station, 2013 Reader format three-hour observational data.
DOI: 10.48567/g5he-m908
title: Elizabeth Automatic Weather Station, 2013 Reader format three-hour observational data.
DOI: 10.48567/m4n6-4f73
title: Emilia Automatic Weather Station, 2013 Reader format three-hour observational data.
DOI: 10.48567/rxe0-dr29
title: Eric Automatic Weather Station, 2013 Reader format three-hour observational data.
DOI: 10.48567/8a5z-gt02
title: Erin Automatic Weather Station, 2013 Reader format three-hour observational data.
DOI: 10.48567/1pdk-fy70
title: Evans Knoll Automatic Weather Station, 2013 Reader format three-hour observational data.
DOI: 10.48567/fzw3-ae42
title: Ferrell II Automatic Weather Station, 2013 Reader format three-hour observational data.
DOI: 10.48567/3j7k-8k50
title: Gill Automatic Weather Station, 2013 Reader format three-hour observational data.
DOI: 10.48567/7mwx-r886
title: Harry Automatic Weather Station, 2013 Reader format three-hour observational data.
DOI: 10.48567/rz4h-hp07
title: Henry Automatic Weather Station, 2013 Reader format three-hour observational data.
DOI: 10.48567/t5xj-mx08
title: Hugo Island Automatic Weather Station, 2013 Reader format three-hour observational data.
DOI: 10.48567/erev-9409
title: JASE2007 Automatic Weather Station, 2013 Reader format three-hour observational data.
DOI: 10.48567/z65n-k361
title: Janet Automatic Weather Station, 2013 Reader format three-hour observational data.
DOI: 10.48567/590t-h233
title: Kominko-Slade Automatic Weather Station, 2013 Reader format three-hour observational data.
DOI: 10.48567/y5sd-4f70
title: Laurie II Automatic Weather Station, 2013 Reader format three-hour observational data.
DOI: 10.48567/q7ma-dm08
title: Manuela Automatic Weather Station, 2013 Reader format three-hour observational data.
DOI: 10.48567/2jc6-bh04
title: Marble Point Automatic Weather Station, 2013 Reader format three-hour observational data.
DOI: 10.48567/63n1-sa77
title: Margaret Automatic Weather Station, 2013 Reader format three-hour observational data.
DOI: 10.48567/e1ya-1p02
title: Marilyn Automatic Weather Station, 2013 Reader format three-hour observational data.
DOI: 10.48567/pdaz-m578
title: Marlene Automatic Weather Station, 2013 Reader format three-hour observational data.
DOI: 10.48567/79cy-vf16
title: Mizuho Automatic Weather Station, 2013 Reader format three-hour observational data.
DOI: 10.48567/epf9-m128
title: Mount Siple Automatic Weather Station, 2013 Reader format three-hour observational data.
DOI: 10.48567/dfhg-s682
title: Nico Automatic Weather Station, 2013 Reader format three-hour observational data.
DOI: 10.48567/jmg6-b234
title: PANDA-South Automatic Weather Station, 2013 Reader format three-hour observational data.
DOI: 10.48567/k31t-my04
title: Pegasus North Automatic Weather Station, 2013 Reader format three-hour observational data.
DOI: 10.48567/3nfe-5031
title: Possession Island Automatic Weather Station, 2013 Reader format three-hour observational data.
DOI: 10.48567/b44j-s521
title: Relay Station Automatic Weather Station, 2013 Reader format three-hour observational data.
DOI: 10.48567/bt8b-jd71
title: Sabrina Automatic Weather Station, 2013 Reader format three-hour observational data.
DOI: 10.48567/d8y4-fa19
title: Schwerdtfeger Automatic Weather Station, 2013 Reader format three-hour observational data.
DOI: 10.48567/y70b-y728
title: Siple Dome Automatic Weather Station, 2013 Reader format three-hour observational data.
DOI: 10.48567/qrw8-1991
title: Theresa Automatic Weather Station, 2013 Reader format three-hour observational data.
DOI: 10.48567/rbnn-z426
title: Thurston Island Automatic Weather Station, 2013 Reader format three-hour observational data.
DOI: 10.48567/xj2r-j842
title: Tom Automatic Weather Station, 2013 Reader format three-hour observational data.
DOI: 10.48567/6p37-kq74
title: Vito Automatic Weather Station, 2013 Reader format three-hour observational data.
DOI: 10.48567/1d3h-3v13
title: Whitlock Automatic Weather Station, 2013 Reader format three-hour observational data.
DOI: 10.5281/zenodo.6977462
title: Test

description: <strong>Spark Empirical</strong> <br> This dataset contains Stack Overflow manual study results for the paper "An Empirical Study on the Challenges that Developers Encounter When Developing Apache Spark Applications". the <em>data</em> folder contains the <em>Stackoverflow Manual Results.csv </em>file that is the manual analysis result for the Stack Overflow posts. The CSV file contains information on the classification of the data, the reasons and the number of views, etc. the <em>scripts</em> folder contains the python and SQL files that are used for data collection and data analysis. <em>query_data.sql</em> is used to collect data from the Stack Exchange website. <em>sample.py</em> is used to sample data for the manual analysis in the paper. <em>common_issue.py</em> is used to study the percentage of common issues in rq1. <em>popularity.py </em>is used to calculate the average of normalized view counts in rq2. <em>popularity_difficulty.py</em> is used to calculate the average of raw view counts and the median hours to receive an answer in rq2. <em>root_cuase.py</em> is used to study the percentage of root causes in rq3.
DOI: 10.5281/zenodo.6901964
title: Marine Data Sharing Companion Package

description: Collection of data from marine data sharing study conducted between January 2022 and June 2022. Contents: Focus group interview guide Workshop summary of contents Coding categories and subcategories of transcriptions Sample of quotes from participants and their related subcategories For confidentiality reasons, only summaries and excerpts of the data collected are presented in this package. This content can be used to back up our findings and results w.r.t. marine data sharing, working as a support to the chain of evidence of the findings.
DOI: 10.5281/zenodo.6901963
title: Marine Data Sharing Companion Package

description: Collection of data from marine data sharing study conducted between January 2022 and June 2022. Contents: Focus group interview guide Workshop summary of contents Coding categories and subcategories of transcriptions Sample of quotes from participants and their related subcategories For confidentiality reasons, only summaries and excerpts of the data collected are presented in this package. This content can be used to back up our findings and results w.r.t. marine data sharing, working as a support to the chain of evidence of the findings.
DOI: 10.5281/zenodo.5086359
title: Semianalytic VT

description: Grid of optimal SNRs and detection probabilities for calculating semianalyic VT estimates, detection probabilities, and detection weights for an arbitrary dataset. The datasets are grid over primary mass, mass ratio, and redshift for which optimal SNRs and detection probabilities have been pre-computed. There are two datasets in the h5 file: 'midhighlatelow_network' and 'design_network', which assume 3-detector networks consisting of LIGO-Hanford, LIGO-Livingston, and Virgo. Can be used in conjunction with this Github repo for estimating detection probabilities and relative detection weights for an arbitrary population.
DOI: 10.5281/zenodo.5086358
title: Semianalytic VT

description: Grid of optimal SNRs and detection probabilities for calculating semianalyic VT estimates, detection probabilities, and detection weights for an arbitrary dataset. The datasets are grid over primary mass, mass ratio, and redshift for which optimal SNRs and detection probabilities have been pre-computed. There are two datasets in the h5 file: 'midhighlatelow_network' and 'design_network', which assume 3-detector networks consisting of LIGO-Hanford, LIGO-Livingston, and Virgo. Can be used in conjunction with this Github repo for estimating detection probabilities and relative detection weights for an arbitrary population.
DOI: 10.5281/zenodo.6921620
title: Data related to "Upper cortical layer-driven network impairment in schizophrenia" paper, by Batiuk, Tyler et al.

description: This is Data for "Upper cortical layer-driven network impairment in schizophrenia" paper, by Batiuk, Tyler et al., 2022 This repository contains: Supplementary Dataset Tables 1-4 (Supplementary_Dataset_Tables_1-4.xlsx). They contain DE genes and GO terms from snRNA-seq and visium analysis. Single nuclei and Visium spatial transcriptomics sequencing data (snRNA-seq_and_spatial_transcriptomics.zip) containing raw count matrices of snRNA-seq samples; Conos object with aligned snRNA-seq samples; snRNA-seq single nuclei cell subtype annotations; raw count matrices of Visium spatial transcriptomics samples; Visium spatial transcriptomics manual histological cortical layer annotations; and 10x Genomics spaceranger count pipeline output for Visium spatial transcriptomics data. Histological images of H&E stained Visium spatial transcriptomics samples mounted on visium slide capture area (visium_sample_images.zip)
DOI: 10.5281/zenodo.5810785
title: Data related to "Upper cortical layer-driven network impairment in schizophrenia" paper, by Batiuk, Tyler et al.

description: This is Data for "Upper cortical layer-driven network impairment in schizophrenia" paper, by Batiuk, Tyler et al., 2022 This repository contains: Supplementary Dataset Tables 1-4 (Supplementary_Dataset_Tables_1-4.xlsx). They contain DE genes and GO terms from snRNA-seq and visium analysis. Single nuclei and Visium spatial transcriptomics sequencing data (snRNA-seq_and_spatial_transcriptomics.zip) containing raw count matrices of snRNA-seq samples; Conos object with aligned snRNA-seq samples; snRNA-seq single nuclei cell subtype annotations; raw count matrices of Visium spatial transcriptomics samples; Visium spatial transcriptomics manual histological cortical layer annotations; and 10x Genomics spaceranger count pipeline output for Visium spatial transcriptomics data. Histological images of H&E stained Visium spatial transcriptomics samples mounted on visium slide capture area (visium_sample_images.zip)
DOI: 10.6084/m9.figshare.20459229
title: diversity_lat.xlsx

description: Data on species diversity in natural and artificial breakwaters at latitudes.
DOI: 10.6084/m9.figshare.20459229.v1
title: diversity_lat.xlsx

description: Data on species diversity in natural and artificial breakwaters at latitudes.
DOI: 10.6084/m9.figshare.20456373
title: ITEM A: Content Analysis of Student Reflections 2019

description: Content analysis of student reflections 2019
DOI: 10.6084/m9.figshare.20456373.v1
title: ITEM A: Content Analysis of Student Reflections 2019

description: Content analysis of student reflections 2019
DOI: 10.6073/pasta/fe03987854ff4f498c8ac84d4fd51e41
title: Soil moisture, temperature, and electrical conductivity data from the black sand extended growing season length experiment, hourly, 2018 - ongoing

description: As a result of climate change, the Rocky Mountain Front Range is experiencing warmer summers and earlier snowmelt. Due to the importance of snow for regulating soil temperature, growing season length, and available moisture in alpine ecosystems, even small shifts in the snow-free period could have large impacts. The focus of the Growing Season Length Experiment is to examine how terrain-related differences in climate exposure influence the way alpine habitats respond to climate change via earlier snowmelt. To simulate how changes in growing season length may affect biotic and abiotic components, NWT LTER researchers established 5 experimental sites each containing a pair 10 x 40m rectangular plots. These blocks include north and south facing aspects, subalpine and alpine tundra meadows in a range of hydrological conditions (e.g. dry meadows, moist meadows, wet meadows). We accelerated snowmelt in one plot of each block by adding chemically inert black sand, while keeping the second plot as an unmanipulated control (black sand was added to these plots after snow had naturally melted). This dataset includes measurements of soil temperature, moisture, and electrical conductivity.
DOI: 10.6084/m9.figshare.20458929
title: ITEM B: Content Analysis of Course Strengths 2019

description: Content analysis of MSGR course strengths 2019
DOI: 10.6084/m9.figshare.20458929.v1
title: ITEM B: Content Analysis of Course Strengths 2019

description: Content analysis of MSGR course strengths 2019
DOI: 10.5281/zenodo.6977484
title: Test

description: This dataset contains Stack Overflow manual study results for the paper "An Empirical Study on the Challenges that Developers Encounter When Developing Apache Spark Applications". the <em>data</em> folder contains the <em>Stackoverflow Manual Results.csv </em>file that is the manual analysis result for the Stack Overflow posts. The CSV file contains information on the classification of the data, the reasons and the number of views, etc. the <em>scripts</em> folder contains the python and SQL files that are used for data collection and data analysis. <em>query_data.sql</em> is used to collect data from the Stack Exchange website. <em>sample.py</em> is used to sample data for the manual analysis in the paper. <em>common_issue.py</em> is used to study the percentage of common issues in rq1. <em>popularity.py </em>is used to calculate the average of normalized view counts in rq2. <em>popularity_difficulty.py</em> is used to calculate the average of raw view counts and the median hours to receive an answer in rq2. <em>root_cuase.py</em> is used to study the percentage of root causes in rq3.
DOI: 10.5281/zenodo.6977461
title: Test

description: This dataset contains Stack Overflow manual study results for the paper "An Empirical Study on the Challenges that Developers Encounter When Developing Apache Spark Applications". the <em>data</em> folder contains the <em>Stackoverflow Manual Results.csv </em>file that is the manual analysis result for the Stack Overflow posts. The CSV file contains information on the classification of the data, the reasons and the number of views, etc. the <em>scripts</em> folder contains the python and SQL files that are used for data collection and data analysis. <em>query_data.sql</em> is used to collect data from the Stack Exchange website. <em>sample.py</em> is used to sample data for the manual analysis in the paper. <em>common_issue.py</em> is used to study the percentage of common issues in rq1. <em>popularity.py </em>is used to calculate the average of normalized view counts in rq2. <em>popularity_difficulty.py</em> is used to calculate the average of raw view counts and the median hours to receive an answer in rq2. <em>root_cuase.py</em> is used to study the percentage of root causes in rq3.
DOI: 10.6084/m9.figshare.20459043
title: ITEM C: Content Analysis of Course Weaknesses 2019

description: Content analysis of MSGR course weaknesses 2019
DOI: 10.6084/m9.figshare.20459043.v1
title: ITEM C: Content Analysis of Course Weaknesses 2019

description: Content analysis of MSGR course weaknesses 2019
DOI: 10.5281/zenodo.6975849
title: Relative roles of sediment transport and localized erosion on phosphorus load in the lower Susquehanna River and its mouth in the Chesapeake Bay, USA

description: Data set includes average wind and wave conditions for the mouth of the Susquehanna River that were used to calculate normal and storm conditions; turbidity measurements at each site; mass of particulate matter from water column and erosion experiments; inorganic phosphate from sequential extraction of particulate matter in water column, sediment, and erosion experiments; and δ<sup>15</sup>N, δ<sup>13</sup>C, and C:N ratios of particulate matter from the water column and from erosion experiments.
DOI: 10.5281/zenodo.6975848
title: Relative roles of sediment transport and localized erosion on phosphorus load in the lower Susquehanna River and its mouth in the Chesapeake Bay, USA

description: Data set includes average wind and wave conditions for the mouth of the Susquehanna River that were used to calculate normal and storm conditions; turbidity measurements at each site; mass of particulate matter from water column and erosion experiments; inorganic phosphate from sequential extraction of particulate matter in water column, sediment, and erosion experiments; and δ<sup>15</sup>N, δ<sup>13</sup>C, and C:N ratios of particulate matter from the water column and from erosion experiments.
DOI: 10.6084/m9.figshare.20459262.v1
title: 293T_6d.bw

description: big wig
DOI: 10.6084/m9.figshare.20459262
title: 293T_6d.bw

description: big wig