cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4824364
title: Dados bioclimáticos para modelagem de distribuição de espécies baseada em variáveis ambientais e aerossóis na região próxima a Manaus (AM)

description: Esse conjunto de dados foi gerado para possibilitar a realização de uma modelagem de distribuição de espécies baseada em variáveis ambientais e aerossóis na região próxima a Manaus (AM). Os dados climáticos foram obtidos por meio de uma interpolação espacial a partir dos dados coletados pelas aeronaves do projeto GOAmazon 2014/15. Já os dados de ocorrência de espécies foram obtidos a partir do Portal da Biodiversidade do Instituto Chico Mendes de Conservação da Biodiversidade (ICMBio) e do Global Biodiversity Information Facility (GBIF).
DOI: 10.5281/zenodo.4824365
title: Dados bioclimáticos para modelagem de distribuição de espécies baseada em variáveis ambientais e aerossóis na região próxima a Manaus (AM)

description: Esse conjunto de dados foi gerado para possibilitar a realização de uma modelagem de distribuição de espécies baseada em variáveis ambientais e aerossóis na região próxima a Manaus (AM). Os dados climáticos foram obtidos por meio de uma interpolação espacial a partir dos dados coletados pelas aeronaves do projeto GOAmazon 2014/15. Já os dados de ocorrência de espécies foram obtidos a partir do Portal da Biodiversidade do Instituto Chico Mendes de Conservação da Biodiversidade (ICMBio) e do Global Biodiversity Information Facility (GBIF).
DOI: 10.17909/f8pz-vj63
title: The TESS Stellar Variability Catalog ("TESS-SVC")

description: The TESS Stellar Variability Catalog (TESS-SVC) describes which stars, among those observed at 2-min cadence in the TESS light curve photometry, show periodic variability over the timescales of 0.01 to 13 days. The TESS-SVC includes a broad range of periodic variable stars (rotation, pulsation, eclipsing binaries, etc.) that were selected based on the photometric periodogram analysis described in Fetherolf et al. 2023. Each star in the TESS-SVC includes the measured period of photometric variability, time and amplitude of the maximum flux, significance of the detected variability signal, properties extracted from the TESS Input Catalog, and a summary figure. Each summary figure shows the full TESS light curve (left panels), Lomb-Scargle periodogram or auto-correlation function (center panels), and the light curve phase-folded on the measured photometric variability period (right panels). The periodogram results provided are extracted from a single sector of TESS PDCSAP photometry, and the method for selecting the specific sector for stars observed in multiple TESS sectors is described in Fetherolf et al. 2023.
DOI: 10.5281/zenodo.8226638
title: High-resolution surface wind observations over complex terrain: Big Southern Butte, Salmon River Canyon, Birch Creek

description: This dataset contains high-resolution wind observations from three field campaigns that took place during 2010-2014 at Big Southern Butte, Salmon River Canyon, and Birch Creek, Idaho. There are three SQLite databases containing 30-s averaged 3-m wind speed, wind direction, and wind gust data from 30-90 cup-and-vane anemometers over a period of 2-4 months at each field site.
description: Additional data including sodar, radar, sonic anemometer, and weather station data are available for select periods at each site. Contact natalie.s.wagenbrenner at usda dot gov for more information.
DOI: 10.5281/zenodo.8226637
title: High-resolution surface wind observations over complex terrain: Big Southern Butte, Salmon River Canyon, Birch Creek

description: This dataset contains high-resolution wind observations from three field campaigns that took place during 2010-2014 at Big Southern Butte, Salmon River Canyon, and Birch Creek, Idaho. There are three SQLite databases containing 30-s averaged 3-m wind speed, wind direction, and wind gust data from 30-90 cup-and-vane anemometers over a period of 2-4 months at each field site.
description: Additional data including sodar, radar, sonic anemometer, and weather station data are available for select periods at each site. Contact natalie.s.wagenbrenner at usda dot gov for more information.
DOI: 10.5281/zenodo.7312098
title: Does Code Review Speed Matter for Practitioners? A Qualitative Study of Code Velocity

description: This dataset contains the following: The results from a survey about code velocity. The R script to analyze the survey data.
DOI: 10.5281/zenodo.8242289
title: Does Code Review Speed Matter for Practitioners?

description: This dataset contains the following: The results from a survey about code velocity. The R script to analyze the survey data. The survey questionnaire.
DOI: 10.5281/zenodo.7312097
title: Does Code Review Speed Matter for Practitioners?

description: This dataset contains the following: The results from a survey about code velocity. The R script to analyze the survey data. The survey questionnaire.
DOI: 10.15139/s3/ouipot
title: Replication Data for: Rush hour-and-a-half: traffic is spreading out post-lockdown

description: This archive contains the data used for the peak spreading analysis in the paper "Rush hour-and-a-half: traffic is spreading out post-lockdown" by Matt Bhagat-Conway and Sam Zhang. It is downloaded from theCaltrans PEMS system, and documentation for the datasets is available from that system. We used the "Station 5-minute" and "Station meta" datasets. The data are created by Caltrans, but is expressly specified as being in the public domain. Due to technical limitations, the data are split across several tar files, one for each Caltrans district. All of the data files need to be moved into a single flat directory structure for the code to work. Within the tar files, there is one file for each day of the analysis period, from January 1, 2016 to August 18, 2022. Most are complete; a handful are truncated, but this is what was available from the PeMS system as of April 2023. Given the large size of the dataset these are not expected to influence results. All of the data are used to create the fundamental diagram figure in the paper, but for the main analysis it is filtered to weekdays in the defined pre-pandemic, lockdown, and post-lockdown periods. Metadata for sensors are in the `meta.tar` file. The metadata extend well past the analysis period, but are filtered when used to include only the analysis period and several days after (as sensors that moved shortly after the analysis are suspect).
DOI: 10.6084/m9.figshare.23960727
title: Phenotypes and genomes for: <b>The genomics and physiology of abiotic stressors associated with global elevation gradients in </b><i><b>Arabidopsis thaliana</b></i>

description: This data contains:1) Phenotypes taken in a diverse panel of ecotypes (up to 270) in three large-scale experiments were we measured plant responses to high elevation stressors: low partial CO<sub>2 </sub>pressure, high light, and night freezing. Also included are environmental conditions obtained from the CHELSA repository based on coordinates of origin. For a full explanation of abbreviations please contact Diana Gamba. 2) a VCF file contain ing biallelic SNPs of 16 newly sequenced Arabidopsis accessions from eastern Africa.3) genomic maps (bam files) of newly sequenced accessions mapped to the TAIR10 Arabidopsis thaliana reference genome.For methods, please refer to the associated manuscript.
DOI: 10.7278/s50d-0phb-z0x0
title: Dataset for: Geochemical database (Major and trace element) of Permian to Jurassic igneous samples from central Mongolia, south Russia, and NE China
DOI: 10.5281/zenodo.4317967
title: Aircraft Traffic Density Database: /Data

description: This dataset is the associated input for the software, traffic-density-database. It is intended to process observed aircraft traffic information into unmitigated collision rates given user specified geographic regions or aircraft tracks. The user may also specify the time of day, month of year, and day of week to be evaluated. Unmitigated collision rates are useful for aircraft safety assessments, and may be combined with the efficacy of mitigations—e.g., air traffic control, detect and avoid, see and avoid—in order to estimate the total operation safety. The input data included with this release has the following attributes. See the README in the Data directory for more detail on the contents of each data file. The data span 8727 observed hours, from 4 November 2018 to 3 November 2019. Note that there are periods where there are no radar data available, so the data span does not correspond directly to the full date range extent. There are over 31.65 million flight hours observed (includes both discrete-code and 1200-code aircraft). The vertical spatial (height) discretization corresponds to standard altitude feature thresholds in the national airspace: 100 feet Above Ground Level (AGL): Cutoff to exclude on-ground traffic 500 feet AGL: nominal lower manned aircraft operating altitude 1200 feet AGL: typical lower altitude for Class E airspace 3000 feet AGL: approximate upper bound for Class D airspace 5000 feet AGL: approximate upper bound for Class C airspace 10,000 feet Mean Sea Level (MSL): typical Class B upper bound, and lower threshold for transponder requirements 18,000 feet MSL: lower bound for Class A Airspace 29,000 feet MSL: Reduced Vertical Separation Minimum (RVSM) lower altitude bound 41,100 feet MLS: RVSM upper altitude bound 60,100 feet MSL: upper bound for Class A Airspace The temporal discretization is 3 hours. The latitude range is 22.83°N to 50.00°N. The longitude range is 127.00°W to 64.83°W. (Note: MATLAB indicates W longitude using negative numbers, e.g., 127.00°W is -127.00.) Two tables with different horizontal spatial discretization are provided: one for all altitudes, where the discretization is 1/6 of a degree in latitude and longitude (corresponding to approximately 10 NM); and one for altitudes below 5000 feet, where the discretization is 1/24 of a degree (approximately 2.5 NM). The table containing all altitudes is the default.
DOI: 10.5281/zenodo.8250626
title: Aircraft Traffic Density Database: /Data

description: This dataset is the associated input for the software, traffic-density-database. It is intended to process observed aircraft traffic information into unmitigated collision rates given user specified geographic regions or aircraft tracks. The user may also specify the time of day, month of year, and day of week to be evaluated. Unmitigated collision rates are useful for aircraft safety assessments, and may be combined with the efficacy of mitigations—e.g., air traffic control, detect and avoid, see and avoid—in order to estimate the total operation safety. The input data included with this release has the following attributes. See the README in the Data directory for more detail on the contents of each data file. The data span 8727 observed hours, from 4 November 2018 to 3 November 2019. Note that there are periods where there are no radar data available, so the data span does not correspond directly to the full date range extent. There are over 31.65 million flight hours observed (includes both discrete-code and 1200-code aircraft). The vertical spatial (height) discretization corresponds to standard altitude feature thresholds in the national airspace: 100 feet Above Ground Level (AGL): Cutoff to exclude on-ground traffic 500 feet AGL: nominal lower manned aircraft operating altitude 1200 feet AGL: typical lower altitude for Class E airspace 3000 feet AGL: approximate upper bound for Class D airspace 5000 feet AGL: approximate upper bound for Class C airspace 10,000 feet Mean Sea Level (MSL): typical Class B upper bound, and lower threshold for transponder requirements 18,000 feet MSL: lower bound for Class A Airspace 29,000 feet MSL: Reduced Vertical Separation Minimum (RVSM) lower altitude bound 41,100 feet MLS: RVSM upper altitude bound 60,100 feet MSL: upper bound for Class A Airspace The temporal discretization is 3 hours. The latitude range is 22.83°N to 50.00°N. The longitude range is 127.00°W to 64.83°W. (Note: MATLAB indicates W longitude using negative numbers, e.g., 127.00°W is -127.00.) Two tables with different horizontal spatial discretization are provided: one for all altitudes, where the discretization is 1/6 of a degree in latitude and longitude (corresponding to approximately 10 NM); and one for altitudes below 5000 feet, where the discretization is 1/24 of a degree (approximately 2.5 NM). The table containing all altitudes is the default.
DOI: 10.5281/zenodo.8250633
title: Aircraft Traffic Density Database: /Data

description: This dataset is the associated input for the software, traffic-density-database. It is intended to process observed aircraft traffic information into unmitigated collision rates given user specified geographic regions or aircraft tracks. The user may also specify the time of day, month of year, and day of week to be evaluated. Unmitigated collision rates are useful for aircraft safety assessments, and may be combined with the efficacy of mitigations—e.g., air traffic control, detect and avoid, see and avoid—in order to estimate the total operation safety. The input data included with this release has the following attributes. See the README in the Data directory for more detail on the contents of each data file. The data span 8727 observed hours, from 4 November 2018 to 3 November 2019. Note that there are periods where there are no radar data available, so the data span does not correspond directly to the full date range extent. There are over 31.65 million flight hours observed (includes both discrete-code and 1200-code aircraft). The vertical spatial (height) discretization corresponds to standard altitude feature thresholds in the national airspace: 100 feet Above Ground Level (AGL): Cutoff to exclude on-ground traffic 500 feet AGL: nominal lower manned aircraft operating altitude 1200 feet AGL: typical lower altitude for Class E airspace 3000 feet AGL: approximate upper bound for Class D airspace 5000 feet AGL: approximate upper bound for Class C airspace 10,000 feet Mean Sea Level (MSL): typical Class B upper bound, and lower threshold for transponder requirements 18,000 feet MSL: lower bound for Class A Airspace 29,000 feet MSL: Reduced Vertical Separation Minimum (RVSM) lower altitude bound 41,100 feet MLS: RVSM upper altitude bound 60,100 feet MSL: upper bound for Class A Airspace The temporal discretization is 3 hours. The latitude range is 22.83°N to 50.00°N. The longitude range is 127.00°W to 64.83°W. (Note: MATLAB indicates W longitude using negative numbers, e.g., 127.00°W is -127.00.) Two tables with different horizontal spatial discretization are provided: one for all altitudes, where the discretization is 1/6 of a degree in latitude and longitude (corresponding to approximately 10 NM); and one for altitudes below 5000 feet, where the discretization is 1/24 of a degree (approximately 2.5 NM). The table containing all altitudes is the default.
DOI: 10.5281/zenodo.4317966
title: Aircraft Traffic Density Database: /Data

description: This dataset is the associated input for the software, traffic-density-database. It is intended to process observed aircraft traffic information into unmitigated collision rates given user specified geographic regions or aircraft tracks. The user may also specify the time of day, month of year, and day of week to be evaluated. Unmitigated collision rates are useful for aircraft safety assessments, and may be combined with the efficacy of mitigations—e.g., air traffic control, detect and avoid, see and avoid—in order to estimate the total operation safety. The input data included with this release has the following attributes. See the README in the Data directory for more detail on the contents of each data file. The data span 8727 observed hours, from 4 November 2018 to 3 November 2019. Note that there are periods where there are no radar data available, so the data span does not correspond directly to the full date range extent. There are over 31.65 million flight hours observed (includes both discrete-code and 1200-code aircraft). The vertical spatial (height) discretization corresponds to standard altitude feature thresholds in the national airspace: 100 feet Above Ground Level (AGL): Cutoff to exclude on-ground traffic 500 feet AGL: nominal lower manned aircraft operating altitude 1200 feet AGL: typical lower altitude for Class E airspace 3000 feet AGL: approximate upper bound for Class D airspace 5000 feet AGL: approximate upper bound for Class C airspace 10,000 feet Mean Sea Level (MSL): typical Class B upper bound, and lower threshold for transponder requirements 18,000 feet MSL: lower bound for Class A Airspace 29,000 feet MSL: Reduced Vertical Separation Minimum (RVSM) lower altitude bound 41,100 feet MLS: RVSM upper altitude bound 60,100 feet MSL: upper bound for Class A Airspace The temporal discretization is 3 hours. The latitude range is 22.83°N to 50.00°N. The longitude range is 127.00°W to 64.83°W. (Note: MATLAB indicates W longitude using negative numbers, e.g., 127.00°W is -127.00.) Two tables with different horizontal spatial discretization are provided: one for all altitudes, where the discretization is 1/6 of a degree in latitude and longitude (corresponding to approximately 10 NM); and one for altitudes below 5000 feet, where the discretization is 1/24 of a degree (approximately 2.5 NM). The table containing all altitudes is the default.
DOI: 10.23728/b2share.63f3419b8ab149b2b090fd93c93c9f74
title: ¿Aglomeración en la periferia? Análisis exploratorio espacial de flujos de transporte de bienes agrícolas en Perú [Data set]

description: El papel de los costes de transporte de bienes agrícolas en los modelos centro-periferia ha sido normalmente ignorado en la literatura de la Nueva Geografía Económica (NGE). Esto se debe a la convención de tratar el bien agrícola como una constante, asumiendo un valor nulo de los costes de transporte agrícola en estos modelos. El objetivo de este capítulo es examinar el impacto de los costes del transporte agrícola en la estructura espacial de la economía regional de Perú. Para ello, se presenta una reformulación del modelo de costes de transporte tipo iceberg de Krugman, para considerar el sector agrícola. El análisis empírico emplea datos reales de transporte de los productos agrícolas y métodos de análisis exploratorio de datos espaciales de origen-destino para analizar cómo cambia la estructura espacial regional cuando los costes de transporte agrícola son incluidos en la dinámica de los modelos NGE. Los resultados del presente capítulo muestran que los costes de transporte agrícola generan flujos que se asocian sistemáticamente con los flujos hacia o desde regiones cercanas, generando efectos espaciales de desbordamiento (“spillover”) en el sector agrícola. Estos resultados ponen de manifiesto la significatividad de la variable de transporte agrícola en cuanto generadora de economías de aglomeración.
description: Análisis exploratorio de datos de flujos espaciales.
description: Cómo citar la base de datos (estilo APA): Herrera-Catalán, P.; Chasco, C. (2023). ¿Aglomeración en la periferia? Análisis exploratorio espacial de flujos de transporte de bienes agrícolas en Perú [Data set]. (doi: 10.23728/b2share.63f3419b8ab149b2b090fd93c93c9f74). Fuente: Herrera-Catalán P, Chasco C (2023) ¿Aglomeración en la periferia? Análisis exploratorio espacial de flujos de transporte de bienes agrícolas en Perú. In Parejo FM, Rangel JF, Linares AM (eds): Organización de la Producción, Instituciones y Cooperación Empresarial. Estudios Aplicados para el Desarrollo Rural. Editorial Dykinson: Madrid; pp. 177-197. https://www.jstor.org/stable/jj.5076297.12
DOI: 10.18739/a2862bd1g
title: Blossom Shoals water-column, seabed sediment, and bathymetry data. Icy Cape, Chukchi Sea, Alaska. 2019-2020.

description: This dataset includes vessel-based water-column profile and seabed data collected around Blossom Shoals, a shoal complex offshore of Icy Cape in northwestern Alaska (in the Chukchi Sea). Data were collected from the Research Vessel (R/V) Sikuliaq (offshore) and a companion workboat (inshore). Water-column profile data include salinity, temperature, depth, and turbidity data collected using a RBR Maestro CTD/Tu (conductivity, temperature, depth, turbidity) sensor package. Profile data also include median diameters and volumetric concentrations of suspended particles, where were collected using a Sequoia LISST200X (laser in situ scattering transmissometer). Seabed grab samples were collected from the Sikuliaq using a shipek grab sampler and from the workboat using a hand-operated mini van veen grab sampler. Samplers were bagged and returned chilled to the lab for particle-size analyses in an Escitec Bettersizer S3Plus laser diffraction sensor. Sediments were not treated for organics due to generally low organic contents. Samples contained primarily sand except for a few isolated locations where mud was found. Data were collected in November 2019 during the fall freezeup season when pancake ice were beginning to form. Data were also collected in late September and early October 2020 during a mooring recovery cruise. Single-beam bathymetry data (which were only collected in 2020) were gathered using a commercial fish finder mounted on the workboat and connected to a data logger.
DOI: 10.6078/m7r20zh6
title: The Mesoamerican Turkey Project / Proyecto Guajolote Mesoamericano
DOI: 10.5517/ccdc.csd.cc24134g
title: CCDC 1967264: Experimental Crystal Structure Determination

description: Related Article: Mohd Mustufa, Ambreen Abbasi, Summaiya Hanif, Zia ul Haq Bhat, Hina kabeer, Mohammad Jane Alam, Musheer Ahmad, Mohammad Shakir
|2023|Chem. Inorg. Mater.|1|100011|doi:10.1016/j.cinorg.2023.100011
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.34740/kaggle/dsv/6308780
title: Balochi Language to English Translation Dataset
DOI: 10.5281/zenodo.8250677
title: Supplementary Files and Data of 'Live-bearing cockroach genome reveals convergent evolutionary mechanisms linked to viviparity in insects and beyond'

description: Live birth (viviparity) has arisen repeatedly and independently among animals. We sequenced the genome and transcriptome of the viviparous Pacific beetle-mimic cockroach and performed comparative analyses with two other viviparous insect lineages, tsetse flies and aphids, to unravel the basis underlying the transition to viviparity in insects. We identified pathways undergoing adaptive evolution for insects, involved in uro-genital remodeling, tracheal system, heart development, and nutrient metabolism. Transcriptomic analysis in the cockroach and tsetse viviparity reveals that uterine remodeling and nutrient production are increased and the immune response is altered during pregnancy, facilitating structural and physiological changes to accommodate and nourish the progeny. These patterns of convergent evolution of viviparity among insects, together with similar adaptive mechanisms identified among vertebrates, highlight that the transition to viviparity requires changes in uro-genital remodeling, enhanced tracheal and heart development (corresponding to angiogenesis in vertebrates), nutrient metabolism, and shifted immunity in all animal systems.