cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc21tprb
title: CCDC 1901532: Experimental Crystal Structure Determination

description: Related Article: N. Sutthirat, J.W. Ziller, J.Y. Yang, Z. Thammavongsy|2019|Acta Crystallogr.,Sect.E:Cryst.Commun.|75|438|doi:10.1107/S2056989019003256
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: 10.14469/hpc/5099
title: FAIR Data table. Computed relative free energies of cyclic chiral lactols

description: FAIR Data table. Computed relative free energies of cyclic chiral lactols
DOI: 10.5281/zenodo.2583977
title: The Software Heritage Graph Dataset

description: Software Heritage is the largest existing public archive of software source<br> code and accompanying development history: it currently spans more than five<br> billion unique source code files and one billion unique commits, coming from<br> more than 80 million software projects. This is the Software Heritage graph dataset: a fully-deduplicated<br> Merkle DAG representation of the Software Heritage archive. The dataset links<br> together file content identifiers, source code directories, Version Control<br> System (VCS) commits tracking evolution over time, up to the full states of VCS<br> repositories as observed by Software Heritage during periodic crawls. The<br> dataset’s contents come from major development forges (including GitHub and<br> GitLab), FOSS distributions (e.g., Debian), and language-specific package<br> managers (e.g., PyPI). Crawling information is also included, providing<br> timestamps about when and where all archived source code artifacts have been<br> observed in the wild. The Software Heritage graph dataset is available in multiple formats, including<br> downloadable CSV dumps and Apache Parquet files for local use, as well as a<br> public instance on Amazon Athena interactive query service for ready-to-use<br> powerful analytical processing. By accessing the dataset, you agree with the Software Heritage Ethical Charter<br> for using the archive data, and the terms of use for bulk access. If you use this dataset for research purposes, please cite the following paper: Antoine Pietri, Diomidis Spinellis, Stefano Zacchiroli. <br> <em>The Software Heritage Graph Dataset: Public software development under one roof</em>. <br> In proceedings of MSR 2019: The 16th International Conference on Mining Software Repositories, May 2019, Montreal, Canada. Co-located with ICSE 2019. <br> preprint, bibtex You can also refer to the above paper for more information the dataset and sample queries.
DOI: 10.5281/zenodo.2583978
title: The Software Heritage Graph Dataset

description: Software Heritage is the largest existing public archive of software source<br> code and accompanying development history: it currently spans more than five<br> billion unique source code files and one billion unique commits, coming from<br> more than 80 million software projects. This is the Software Heritage graph dataset: a fully-deduplicated<br> Merkle DAG representation of the Software Heritage archive. The dataset links<br> together file content identifiers, source code directories, Version Control<br> System (VCS) commits tracking evolution over time, up to the full states of VCS<br> repositories as observed by Software Heritage during periodic crawls. The<br> dataset’s contents come from major development forges (including GitHub and<br> GitLab), FOSS distributions (e.g., Debian), and language-specific package<br> managers (e.g., PyPI). Crawling information is also included, providing<br> timestamps about when and where all archived source code artifacts have been<br> observed in the wild. The Software Heritage graph dataset is available in multiple formats, including<br> downloadable CSV dumps and Apache Parquet files for local use, as well as a<br> public instance on Amazon Athena interactive query service for ready-to-use<br> powerful analytical processing. By accessing the dataset, you agree with the Software Heritage Ethical Charter<br> for using the archive data, and the terms of use for bulk access. If you use this dataset for research purposes, please cite the following paper: Antoine Pietri, Diomidis Spinellis, Stefano Zacchiroli. <br> <em>The Software Heritage Graph Dataset: Public software development under one roof</em>. <br> In proceedings of MSR 2019: The 16th International Conference on Mining Software Repositories, May 2019, Montreal, Canada. Co-located with ICSE 2019. <br> preprint, bibtex You can also refer to the above paper for more information the dataset and sample queries.
DOI: 10.7910/dvn/qctbp5
title: Rohingya Youth Protection Survey- 2018

description: The current situation in Bangladesh and Myanmar constitutes a humanitarian crisis with a complex set of political and human rights sensitivities. Since August 2017, over 700,000 Rohingya Muslims have crossed the border from Myanmar into Bangladesh. These refugees have been living in camps in the Ukhia and Teknaf sub-districts for almost a year by now, but many pressing needs and concerns remain for this community. Through a rapid needs assessment and focus group discussions which informed the first part of this study, key informant interviews with various humanitarian relief agency workers and the BRAC response team, and a review of the grey literature, it became clear that there were grave information gaps around protection concerns. As such, this study will investigate protection concerns, especially for adolescents and young adults, as well as the experience of key community leaders on the subject of life in the camp and the potential for repatriation. (2018-09-19)
DOI: 10.15129/af09df5c-a25d-4c8c-88d9-8a6df1d8213d
title: Data for: "Combining mathematical modelling with in vitro experiments to predict in vivo drug-eluting stent performance"

description: Drug-eluting stents are the preferred treatment for symptomatic coronary artery disease.However, despite rapid evolution of the stent design there remain limitations in their use and efficacy.  The precise contribution of the drug dose and release kinetics from stents to device efficacy is unclear and existing models are limited in their capacity to fully evaluate this. This work developed a predictive computational model of drug release and tissue distribution from the stent into the artery wall tissue, using in vitro data and validating with in vivo data.  The data used to inform and validate this model are contained within his repository,
DOI: 10.5281/zenodo.2591452
title: Trained CNN for skin detection - https://arxiv.org/abs/1802.02531

description: Trained CNN for skin detection https://arxiv.org/abs/1802.02531
DOI: 10.5281/zenodo.2591453
title: Trained CNN for skin detection - https://arxiv.org/abs/1802.02531

description: Trained CNN for skin detection

https://arxiv.org/abs/1802.02531
DOI: 10.5517/ccdc.csd.cc21vvrj
title: CCDC 1902648: Experimental Crystal Structure Determination

description: Related Article: Brian W. Skelton, Massimiliano Massi|2019|CSD Communication|||
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: 10.14469/hpc/5103
title: (R,S,R,S), methyl axial, N eq +BJ -1625.863060

description: Gaussian Calculation
DOI: 10.5066/p9kkb3h2
title: Flood inundation and flood depth for the Yellow River in Gwinnett County, Georgia based on water-surface elevation at the U.S. Geological Survey streamgages Yellow River, near Snellville, Georgia (02206500) and Yellow River at Ga. 124, near Lithonia, Georgia (02207120)

description: The digital flood-inundation maps included in this data release were developed for a 16.4-mile reach of the Yellow River from 0.5-mile upstream of River Drive to Centerville Highway (Georgia State Route 124), Gwinnett County, Georgia (Ga.) to depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at two U.S. Geological Survey (USGS) streamgages in the mapped area. The maps for the 9.0-mile reach from 0.5-mile upstream of River Drive to Stone Mountain Highway (US Route 78) are referenced to the Yellow River, near Snellville, Georgia (Ga.; station 02206500), and the maps for the 7.4-mile reach from Stone Mountain Highway to Centerville Highway are referenced to the streamgage Yellow River at Ga. 124, near Lithonia, Ga. (02207120). Real-time stage information from these streamgages can be used with these maps to estimate near real-time areas of inundation. The forecasted peak-stage information for the USGS streamgages Yellow River, near Snellville, Ga. (02206500), and Yellow River at Ga. 124, near Lithonia, Ga. (02207120), can be used in conjunction with the maps developed for this study to show predicted areas of flood inundation. A one-dimensional step-backwater model was developed using the U.S. Army Corps of Engineers Hydrologic Engineering Center's River Analysis System (HEC-RAS) software for the Yellow River and was used to compute flood profiles for this 16.4-mile reach of the Yellow River. The hydraulic model was then used to simulate 16 water-surface profiles at 1.0-foot (ft) intervals at the Yellow River near Snellville, Ga. streamgage and 17 water-surface profiles at 1.0-ft intervals at the Yellow River near Lithonia, Ga. streamgage. At the Yellow River near Snellville, Ga. streamgage, the profiles ranged from a stage of 18.0 ft, which is 819.1 ft above the North American Vertical Datum of 1988 (NAVD 88), to a stage of 33.0 ft, which is 834.1 ft above NAVD 88. At the Yellow River at Ga. 124, near Lithonia, Ga. streamgage, the profiles ranged from the National Weather Service action stage of 13.0 ft, which is 732.5 ft above NAVD 88, to a stage of 29.0 ft, which is 748.5 ft above NAVD 88. The simulated water-surface profiles were then combined with a geographic information system digital elevation model-derived from light detection and ranging (lidar) data having a 5.0-ft horizontal resolution-to delineate the area flooded at each 1.0-ft interval of stream stage for each streamgage. Information about the study, floods, and methods used can be found in the USGS Scientific Investigations Report 2019-5009 online at https://doi.org/10.3133/sir20195009.
DOI: 10.25345/c5mg8z
title: MassIVE MSV000083557 - Soil Metaproteome of two Canga associated sites
title: Soil metaproteome of two sites near mining operation in Southern Brazil
DOI: 10.25345/c5r63j
title: MassIVE MSV000083556 - GNPS Mass Spectrometric Assessment of Talaromyces cecidicola and N12
title: Talaromyces cecidicola coculture with another microbe N12
DOI: 10.5063/f16971vp
title: Commercial fishery seafood products information from Alaska Commercial Operator's Annual Reports (COAR), 1984 to 2017

description: Three datasets from the Alaska Commercial Operator's Annual Reports (COAR) are included: 1) annual wholesale production, both weight and value for fish and shellfish products from 1984-2016, 2) average wholesale price per pound for processed salmon products from 2000-2016, and 3) statewide salmon wholesale value and pounds processed from 1984-2017. With these COAR datasets, all commercial products that are caught, bought, sold, and processed are required to be submitted annually. Within the COAR reports, information including the species, area of purchase (based on the statistical regions from the Alaska Department of Fish and Wildlife (ADF&amp;G), pounds and value, process type (e.g. frozen, fresh) and production information (e.g. fillets, smoked) are reported. All prices and weights are based off the first wholesale value.
DOI: 10.17632/hpfprmy49b.1
title: Data for: HClaimE: A Tool to Identify Health Claims in Health News Headlines

description: This data set contains 564 health research news headlines with manual annotations of the health claims in the headlines and metadata such as publication dates and sources. The headlines were selected from news articles published on ScienceDaily.com from January 2016 to June 2017, including 212 headlines on breast cancer and 352 on diabetes. The news articles came from 286 different sources, such as Scripps Research Institute. A health claim is defined as a triple construct (a triplet); it is made up of an independent variable (IV – namely, what is being manipulated), a dependent variable (DV – namely, what is being measured), and the relation between the two. Among the 564 headlines, 416 contain health claims, while the other 148 headlines do not.
DOI: 10.5066/p9z0sbkz
title: Lake Erie Fish Community Data, 2013-2018

description: Assessing the distribution and abundance of both predator and prey (forage) fish species is a cornerstone of ecosystem-based based fishery management, and supports decision making that considers food-web interactions. In support of binational Great Lakes fishery management the objectives of this survey were to: provide estimates of densities of key forage and predator species in the western basin of Lake Erie, to assess seasonal and spatial distributions of fishes, and to assess year class strength. A systematic grid sampling approach with 41 stations was sampled during June (Spring) and September (Autumn), starting in 2013. This data release adds 2018 data to the set for a total of six years observation using the same gear and sampling design.
DOI: 10.6073/pasta/0fdb0fd1ecfdc6a79281341d7bae75a2
title: LTER Meteorological Stations at the Kellogg Biological Station, Hickory Corners, MI  (1988 to 2018)

description: Dataset Abstract
        Meteorological data have been collected on the LTER main site since 1988.
Originally, wind direction and speed, total solar radiation, net solar radiation, rainfall, air temperature, and relative humidity were recorded hourly in connection with measurements of evapotranspiration and deep percolation from a 6 m2 in-ground weighing lysimeter. The lysimeter and fetch area were planted to continuous alfalfa.
Although the lysimeter is no longer in operation, weather data collection continues. Precipitation was measured with a tipping bucket rain gauge until fall of 1997, when it was replaced with a weighing bucket rain gauge. In 2007 the belford rain gauge was replaced by a NOAH IV weighing bucket gage. All other sensors are electronic, connected to a data logger that is polled remotely. Instruments to measure wind speed and direction are mounted on a tower at 10 m. Light sensors (net solar radiation and photosynthetically active radiation) a thermometer, and an RH probe (with a built-in thermometer) are mounted at about 3 m. Soil moisture and temperature are monitored near the base of the tower.
        original data source http://lter.kbs.msu.edu/datasets/5
DOI: 10.5281/zenodo.2591184
title: supplement_ahrens_et_al_mar_2019

description: Data include all force-distance-raw-data of the article <em>A Combined AFM and Lateral Stretch Device Enables Microindentation Analyses of Living Cells at High Strains. </em>Furthermore, images used for local strain calibration are included.
DOI: 10.6073/pasta/da1dfba410bfe4a28e574ae6403eddc7
title: SGS-LTER Standard Production Data: 1983-2008 Annual Aboveground Net Primary Production on the Central Plains Experimental Range, Nunn, Colorado, USA 1983-2008, ARS Study Number 6 (Reformatted to ecocomDP Design Pattern)

description: This data package is formatted according to the "ecocomDP", a data package design pattern for ecological community surveys, and data from studies of composition and biodiversity. For more information on the ecocomDP project see https://github.com/EDIorg/ecocomDP/tree/master, or contact EDI https://environmentaldatainitiative.org.

This Level 1 data package was derived from the Level 0 data package found here: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-sgs&identifier=700&revision=1

The abstract below was extracted from the Level 0 data package and is included for context:
DOI: 10.5281/zenodo.2591183
title: supplement_ahrens_et_al_mar_2019

description: Data include all force-distance-raw-data of the article <em>A Combined AFM and Lateral Stretch Device Enables Microindentation Analyses of Living Cells at High Strains. </em>Furthermore, images used for local strain calibration are included.
DOI: 10.15139/s3/kisnxi
title: MCIP Model Version 4.3 -- 1/2015-12/2015 Continental US_12km

description: Data Summary: MCIPv4.3 output data from a January to December 2015 simulation over the Continental US based on WRFv3.8.Note:The datasets are on a Google Drive. The metadata associated with this DOI contain the link to the Google Drive folder and instructions for downloading the data. File Location and Download Instructions:  Link to MCIP Output FilesDownload instructions File Format:The 2015 model output are stored as netcdf formatted files using I/O API data structures (https://www.cmascenter.org/ioapi/). Information on the model projection and grid structure is contained in the header information of the netcdf file. The output files can be opened and manipulated using I/O API utilities (e.g. M3XTRACT, M3WNDW) or other software programs that can read and write netcdf formatted files (e.g. Fortran, R, Python).
DOI: 10.5281/zenodo.2590675
title: Thermodynamic data for astrochemistry

description: Collection of thermodynamic properties of 78 chemical species. The properties include partition functions, rotational and vibrational temperatures, electronic energy levels, the electronic potential energies at 0K., and several minimum energy geometries. The main purpose of this data is to compute the Gibbs free energies of the species, to be used in chemical reaction rates. Therefore, these are provided in the output directory (up to 3000K). A easily exportable JSON file with all thermodynamic properties is also available in the output directory. The data has partially been collected from several databases and individual papers. Missing data has been computed by ourselves. All input data is homogenised to a single format, which is not the case when collected from different sources. The reference folder contains extensive information on the sources where we got the data from. More detailed explanations can be found in the several README files. All processing has been performed using my repository of python scripts (https://bitbucket.org/JelsB/thermochemistry). We encourage people to extend the scripts with more features and extend the data with more chemical species. This work strives towards consistency, since we (and others) found inconsistencies between the thermodynamic databases <em>NIST/JANAF tables</em> and NASA's <em>Third Millennium Ideal Gas and Condensed Phase Thermochemical Database for Combustion with Updates from Active Thermochemical Tables. </em>
description: This data is produced and used by Boulangier et al. 2019b (subm.)
DOI: 10.5281/zenodo.2590674
title: Thermodynamic data for astrochemistry

description: Collection of thermodynamic properties of 78 chemical species. The properties include partition functions, rotational and vibrational temperatures, electronic energy levels, the electronic potential energies at 0K., and several minimum energy geometries. The main purpose of this data is to compute the Gibbs free energies of the species, to be used in chemical reaction rates. Therefore, these are provided in the output directory (up to 3000K). A easily exportable JSON file with all thermodynamic properties is also available in the output directory. The data has partially been collected from several databases and individual papers. Missing data has been computed by ourselves. All input data is homogenised to a single format, which is not the case when collected from different sources. The reference folder contains extensive information on the sources where we got the data from. More detailed explanations can be found in the several README files. All processing has been performed using my repository of python scripts (https://bitbucket.org/JelsB/thermochemistry). We encourage people to extend the scripts with more features and extend the data with more chemical species. This work strives towards consistency, since we (and others) found inconsistencies between the thermodynamic databases <em>NIST/JANAF tables</em> and NASA's <em>Third Millennium Ideal Gas and Condensed Phase Thermochemical Database for Combustion with Updates from Active Thermochemical Tables. </em>
description: This data is produced and used by Boulangier et al. 2019b (subm.)
DOI: 10.15139/s3/gulg2k
title: Vision Zero Boston Action Plan (2016). Boston, Massachusetts

description: Vision Zero is a strategy to reduce all traffic fatalities and severe injuries to zero, while increasing safe, equitable, and healthy mobility for all. Vision Zero Plans help guide municipalities, counties, and other areas to address these strategies considering the local context.
DOI: 10.17632/mkz9zv8gc6.1
title: Data for: Islandscapes in the Anthropocene: fire as a motor of rapid environmental degradation during the first peopling of Malta 7500 years ago

description: Data associated with the article "Islandscapes in the Anthropocene: fire as a motor of rapid environmental degradation during the first peopling of Malta 7500 years ago" submitted for review to QSR.
DOI: 10.5281/zenodo.2591503
title: TAU Urban Acoustic Scenes 2019 Openset, Development dataset

description: TAU Urban Acoustic Scenes 2019 Openset development dataset is designed for researching open set classification problem in acoustic scene classification. In this problem, the test recording may be from a different environment than the 10 target classes. The dataset consists of 10-seconds audio segments from 10 acoustic scenes: Airport - <em>airport</em> Indoor shopping mall - <em>shopping_mall</em> Metro station - <em>metro_station</em> Pedestrian street - <em>street_pedestrian</em> Public square - <em>public_square</em> Street with medium level of traffic - <em>street_traffic</em> Travelling by a tram - <em>tram</em> Travelling by a bus - <em>bus</em> Travelling by an underground metro - <em>metro</em> Urban park - <em>park</em> In addition, material for Unknown scene `unknown` is provided from 4 scenes: Beach - <em>beach</em>, extracted from TUT Acoustic scenes 2017 dataset Library - <em>library</em>, extracted from TUT Acoustic scenes 2017 dataset Office - <em>office</em>, extracted from TUT Acoustic scenes 2017 dataset Public event - <em>public_event</em>
DOI: 10.5063/f1xs5sp4
title: Commercial loans data, Alaska

description: This dataset includes Alaska commercial loan data from 1976-2016. These data were used for the State of Alaska Salmon and People (SASAP) project to examine fisheries related loans. The goal of the commercial fishing loan program is to provide long-term, low interest loans to improve the quality of Alaska seafood products. The program is operated by the Alaska Department of Commerce, Community, and Economic Development; Division of Economic Development. The loans promote development of resident fisheries and maintenance of commercial fishing vessels and gear and are available to individuals who have been Alaska residents for the past 2 years. Loans are available for purchases made within the 12 months prior to loan application or to refinance vessel or gear loans made by other lenders more than 12 months before loan application. Interest rates are fixed at the time of loan approval. The fisheries related loans data used by SASAP are associated with hatcheries (fisheries enhancement) and individual fishermen, and can be identified using the "Fund Abbreviation" column, with relevant codes being CF (Commercial Fishing) and FE (Fisheries Enhancement). The purposes of these codes can be looked up in the provided list of current and historical loan codes.