cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3471983
title: Code and Comments Dataset

description: The code and comment data are a compilation of code blocks and their related comments. Doxygen successfully ran on 106,304 different GitHub projects. A total of 16,115,540 code-comment pairs were obtained by running Doxygen on C, C++, Java, and Python projects. The source code in these pairs can be of various granularities: classes, methods, functions, and variables. These data provide an association between source code and a description of that code. The data directory contains one directory for each project downloaded from GitHub. These project directories are named with the GraphQL ID from GitHub's GraphQL API. In each of these GraphQL-ID labeled directories, there is a license.txt, a url.txt, and a derivatives directory. The license.txt contains the license for the original project, the url.txt contains a link to the original project on GitHub, and the derivatives directory contains the output of running Doxygen on the project. The Doxygen output is a json file, structured as a dictionary with a "contents" field, where the value of that field is a list of lists containing 3 elements each. The following is a mock example of that structure: {"contents": [[path1, snippet1, comment1], [path2, snippet2, comment2], ...]}. The "path" is a filepath relative to the original project from which the code and comment were obtained. The "snippet" is the actual body of the source code. The "comment" is the corresponding comment. For convenience, there is also an initialize.py python script that iterates through all of the json files in the data directory and stores them in an SQLite database called "all_data.db".
DOI: 10.5281/zenodo.3471984
title: deleted

description: deleted
DOI: 10.5281/zenodo.3471985
title: Code and Comments Dataset

description: The code and comment data are a compilation of code blocks and their related comments. Doxygen successfully ran on 106,304 different GitHub projects. A total of 16,115,540 code-comment pairs were obtained by running Doxygen on C, C++, Java, and Python projects. The source code in these pairs can be of various granularities: classes, methods, functions, and variables. These data provide an association between source code and a description of that code. The data directory contains one directory for each project downloaded from GitHub. These project directories are named with the GraphQL ID from GitHub's GraphQL API. In each of these GraphQL-ID labeled directories, there is a license.txt, a url.txt, and a derivatives directory. The license.txt contains the license for the original project, the url.txt contains a link to the original project on GitHub, and the derivatives directory contains the output of running Doxygen on the project. The Doxygen output is a json file, structured as a dictionary with a "contents" field, where the value of that field is a list of lists containing 3 elements each. The following is a mock example of that structure: {"contents": [[path1, snippet1, comment1], [path2, snippet2, comment2], ...]}. The "path" is a filepath relative to the original project from which the code and comment were obtained. The "snippet" is the actual body of the source code. The "comment" is the corresponding comment. For convenience, there is also an initialize.py python script that iterates through all of the json files in the data directory and stores them in an SQLite database called "all_data.db".
DOI: 10.5281/zenodo.3472032
title: Code and Comments Dataset

description: The code and comment data are a compilation of code blocks and their related comments. Doxygen successfully ran on 106,304 different GitHub projects. A total of 16,115,540 code-comment pairs were obtained by running Doxygen on C, C++, Java, and Python projects. The source code in these pairs can be of various granularities: classes, methods, functions, and variables. These data provide an association between source code and a description of that code. The data directory contains one directory for each project downloaded from GitHub. These project directories are named with the GraphQL ID from GitHub's GraphQL API. In each of these GraphQL-ID labeled directories, there is a license.txt, a url.txt, and a derivatives directory. The license.txt contains the license for the original project, the url.txt contains a link to the original project on GitHub, and the derivatives directory contains the output of running Doxygen on the project. The Doxygen output is a json file, structured as a dictionary with a "contents" field, where the value of that field is a list of lists containing 3 elements each. The following is a mock example of that structure: {"contents": [[path1, snippet1, comment1], [path2, snippet2, comment2], ...]}. The "path" is a filepath relative to the original project from which the code and comment were obtained. The "snippet" is the actual body of the source code. The "comment" is the corresponding comment. For convenience, there is also an initialize.py python script that iterates through all of the json files in the data directory and stores them in an SQLite database called "all_data.db".
DOI: 10.6073/pasta/c30c1c56510319f4f16c504027a3120d
title: Jornada Basin LTER: Wireless meteorological station at NPP P-TOBO site: 1-hour summary data: 2013 - ongoing

description: Hourly summary data at NPP P-TOBO met station. Average/maximum/minimum air temperature; average relative humidity and wind direction; average/maximum wind speed; solar radiation; albedo. These are measured and calculated based on 1-second scan rate of all sensors located at an automated meteorological station installed at Jornada LTER NPP P-TOBO site. Wind speed is measured at 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature and relative humidity at approximate 2.5m. Solar radiation is measured at 3m. This climate station is operated by the Jornada LTER Program. This is an ONGOING dataset.
DOI: 10.5281/zenodo.3472031
title: DWD European Weather 2

description: opendata.dwd.de - OpenData by Deutscher Wetter Dienst Conditions: https://www.dwd.de/EN/service/copyright/copyright_node.html dates and times are UTC.
DOI: 10.5281/zenodo.3472034
title: Code and Build Artifacts Dataset

description: The code and build artifacts are a compilation of source code projects and their related build outputs. The build process, which consisted of running the make command, successfully ran on 3,049 different GitHub projects. Over 30,000 build outputs were produced from C and C++ projects. The build outputs are the results of running a particular project's make command. These derivatives can include executables, .o files, .so files, .a files, or other project-specific build artifacts. The output was accepted as long as the make command completed without error; thus, there is no guarantee that every project will contain every type of artifact. These data provide an association between source code and the build artifacts of that code. The data directory contains one directory for each project downloaded from GitHub. These project directories are named with the GraphQL ID from GitHub's GraphQL API. In each of these GraphQL-ID labeled directories, there is a license.txt, a url.txt, source directory, and a derivatives directory. The license.txt contains the license for the original project, the url.txt contains a link to the original project on GitHub, the source directory contains the original code, and the derivatives directory contains the outputs from building the project, which include the previously mentioned files.
DOI: 10.5281/zenodo.3472035
title: Code and Build Artifacts Dataset

description: The code and build artifacts are a compilation of source code projects and their related build outputs. The build process, which consisted of running the make command, successfully ran on 3,049 different GitHub projects. Over 30,000 build outputs were produced from C and C++ projects. The build outputs are the results of running a particular project's make command. These derivatives can include executables, .o files, .so files, .a files, or other project-specific build artifacts. The output was accepted as long as the make command completed without error; thus, there is no guarantee that every project will contain every type of artifact. These data provide an association between source code and the build artifacts of that code. The data directory contains one directory for each project downloaded from GitHub. These project directories are named with the GraphQL ID from GitHub's GraphQL API. In each of these GraphQL-ID labeled directories, there is a license.txt, a url.txt, source directory, and a derivatives directory. The license.txt contains the license for the original project, the url.txt contains a link to the original project on GitHub, the source directory contains the original code, and the derivatives directory contains the outputs from building the project, which include the previously mentioned files.
DOI: 10.5281/zenodo.3472036
title: Code and Static Analysis Dataset

description: The code and static analysis dataset is a compilation of source code projects and their outputs from running the static analysis tool, Infer, on 3,170 different C and C++ GitHub projects. These data provide an association between source code and a static analysis of that code. The data directory contains one directory for each project downloaded from GitHub. These project directories are named with the GraphQL ID from GitHub's GraphQL API. In each of these GraphQL-ID labeled directories, there is a license.txt, a url.txt, a source directory, and a derivatives directory. The license.txt contains the license for the original project, the url.txt contains a link to the original project on GitHub, the source directory contains the original code, and the derivatives directory contains the output of Infer.
DOI: 10.5281/zenodo.3472037
title: Code and Static Analysis Dataset

description: The code and static analysis dataset is a compilation of source code projects and their outputs from running the static analysis tool, Infer, on 3,170 different C and C++ GitHub projects. These data provide an association between source code and a static analysis of that code. The data directory contains one directory for each project downloaded from GitHub. These project directories are named with the GraphQL ID from GitHub's GraphQL API. In each of these GraphQL-ID labeled directories, there is a license.txt, a url.txt, a source directory, and a derivatives directory. The license.txt contains the license for the original project, the url.txt contains a link to the original project on GitHub, the source directory contains the original code, and the derivatives directory contains the output of Infer.
DOI: 10.5281/zenodo.3471923
title: Elimination of Empirical, Ineffective and Expensive PG Plus Tests to Characterize Modified Binders

description: Corresponding data set for Tran-SET Project No. 18BASU02. Abstract of the final report is stated below for reference: "For characterizing the polymer modified binders, different state Departments of Transportation (DOTs) use different time consuming and empirical Performance Grade (PG) Plus test methods. Furthermore, the PG Plus tests are silent when asphalt binders are modified with chemicals such as polyphosphoric acid (PPA). But, the effects of the elastomeric or plastomeric polymer are not accurately identified through these conventional tests such as Elastic Recovery (ER) and tenacity. Thus, the main research goal of this study is to recommend alternative test method(s), which can possibly be pursued by using a commonly available device, a Dynamic Shear Rheometer (DSR). Three PG binders (PG 64-22, PG 70-22 and PG 76-22) certified in Arkansas have been selected for this investigation. These asphalt binders from different sources were evaluated in the laboratory to establish a good correlation. These binders have been prepared with different modifiers: styrene-butadiene-styrene (SBS) polymer, PPA, or a combination of both. Multiple Stress Creep and Recovery (MSCR), ER-DSR, Linear Amplitude Sweep (LAS), and Binder Yield Energy Test (BYET) were explored to find their effectiveness. After exploring the DSR-based tests, it can be concluded that the ER-DSR and MSCR tests can be good replacements of elastic recovery test (AASHTO T 301). However, the presence of PPA cannot be identified by following these test methods. Thus, chemical tests such as SARA (Saturate, Aromatic, Resin and Asphaltene) analysis, pH and FTIR (Fourier-transform Infrared spectroscopy) were explored to identify the presence of PPA. The pH measurement test was found to be useful to trace the presence of acid and degree of modification. The SARA analysis was used for observing the changes in chemical fraction due to aging and subsequently useful to predict rutting. On the other hand, spectroscopical analysis (FTIR) was found to be an effective tool to evaluate the change in chemical fingerprint due to aging."
description: Tran-SET Project No. 18BASU02
DOI: 10.5281/zenodo.3471924
title: Elimination of Empirical, Ineffective and Expensive PG Plus Tests to Characterize Modified Binders

description: Corresponding data set for Tran-SET Project No. 18BASU02. Abstract of the final report is stated below for reference: "For characterizing the polymer modified binders, different state Departments of Transportation (DOTs) use different time consuming and empirical Performance Grade (PG) Plus test methods. Furthermore, the PG Plus tests are silent when asphalt binders are modified with chemicals such as polyphosphoric acid (PPA). But, the effects of the elastomeric or plastomeric polymer are not accurately identified through these conventional tests such as Elastic Recovery (ER) and tenacity. Thus, the main research goal of this study is to recommend alternative test method(s), which can possibly be pursued by using a commonly available device, a Dynamic Shear Rheometer (DSR). Three PG binders (PG 64-22, PG 70-22 and PG 76-22) certified in Arkansas have been selected for this investigation. These asphalt binders from different sources were evaluated in the laboratory to establish a good correlation. These binders have been prepared with different modifiers: styrene-butadiene-styrene (SBS) polymer, PPA, or a combination of both. Multiple Stress Creep and Recovery (MSCR), ER-DSR, Linear Amplitude Sweep (LAS), and Binder Yield Energy Test (BYET) were explored to find their effectiveness. After exploring the DSR-based tests, it can be concluded that the ER-DSR and MSCR tests can be good replacements of elastic recovery test (AASHTO T 301). However, the presence of PPA cannot be identified by following these test methods. Thus, chemical tests such as SARA (Saturate, Aromatic, Resin and Asphaltene) analysis, pH and FTIR (Fourier-transform Infrared spectroscopy) were explored to identify the presence of PPA. The pH measurement test was found to be useful to trace the presence of acid and degree of modification. The SARA analysis was used for observing the changes in chemical fraction due to aging and subsequently useful to predict rutting. On the other hand, spectroscopical analysis (FTIR) was found to be an effective tool to evaluate the change in chemical fingerprint due to aging."
description: Tran-SET Project No. 18BASU02
DOI: 10.6073/pasta/5c625c1b07dcfbb5accce551d0134e23
title: Jornada Basin LTER: Wireless meteorological station at NPP T-EAST site: 30-minute summary data: 2013 - ongoing

description: 30-minute summary data at NPP P-TOBO met station. Average air temperature, relative humidity, wind speed, wind direction, and solar radiation are measured and calculated based on 1-second scan rate of all sensors located at an automated meteorological station installed at Jornada LTER NPP P-TOBO site. Wind speed is measured at 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature and relative humidity at approximate 2.5m. Solar radiation is measured at 3m. This climate station is operated by the Jornada LTER Program. This is an ONGOING dataset.
DOI: 10.6073/pasta/b462833c285e4d309251f04691bb2430
title: Jornada Basin LTER: Wireless meteorological station at NPP P-TOBO site: Daily summary data: 2013 - ongoing

description: Daily summary data at NPP P-TOBO met station. Average/maximum/minimum air temperature; average/maximum relative humidity and wind speed and average wind direction; solar radiation; albedo. These are measured and calculated based on 1-second scan rate of all sensors located at an automated meteorological station installed at Jornada LTER NPP P-TOBO site. Wind speed is measured at 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature and relative humidity at approximate 2.5m. Solar radiation is measured at 3m. This climate station is operated by the Jornada LTER Program. This is an ONGOING dataset.
DOI: 10.6073/pasta/d4902dfb9aa2d20066371355d75d31b7
title: Jornada Basin LTER: Wireless meteorological station at NPP P-TOBO site: 1-second summary precipitation data: 2013 - ongoing

description: 1-second summary precipitation data at NPP P-TOBO met station. Total precipitation per second is recorded only if there is measurable precipitation. This is recorded at an automated meteorological station installed at Jornada LTER NPP P-TOBO site.. This climate station is operated by the Jornada LTER Program. IMPORTANT: Rainfall is only recorded when a bucket tip occurs on the tipping bucket rain gauge. If there is missing data due to equipment or other problems, there will be no indication of this within this dataset. The user needs to review precipitation values in the 30 minute or hourly summary datasets to determine if there are missing data in this 1 second detailed precipitation dataset. Reference the package i.d.'s knb-lter-jrn.210437027, knb-lter-jrn.210437042, and knb-lter-jrn.210437057 for the NPP P-TOBO met station 30 minute, hourly, and daily summary datasets, respectively. This is an ONGOING dataset.
DOI: 10.5281/zenodo.3233556
title: Data for the study: Comparison of beamformer implementations for MEG source localization

description: This data set is a part of the study 'Comparison of beamformers implementations for MEG source localization'. The dataset includes 64 phantom datasets, 2 human datasets, and 50 simulated datasets. The data also include segmented MRI files from FreeSurfer for MEG phantom (Megin Oy, Helsinki, Finland) and a human subject MRI. It also includes the used versions of the four beamforming packages (MNE-Python, FieldTrip, SPM12(DAiSS), and Brainstorm) and codes used for the analysis.
DOI: 10.6073/pasta/ad3f5f822900f90ada479283504a5ece
title: Jornada Basin LTER: Wireless meteorological station at NPP P-TOBO site: 5-minute summary wind and air temperature data: 2013 - ongoing

description: 5-minute summary data at NPP P-TOBO met station. Average air temperature, wind speed and wind direction are measured and calculated based on 1-second scan rate of all sensors located at an automated meteorological station installed at Jornada LTER NPP P-TOBO site. Wind speed is measured at 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature at approximate 2.5m. This climate station is operated by the Jornada LTER Program. This is an ONGOING dataset.
DOI: 10.6073/pasta/c94a6596f07c4ca29f9ac0be877e2751
title: Jornada Basin LTER: Wireless meteorological station at NPP P-TOBO site: 30-minute soil volumetric water content data: 2013 - ongoing

description: 30-minute Soil Moisture data at NPP P-TOBO met station. Collection of soil volumetric water content data at Jornada LTER NPP sites, New Mexico, supports the environmental monitoring objectives of the Jornada LTER monitoring program that look at plant-soil water dynamics. Volumetric water content, bulk electrical conductivity, soil temperature, and bulk dielectric permittivity are measured every 30 minutes at an automated meteorological station installed at Jornada LTER NPP P-TOBO site. Measurements are made every 30 minutes at 10 cm, 20 cm, and 30 cm soil depths. This is an ONGOING dataset.
DOI: 10.6073/pasta/437a80660416bf8763a4f741f24acefb
title: Jornada Basin LTER: Wireless meteorological station at NPP P-TOBO site: Daily average soil volumetric water content data: 2013 - ongoing

description: Daily average of 30-minute Volumetric Water Content at NPP P-TOBO met station. Collection of soil volumetric water content data at Jornada LTER NPP sites, New Mexico, supports the environmental monitoring objectives of the Jornada LTER monitoring program that look at plant-soil water dynamics. Volumetric water content, bulk electrical conductivity, soil temperature, and bulk dielectric permittivity are measured and calculated at an automated meteorological station installed at Jornada LTER NPP P-TOBO site. The daily average at 3 soil depths is calculated from measurements made every 30 minutes at 10 cm, 20 cm, and 30 cm. This is an ONGOING dataset.
DOI: 10.5281/zenodo.3465952
title: Dataset for "Possible Wintertime Sources of Fine Particles in an Urban Environment"

description: Dataset associated with "Possible Wintertime Sources of Fine Particles in an Urban Environment", Meskhidze et al., (2019), Journal of Geophysical Research. The files are tarball archives compressed using XZ utils. To limit single file size, the data are archived at the directory level. The total extracted data volume is 126.8 GB. README.md (markdown format) provides further information about the data format and files distributed inside the archive. SP2.README.md explains the SP2 data. A README.md further explaining the SMPS data is included in the compressed archived directory.
DOI: 10.5281/zenodo.3465953
title: Dataset for "Possible Wintertime Sources of Fine Particles in an Urban Environment"

description: Dataset associated with "Possible Wintertime Sources of Fine Particles in an Urban Environment", Meskhidze et al., (2019), Journal of Geophysical Research. The files are tarball archives compressed using XZ utils. To limit single file size, the data are archived at the directory level. The total extracted data volume is 126.8 GB. README.md (markdown format) provides further information about the data format and files distributed inside the archive. SP2.README.md explains the SP2 data. A README.md further explaining the SMPS data is included in the compressed archived directory.
DOI: 10.5281/zenodo.3471989
title: Systematic review blockchain, public and accounting

description: The keywords used for the analysis are "blockchain", "public" and "accounting". The selection of the articles was conducted with an analysis of the metadata on the TUTTO’s database of the University of Turin. The research gave 1954 results focusing attention in the period between 2016 and 2019. Selecting only publications in peer review the results have been reduced to 167. The research presented as a collection of the various databases the following results divided by type of publishing house or database: Scopus (Elsevier)(115), ScienceDirect Journals (Elsevier)(81), Social Sciences Citation Index (Web of Science)(51), Science Citation Index Expanded (Web of Science)(39), Taylor & Francis Online - Journals(16), MEDLINE/PubMed (NLM)(14), Sociological Abstracts(13), SpringerLink Open Access(10), Oxford Journals (Oxford University Press)(10), Sage Journals (Sage Publications)(9), SpringerLink(8), Directory of Open Access Journals (DOAJ)(8), PMC (PubMed Central)(8), Walter De Gruyter Journals/Yearbooks(6), Informa - Taylor & Francis (CrossRef)(6), ACM Digital Library(5), arXiv(4), Arts & Humanities Citation Index (Web of Science)(4), Academic Law Reviews (LexisNexis)(3), HAL (CCSd)(3). Only the sources in English are considered only by reducing the selection to 160 articles. By eliminating the books and duplications the results have been reduced to 156.