cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.11570/16.0004
title: The JCMT Gould Belt Survey: Evidence for dust grain evolution in Perseus star-forming clumps

description: This contains data and other information related to the publication 'The JCMT Gould Belt Survey: Evidence for dust grain evolution in Perseus star-forming clumps' by M. Chen et al.
DOI: 10.11570/19.0004
title: Observations of fast radio bursts at frequencies down to 400 megahertz
DOI: 10.5281/zenodo.3837397
title: On the Threat of npm Vulnerable Dependencies in Node.js Applications

description: <strong>PAPER ABSTRACT</strong> Software vulnerabilities have a large negative impact on the software systems that we depend on daily. Reports on software vulnerabilities always paint a grim picture, with some reports showing that 83% of organizations depend on vulnerable software. However, our experience leads us to believe that, in the grand scheme of things, these software vulnerabilities may have less impact than what is reported. Therefore, we perform a study to better understand the threat of npm vulnerable packages used in Node.js applications. We define three threat levels for vulnerabilities in packages, based on their lifetime, where a package vulnerability is assigned a low threat level if it was hidden or still unknown at the time it was used in the dependent application (t), medium threat level if the vulnerability was reported but not yet published at t, and high if it was publicly announced at t. Then, we perform an empirical study involving 6,673 real-world, active, and mature open source Node.js applications. Our findings show that although 67.93% of the examined applications depend on at least one vulnerable package, 94.91% of the vulnerable packages in those affected applications are classified as having low threat. Moreover, we find that in the case of vulnerable packages classified as having high threat, it is the application's lack of updating that makes them vulnerable, i.e., it is not the existence of the vulnerability that is the real problem. Furthermore, we verify our findings at different stages of the application's lifetime and find that our findings still hold. Our study argues that when it comes to software vulnerabilities, things may not be as bad as they seem and that considering vulnerability threat is key. <strong>THE PACKAGE CONTAINS THE FOLLOWING:</strong> The data of the studied Node.js applications. The data of the npm vulnerable packages (advisories). The data of version releases for the npm vulnerable packages. The scripts used for analyzing the data.
DOI: 10.5281/zenodo.3837396
title: On the Threat of npm Vulnerable Dependencies in Node.js Applications

description: <strong>PAPER ABSTRACT</strong> Software vulnerabilities have a large negative impact on the software systems that we depend on daily. Reports on software vulnerabilities always paint a grim picture, with some reports showing that 83% of organizations depend on vulnerable software. However, our experience leads us to believe that, in the grand scheme of things, these software vulnerabilities may have less impact than what is reported. Therefore, we perform a study to better understand the threat of npm vulnerable packages used in Node.js applications. We define three threat levels for vulnerabilities in packages, based on their lifetime, where a package vulnerability is assigned a low threat level if it was hidden or still unknown at the time it was used in the dependent application (t), medium threat level if the vulnerability was reported but not yet published at t, and high if it was publicly announced at t. Then, we perform an empirical study involving 6,673 real-world, active, and mature open source Node.js applications. Our findings show that although 67.93% of the examined applications depend on at least one vulnerable package, 94.91% of the vulnerable packages in those affected applications are classified as having low threat. Moreover, we find that in the case of vulnerable packages classified as having high threat, it is the application's lack of updating that makes them vulnerable, i.e., it is not the existence of the vulnerability that is the real problem. Furthermore, we verify our findings at different stages of the application's lifetime and find that our findings still hold. Our study argues that when it comes to software vulnerabilities, things may not be as bad as they seem and that considering vulnerability threat is key. <strong>THE PACKAGE CONTAINS THE FOLLOWING:</strong> The data of the studied Node.js applications. The data of the npm vulnerable packages (advisories). The data of version releases for the npm vulnerable packages. The scripts used for analyzing the data.
DOI: 10.6084/m9.figshare.12925388
title: Rådata examensarbete -2020.xlsx

description: Med students Julias contribution to rawdata to the study
DOI: 10.6084/m9.figshare.12925391
title: Excel files containing rawdata (cellcounts and calculations) in vitro trials Lactobacillus plantarum 299 and 299v against seven pathogens.

description: Rawdata containing coculture, pH trials, agar overlay counts and calculations in Excel. Part of data used inthe article named "Efficacy of <i>Lactobacillus plantarum 299 </i>and <i>299v </i>against nosocomial oropharyngeal pathogens <i>in vitro </i>and as an oral prophylactic treatment in a randomized, controlled clinical trial"
DOI: 10.6084/m9.figshare.12925391.v1
title: Excel files containing rawdata (cellcounts and calculations) in vitro trials Lactobacillus plantarum 299 and 299v against seven pathogens.

description: Rawdata containing coculture, pH trials, agar overlay counts and calculations in Excel. Part of data used inthe article named "Efficacy of <i>Lactobacillus plantarum 299 </i>and <i>299v </i>against nosocomial oropharyngeal pathogens <i>in vitro </i>and as an oral prophylactic treatment in a randomized, controlled clinical trial"
DOI: 10.6084/m9.figshare.12925388.v1
title: Rådata examensarbete -2020.xlsx

description: Med students Julias contribution to rawdata to the study
DOI: 10.5284/1083067
title: Northamptonshire Archaeology Journal

description: Northamptonshire Archaeological Society was formed in 1974, as successor to the Northamptonshire Federation of Archaeological Societies, which had operated between 1966 and 1973. A significant part of the remit for the new society was to enable the full publication of the results of archaeological fieldwork in the county through the production of a regular journal, Northamptonshire Archaeology, as well as occasional monographs. Between 1966 and 1973 the Federation had produced an annual bulletin that provided brief roundups of fieldwork across the county, with the volumes numbered 1-8. With the appearance of the county society a full journal was produced containing detailed archaeological reports in addition to the annual roundups. This series continued the established number sequence, beginning with volume 9 in 1974. Publication of the series continues, and had reached volume 40 in 2019.
DOI: 10.5517/ccdc.csd.cc1zllcj
title: CCDC 1835118: Experimental Crystal Structure Determination

description: Related Article: Orhi Esarte Palomero, Richard A. Jones|2020|Organometallics|39|3689|doi:10.1021/acs.organomet.0c00501
DOI: 10.5517/ccdc.csd.cc1zlldk
title: CCDC 1835119: Experimental Crystal Structure Determination

description: Related Article: Orhi Esarte Palomero, Richard A. Jones|2020|Organometallics|39|3689|doi:10.1021/acs.organomet.0c00501
DOI: 10.5517/ccdc.csd.cc1zllfl
title: CCDC 1835120: Experimental Crystal Structure Determination

description: Related Article: Orhi Esarte Palomero, Richard A. Jones|2020|Organometallics|39|3689|doi:10.1021/acs.organomet.0c00501
DOI: 10.5517/ccdc.csd.cc1zllgm
title: CCDC 1835121: Experimental Crystal Structure Determination

description: Related Article: Orhi Esarte Palomero, Richard A. Jones|2020|Organometallics|39|3689|doi:10.1021/acs.organomet.0c00501
DOI: 10.5517/ccdc.csd.cc1zllhn
title: CCDC 1835122: Experimental Crystal Structure Determination

description: Related Article: Orhi Esarte Palomero, Richard A. Jones|2020|Organometallics|39|3689|doi:10.1021/acs.organomet.0c00501
DOI: 10.5517/ccdc.csd.cc1zlljp
title: CCDC 1835123: Experimental Crystal Structure Determination

description: Related Article: Orhi Esarte Palomero, Richard A. Jones|2020|Organometallics|39|3689|doi:10.1021/acs.organomet.0c00501
DOI: 10.5517/ccdc.csd.cc1zllkq
title: CCDC 1835124: Experimental Crystal Structure Determination

description: Related Article: Orhi Esarte Palomero, Richard A. Jones|2020|Organometallics|39|3689|doi:10.1021/acs.organomet.0c00501
DOI: 10.5517/ccdc.csd.cc1zlllr
title: CCDC 1835125: Experimental Crystal Structure Determination

description: Related Article: Orhi Esarte Palomero, Richard A. Jones|2020|Organometallics|39|3689|doi:10.1021/acs.organomet.0c00501
DOI: 10.5517/ccdc.csd.cc1zllms
title: CCDC 1835126: Experimental Crystal Structure Determination

description: Related Article: Orhi Esarte Palomero, Richard A. Jones|2020|Organometallics|39|3689|doi:10.1021/acs.organomet.0c00501
DOI: 10.5517/ccdc.csd.cc1zllnt
title: CCDC 1835127: Experimental Crystal Structure Determination

description: Related Article: Orhi Esarte Palomero, Richard A. Jones|2020|Organometallics|39|3689|doi:10.1021/acs.organomet.0c00501
DOI: 10.5517/ccdc.csd.cc1zllpv
title: CCDC 1835128: Experimental Crystal Structure Determination

description: Related Article: Orhi Esarte Palomero, Richard A. Jones|2020|Organometallics|39|3689|doi:10.1021/acs.organomet.0c00501
DOI: 10.5517/ccdc.csd.cc1zllqw
title: CCDC 1835129: Experimental Crystal Structure Determination

description: Related Article: Orhi Esarte Palomero, Richard A. Jones|2020|Organometallics|39|3689|doi:10.1021/acs.organomet.0c00501
DOI: 10.5517/ccdc.csd.cc21zcm1
title: CCDC 1906023: Experimental Crystal Structure Determination

description: Related Article: Nicholas S. Labrum,  Alyssa C. Cabelof,  Kenneth G. Caulton|2020|Chem.-Eur.J.|26|13915|doi:10.1002/chem.202001749
DOI: 10.5517/ccdc.csd.cc21zcn2
title: CCDC 1906024: Experimental Crystal Structure Determination

description: Related Article: Nicholas S. Labrum,  Alyssa C. Cabelof,  Kenneth G. Caulton|2020|Chem.-Eur.J.|26|13915|doi:10.1002/chem.202001749
DOI: 10.5517/ccdc.csd.cc21zcp3
title: CCDC 1906025: Experimental Crystal Structure Determination

description: Related Article: Nicholas S. Labrum,  Alyssa C. Cabelof,  Kenneth G. Caulton|2020|Chem.-Eur.J.|26|13915|doi:10.1002/chem.202001749
DOI: 10.5517/ccdc.csd.cc21zcq4
title: CCDC 1906026: Experimental Crystal Structure Determination

description: Related Article: Nicholas S. Labrum,  Alyssa C. Cabelof,  Kenneth G. Caulton|2020|Chem.-Eur.J.|26|13915|doi:10.1002/chem.202001749
DOI: 10.5517/ccdc.csd.cc21zcr5
title: CCDC 1906027: Experimental Crystal Structure Determination

description: Related Article: Nicholas S. Labrum,  Alyssa C. Cabelof,  Kenneth G. Caulton|2020|Chem.-Eur.J.|26|13915|doi:10.1002/chem.202001749
DOI: 10.5517/ccdc.csd.cc21zcs6
title: CCDC 1906028: Experimental Crystal Structure Determination

description: Related Article: Nicholas S. Labrum,  Alyssa C. Cabelof,  Kenneth G. Caulton|2020|Chem.-Eur.J.|26|13915|doi:10.1002/chem.202001749
DOI: 10.5517/ccdc.csd.cc21zct7
title: CCDC 1906029: Experimental Crystal Structure Determination

description: Related Article: Nicholas S. Labrum,  Alyssa C. Cabelof,  Kenneth G. Caulton|2020|Chem.-Eur.J.|26|13915|doi:10.1002/chem.202001749
DOI: 10.5517/ccdc.csd.cc21zcv8
title: CCDC 1906030: Experimental Crystal Structure Determination

description: Related Article: Nicholas S. Labrum,  Alyssa C. Cabelof,  Kenneth G. Caulton|2020|Chem.-Eur.J.|26|13915|doi:10.1002/chem.202001749
DOI: 10.5517/ccdc.csd.cc21zcw9
title: CCDC 1906031: Experimental Crystal Structure Determination

description: Related Article: Nicholas S. Labrum,  Alyssa C. Cabelof,  Kenneth G. Caulton|2020|Chem.-Eur.J.|26|13915|doi:10.1002/chem.202001749
DOI: 10.5517/ccdc.csd.cc239ys5
title: CCDC 1945987: Experimental Crystal Structure Determination

description: Related Article: Sheela Thapa, Lingyao Meng, Eshani Hettiarachchi, Yousef K. Bader, Diane A. Dickie, Gayan Rubasinghege, Sergei A. Ivanov, Erika C. Vreeland, Yang Qin|2020|Chem.-Eur.J.|26|13788|doi:10.1002/chem.202002823
DOI: 10.5517/ccdc.csd.cc23crgq
title: CCDC 1947713: Experimental Crystal Structure Determination

description: Related Article: Orhi Esarte Palomero, Richard A. Jones|2020|Organometallics|39|3689|doi:10.1021/acs.organomet.0c00501
DOI: 10.5517/ccdc.csd.cc23crhr
title: CCDC 1947714: Experimental Crystal Structure Determination

description: Related Article: Orhi Esarte Palomero, Richard A. Jones|2020|Organometallics|39|3689|doi:10.1021/acs.organomet.0c00501
DOI: 10.5517/ccdc.csd.cc23crjs
title: CCDC 1947715: Experimental Crystal Structure Determination

description: Related Article: Orhi Esarte Palomero, Richard A. Jones|2020|Organometallics|39|3689|doi:10.1021/acs.organomet.0c00501
DOI: 10.5517/ccdc.csd.cc23crkt
title: CCDC 1947716: Experimental Crystal Structure Determination

description: Related Article: Orhi Esarte Palomero, Richard A. Jones|2020|Organometallics|39|3689|doi:10.1021/acs.organomet.0c00501
DOI: 10.5517/ccdc.csd.cc24wl4s
title: CCDC 1992715: Experimental Crystal Structure Determination

description: Related Article: Andrey N. Usoltsev, Sergey A. Adonin, Boris A. Kolesov, Alexander S. Novikov, Vladimir P. Fedin, Maxim N. Sokolov|2020|Chem.-Eur.J.|26|13776|doi:10.1002/chem.202002014
DOI: 10.5517/ccdc.csd.cc24ys96
title: CCDC 1994828: Experimental Crystal Structure Determination

description: Related Article: Dmitry I. Konovalov, Anton A. Ivanov, Tatiana S. Frolova, Ilia V. Eltsov, Yakov M. Gayfulin, Louis Plunkett, Masoomeh Bazzar, Ali M. Adawi, Jean-Sebastien G. Bouillard, Sergey I. Baiborodin, Olga I. Sinitsyna, Natalia V. Kuratieva, Vadim V. Yanshole, Olga A. Efremova, Michael A. Shestopalov|2020|Chem.-Eur.J.|26|13904|doi:10.1002/chem.202001680
DOI: 10.5517/ccdc.csd.cc24ysb7
title: CCDC 1994829: Experimental Crystal Structure Determination

description: Related Article: Dmitry I. Konovalov, Anton A. Ivanov, Tatiana S. Frolova, Ilia V. Eltsov, Yakov M. Gayfulin, Louis Plunkett, Masoomeh Bazzar, Ali M. Adawi, Jean-Sebastien G. Bouillard, Sergey I. Baiborodin, Olga I. Sinitsyna, Natalia V. Kuratieva, Vadim V. Yanshole, Olga A. Efremova, Michael A. Shestopalov|2020|Chem.-Eur.J.|26|13904|doi:10.1002/chem.202001680
DOI: 10.5517/ccdc.csd.cc24ysc8
title: CCDC 1994830: Experimental Crystal Structure Determination

description: Related Article: Dmitry I. Konovalov, Anton A. Ivanov, Tatiana S. Frolova, Ilia V. Eltsov, Yakov M. Gayfulin, Louis Plunkett, Masoomeh Bazzar, Ali M. Adawi, Jean-Sebastien G. Bouillard, Sergey I. Baiborodin, Olga I. Sinitsyna, Natalia V. Kuratieva, Vadim V. Yanshole, Olga A. Efremova, Michael A. Shestopalov|2020|Chem.-Eur.J.|26|13904|doi:10.1002/chem.202001680
DOI: 10.5517/ccdc.csd.cc24nvg4
title: CCDC 1986246: Experimental Crystal Structure Determination

description: Related Article: Marko Rodewald, J. Mikko Rautiainen, Tobias Niksch, Helmar Görls, Raija Oilunkaniemi, Wolfgang Weigand, Risto S. Laitinen|2020|Chem.-Eur.J.|26|13806|doi:10.1002/chem.202002510
DOI: 10.5517/ccdc.csd.cc24nvh5
title: CCDC 1986247: Experimental Crystal Structure Determination

description: Related Article: Marko Rodewald, J. Mikko Rautiainen, Tobias Niksch, Helmar Görls, Raija Oilunkaniemi, Wolfgang Weigand, Risto S. Laitinen|2020|Chem.-Eur.J.|26|13806|doi:10.1002/chem.202002510
DOI: 10.5517/ccdc.csd.cc24nvj6
title: CCDC 1986248: Experimental Crystal Structure Determination

description: Related Article: Marko Rodewald, J. Mikko Rautiainen, Tobias Niksch, Helmar Görls, Raija Oilunkaniemi, Wolfgang Weigand, Risto S. Laitinen|2020|Chem.-Eur.J.|26|13806|doi:10.1002/chem.202002510
DOI: 10.5281/zenodo.4071465
title: Occurrence data on nickel in food

description: Nickel_OCC_ZENODO.CSV Contains the raw (no data cleaning applied to it) occurrence dataset on nickel as extracted from EFSA DWH on 7 February 2020 in food samples presented in the opinion as described in its section 3.2.1. The data is provided in csv format. This dataset is compliant with EFSA SSD model and contains two additional columns documenting issues identified in the cleaning process (column: issue) and the action taken (column: action) to address the issue (e.g. delete record or update values in specific fields). The link to the catalogues of controlled terminologies can be found under "Related identifiers”.
description: EU; CSV; contam@efsa.europa.eu
DOI: 10.5281/zenodo.4071464
title: Occurrence data on nickel in food

description: Nickel_OCC_ZENODO.CSV Contains the raw (no data cleaning applied to it) occurrence dataset on nickel as extracted from EFSA DWH on 7 February 2020 in food samples presented in the opinion as described in its section 3.2.1. The data is provided in csv format. This dataset is compliant with EFSA SSD model and contains two additional columns documenting issues identified in the cleaning process (column: issue) and the action taken (column: action) to address the issue (e.g. delete record or update values in specific fields). The link to the catalogues of controlled terminologies can be found under "Related identifiers”.
description: EU; CSV; contam@efsa.europa.eu
DOI: 10.5281/zenodo.4247595
title: DeepDataFlow

description: This dataset contains 493k LLVM-IRs taken from a wide range of projects and source programming languages, and includes labels for several compiler data analyses. We also include the logs for the machine learning jobs which produced our published experimental results. The uncompressed dataset uses the following layout: <code>labels/</code> Directory containing machine learning features and labels for programs for compiler data flow analyses. <code>labels/&lt;analysis&gt;/&lt;source&gt;.&lt;id&gt;.&lt;lang&gt;.ProgramFeaturesList.pb</code> A ProgramFeaturesList protocol buffer containing a list of features resulting from running a data flow analysis on a program. <code>graphs/</code> Directory containing ProGraML representations of LLVM IRs. <code>graphs/&lt;source&gt;.&lt;id&gt;.&lt;lang&gt;.ProgramGraph.pb</code> A ProgramGraph protocol buffer of an LLVM IR in the ProGraML representation. <code>ll/</code> Directory containing LLVM-IR files. <code>ir/&lt;source&gt;.&lt;id&gt;.&lt;lang&gt;.ll</code> An LLVM IR in text format, as produced by <code>clang -emit-llvm -S</code> or equivalent. <code>test/</code> A directory containing symlinks to graphs in the <code>graphs/</code> directory, indicating which graphs should be used as part of the test set. <code>train/</code> A directory containing symlinks to graphs in the <code>graphs/</code> directory, indicating which graphs should be used as part of the training set. <code>val/</code> A directory containing symlinks to graphs in the <code>graphs/</code> directory, indicating which graphs should be used as part of the validation set. <code>vocab/</code> Directory containing vocabulary files. <code>vocab/&lt;type&gt;.csv</code> A vocabulary file, which lists unique node texts, their frequency in the dataset, and the cumulative proportion of total unique node texts that is covered. For further information please see our ProGraML repository.
description: {"references": ["Cummins, C., Fisches, Z. V., Ben-Nun, T., Hoefler, T., & Leather, H. (2020). ProGraML: Graph-based Deep Learning for Program Optimization and Analysis. arXiv preprint arXiv:2003.10536."]}
DOI: 10.5281/zenodo.4122436
title: DeepDataFlow

description: This dataset contains 493k LLVM-IRs taken from a wide range of projects and source programming languages, and includes labels for several compiler data analyses. We also include the logs for the machine learning jobs which produced our published experimental results. The uncompressed dataset uses the following layout: <code>labels/</code> Directory containing machine learning features and labels for programs for compiler data flow analyses. <code>labels/&lt;analysis&gt;/&lt;source&gt;.&lt;id&gt;.&lt;lang&gt;.ProgramFeaturesList.pb</code> A ProgramFeaturesList protocol buffer containing a list of features resulting from running a data flow analysis on a program. <code>graphs/</code> Directory containing ProGraML representations of LLVM IRs. <code>graphs/&lt;source&gt;.&lt;id&gt;.&lt;lang&gt;.ProgramGraph.pb</code> A ProgramGraph protocol buffer of an LLVM IR in the ProGraML representation. <code>ll/</code> Directory containing LLVM-IR files. <code>ir/&lt;source&gt;.&lt;id&gt;.&lt;lang&gt;.ll</code> An LLVM IR in text format, as produced by <code>clang -emit-llvm -S</code> or equivalent. <code>test/</code> A directory containing symlinks to graphs in the <code>graphs/</code> directory, indicating which graphs should be used as part of the test set. <code>train/</code> A directory containing symlinks to graphs in the <code>graphs/</code> directory, indicating which graphs should be used as part of the training set. <code>val/</code> A directory containing symlinks to graphs in the <code>graphs/</code> directory, indicating which graphs should be used as part of the validation set. <code>vocab/</code> Directory containing vocabulary files. <code>vocab/&lt;type&gt;.csv</code> A vocabulary file, which lists unique node texts, their frequency in the dataset, and the cumulative proportion of total unique node texts that is covered. For further information please see our ProGraML repository.
description: {"references": ["Cummins, C., Fisches, Z. V., Ben-Nun, T., Hoefler, T., & Leather, H. (2020). ProGraML: Graph-based Deep Learning for Program Optimization and Analysis. arXiv preprint arXiv:2003.10536."]}
DOI: 10.5281/zenodo.4061688
title: Occurrence data of nitrate and nitrite in feed

description: <strong>Annex_III_occurrence data.xlsx</strong> This Annex is an excel file presenting tables on occurrence data on nitrate and nitrite in feed. <strong>Nitrate Nitrite_OCC_ZENODO.CSV</strong> Contains the raw (no data cleaning applied to it) occurrence dataset on nitrate and nitrite as extracted from EFSA DWH on 03 December 2019 in feed samples presented in the opinion as described in its section 3.2.2. The data is provided <em>in csv format</em>. This dataset is compliant with EFSA SSD model and contains two additional columns documenting issues identified in the cleaning process (column: issue) and the action taken (column: action) to address the issue (e.g. delete record or update values in specific fields). The link to the catalogues of controlled terminologies can be found under "Related identifiers”.
description: EU; CSV; contam@efsa.europa.eu
DOI: 10.5281/zenodo.4061687
title: Occurrence data of nitrate and nitrite in feed

description: <strong>Annex_III_occurrence data.xlsx</strong> This Annex is an excel file presenting tables on occurrence data on nitrate and nitrite in feed. <strong>Nitrate Nitrite_OCC_ZENODO.CSV</strong> Contains the raw (no data cleaning applied to it) occurrence dataset on nitrate and nitrite as extracted from EFSA DWH on 03 December 2019 in feed samples presented in the opinion as described in its section 3.2.2. The data is provided <em>in csv format</em>. This dataset is compliant with EFSA SSD model and contains two additional columns documenting issues identified in the cleaning process (column: issue) and the action taken (column: action) to address the issue (e.g. delete record or update values in specific fields). The link to the catalogues of controlled terminologies can be found under "Related identifiers”.
description: EU; CSV; contam@efsa.europa.eu