cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.19425515
title: TRECCOVID-RF-Aspire

description: This is a copy of the TRECCOVID-RF dataset used in the paper "Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity" by Sheshera Mysore, Arman Cohan, Tom Hope. The TRECCOVID dataset presents an ad-hoc search dataset. The versions of the original dataset used may be accessed here: query topics, relevance annotations, and the metadata for papers is obtained from the CORD-19 dataset in the 2021-06-21 release. The dataset released here converts the original TRECCOVID dataset into a reformulated form, TRECCOVID-RF which is used in the paper. <br><br>See further details of the paper, how this dataset was compiled, and how it was used: https://github.com/allenai/aspire<br>The contents of the dataset are as follows: <br><code>abstracts-treccovid.jsonl</code>: <code>jsonl</code> file containing the paper-id, abstracts, and titles for the queries and candidates which are part of the dataset.<br> <code><code>treccovid</code>-queries-release.csv</code>: Metadata associated with every query.<code>test-pid2anns-</code><code><code>treccovid</code>.json</code>: JSON file with the query paper-id, candidate paper-ids for every query paper in the dataset. Use these files in conjunction with <code>abstracts-</code><code><code>treccovid</code>.jsonl</code> to generate files for use in model evaluation. <br><code><code>treccovid</code>-evaluation_splits.json</code>: Paper-ids for the splits to use in reporting evaluation numbers. <code>aspire/src/evaluation/ranking_eval.py</code> included in the github repo accompanying this dataset implements the evaluation protocol and computes evaluation metrics. Please see the paper for descriptions of the experimental protocol we recommend to report evaluation metrics.<br>
DOI: 10.6084/m9.figshare.19395545.v2
title: Zhang2022JGR_data

description: This zip file provides the raw seismic data of the teleseismic and local events for Zhang2022_JGR.
DOI: 10.6084/m9.figshare.19425533
title: SciDocs-Aspire

description: This is a copy of the SciDocs dataset used in the paper "Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity" by Sheshera Mysore, Arman Cohan, Tom Hope. The dataset was originally released here: https://github.com/allenai/scidocs. The original dataset consists of 7 subtasks, 4 of which are used in our paper. The released data here corresponds to these tasks: Citation prediction, Co-citation prediction, Co-Read prediction, Co-View prediction.<br><br>See further details of the paper, how this dataset was compiled, and how it was used: https://github.com/allenai/aspire<br>The contents of the dataset are as follows: <br><code>abstracts-</code><code><code><code>{scidcite/scidcocite/scidcoread/scidcoview}</code></code>.jsonl</code>: <code>jsonl</code> file containing the paper-id, abstracts, and titles for the queries and candidates which are part of the dataset. <br><br> <code><code>{scidcite/scidcocite/scidcoread/scidcoview}</code>-queries-release.csv</code>: Metadata associated with every query.<code><br></code><code>test-pid2anns-</code><code><code>{scidcite/scidcocite/scidcoread/scidcoview}</code></code><code>.json</code>: JSON file with the query paper-id, candidate paper-ids for every query paper in the dataset. Use these files in conjunction with <code>abstracts-</code><code><code><code><code>{scidcite/scidcocite/scidcoread/scidcoview}</code></code></code>.jsonl</code> to generate files for use in model evaluation. <br><code><code><code><code>{scidcite/scidcocite/scidcoread/scidcoview}</code></code></code>-evaluation_splits.json</code>: Paper-ids for the splits to use in reporting evaluation numbers. <code>aspire/src/evaluation/ranking_eval.py</code> included in the github repo accompanying this dataset implements the evaluation protocol and computes evaluation metrics. Please see the paper for descriptions of the experimental protocol we recommend to report evaluation metrics.
DOI: 10.34740/kaggle/dsv/3353472
title: Gullalletimes
DOI: 10.5281/zenodo.6386572
title: Electrical Low-Frequency 1/fγ Noise Due to Surface Diffusion of Scatterers on an Ultra-low-Noise Graphene Platform

description: Experimental dataset for article “Electrical Low-Frequency 1/<em>f<sup>γ</sup></em> Noise Due to Surface Diffusion of Scatterers on an Ultra-low-Noise Graphene Platform”, <em>Nano letters</em>, <strong>21</strong>(18), 7637-7643 (2021), doi: 10.1021/acs.nanolett.1c02325
DOI: 10.5281/zenodo.6386571
title: Electrical Low-Frequency 1/fγ Noise Due to Surface Diffusion of Scatterers on an Ultra-low-Noise Graphene Platform

description: Experimental dataset for article “Electrical Low-Frequency 1/<em>f<sup>γ</sup></em> Noise Due to Surface Diffusion of Scatterers on an Ultra-low-Noise Graphene Platform”, <em>Nano letters</em>, <strong>21</strong>(18), 7637-7643 (2021), doi: 10.1021/acs.nanolett.1c02325
DOI: 10.21227/k73z-ks20
title: Sample

description: Some abstract.
DOI: 10.6084/m9.figshare.19425572
title: Raw data of Work alination.sav

description: Work alienation influences redness for professional development
DOI: 10.34740/kaggle/dsv/3362832
title: Mahanagar Yatayat Dataset
DOI: 10.21227/1zxb-q808
title: Metrics for Recommending Corrective and Preventive Actions (CAPAs) in Software Development Projects: a Systematic Literature Review

description: Several works attempted to establish procedures to individuate bugs, defects or anomalies during the different phases of software development, especially in the implementation phase. The mere detection of anomalies is not sufficient, though, at least until they get  fixed. Corrective actions can be formulated to remove anomalies and enhance the software quality. Preventive actions are equally important in as much as they  avoid the emergence and recurrence of anomalies in the future. To know whether an anomaly exists in any given software, one must measure the software quality attributes related to it using specific software metrics. The main aim of this work was to find out and explain how to meaningfully attribute software metrics to useful corrective and preventive actions.

However, to determine proper actions for the specific contexts, one needs to know more about the anomalies and about their root causes. In this study, we collected three kinds of data (metrics, anomalies, actions), which helped us individuate the dimensions of the problem. We found 384 software metrics, which are used to detect 374 anomalies related to 494 corrective and preventive actions. Our findings demonstrate the need to formulate remedial strategies and build tools to automate the process of determining actions from abnormal metric values.
DOI: 10.21227/jf21-rt22
title: C3I Thermal Automotive Dataset

description: The C3I Thermal Automotive Dataset provides &gt; 35,000 distinct frames along with annotated thermal frames for the development of smart thermal perception system/ object detection system that will enable the automotive industry and researchers to develop safer and more efficient ADAS and self-driving car systems. The overall dataset is acquired, processed, and open-sourced in challenging weather and environmental scenarios. The dataset is recorded from a lost-cost yet effective 640x480 uncooled LWIR thermal camera. The dataset is gathered by mounting the camera stand-alone and on an electric vehicle to minimize mechanical vibrations.
DOI: 10.5281/zenodo.5851241
title: The effect of local universe constraints on halo abundance and clustering

description: These archives contain the data products described in "The effect of local universe constraints on halo adundance and clustering" by M. L. Hutt, H. Desmond, J. Devriendt and A. Slyz (2022). See the README for further details.
DOI: 10.5281/zenodo.5851240
title: The effect of local universe constraints on halo abundance and clustering

description: These archives contain the data products described in "The effect of local universe constraints on halo adundance and clustering" by M. L. Hutt, H. Desmond, J. Devriendt and A. Slyz (2022). See the README for further details.
DOI: 10.21227/xj3d-k060
title: DFT-NEGF study of spin tunneling current under field effect in MTJ data

description: DFT-NEGF study of spin tunneling current under field effect in MTJ for possible application in STT-RAM
DOI: 10.6084/m9.figshare.19425599
title: Anchialine microbial succession

description: When new land is created, initial microbial colonization lays the foundation for further ecological succession of plant and animal communities. Primary microbial succession of new habitats formed during volcanic activity has received little attention, especially aquatic habitats. The anchialine ecosystem, which includes coastal ponds in young lava flows, offers an opportunity to examine this process. Here, we characterized microbial communities of anchialine habitats in Hawaii that were recently created during eruptions of Kilauea in 2018. Samples from three anchialine ponds were collected ~2 years after their formation and at later time points. Sequence profiling of prokaryotic and eukaryotic communities was used to ask whether communities were similar to those from nearby, established anchialine habitats, and if community structure changed over time.<br>Here we make available raw outputs from QIIME2, feature tables summarizing abundance of each taxa in each sample, and raw output of the PERMANOVA tests for each analysis conducted. Please contact jhavird@utexas.edu if you have questions about any of these data.
DOI: 10.6084/m9.figshare.19425599.v1
title: Anchialine microbial succession

description: When new land is created, initial microbial colonization lays the foundation for further ecological succession of plant and animal communities. Primary microbial succession of new habitats formed during volcanic activity has received little attention, especially aquatic habitats. The anchialine ecosystem, which includes coastal ponds in young lava flows, offers an opportunity to examine this process. Here, we characterized microbial communities of anchialine habitats in Hawaii that were recently created during eruptions of Kilauea in 2018. Samples from three anchialine ponds were collected ~2 years after their formation and at later time points. Sequence profiling of prokaryotic and eukaryotic communities was used to ask whether communities were similar to those from nearby, established anchialine habitats, and if community structure changed over time.<br>Here we make available raw outputs from QIIME2, feature tables summarizing abundance of each taxa in each sample, and raw output of the PERMANOVA tests for each analysis conducted. Please contact jhavird@utexas.edu if you have questions about any of these data.
DOI: 10.6084/m9.figshare.6741425.v13
title: COCI N-Triples dataset of all the citation data

description: This dataset contains all the citation data (in N-Triples format) included in COCI, released on 29 January 2022. In particular, any citation in the dataset, defined as an individual of the class <i>cito:Citation</i>, includes the following information:<b>[citation IRI]</b> the Open Citation Identifier (OCI) for the citation, defined in the final part of the URL identifying the citation (https://w3id.org/oc/index/coci/ci/[OCI]);<b>[property "cito:hasCitingEntity"]</b> the citing entity identified by its DOI URL (http://dx.doi.org/[DOI]);<b>[property "cito:hasCitedEntity"]</b> the cited entity identified by its DOI URL (http://dx.doi.org/[DOI]);<b>[property "cito:hasCitationCreationDate"]</b> the creation date of the citation (i.e. the publication date of the citing entity);<b>[property "cito:hasCitationTimeSpan"]</b> the time span of the citation (i.e. the interval between the publication date of the cited entity and the publication date of the citing entity);<b>[type "cito:JournalSelfCitation"]</b> it records whether the citation is a journal self-citations (i.e. the citing and the cited entities are published in the same journal);<b>[type "cito:AuthorSelfCitation"]</b> it records whether the citation is an author self-citation (i.e. the citing and the cited entities have at least one author in common).This version of the dataset contains:<br>1,271,360,867 citations;71,337,645 bibliographic resources.The size of the zipped archive is 62.62 GB, while the size of the unzipped N-Triples file is 1.37 TB.<br>Additional information about COCI can be retrieved in the official webpage.<br>
DOI: 10.6084/m9.figshare.19425524
title: Towards bridging the gap between Knowledge Graphs and Chatbots

description: Supplementary data for the paper "Towards bridging the gap between Knowledge Graphs and Chatbots" at the 22nd International Conference on Web Engineering (ICWE 2022).<br>Chatbots are nowadays being applied widely in different life domains. One major reason for this trend is the mature development process that is supported by large companies and sophisticated conversational platforms. However, the required development steps are mostly done manually while transforming existing knowledge bases into interaction configurations, s.t., algorithms integrated into the conversational platforms are enabled to learn the intended interaction patterns. However, already existing domain knowledge may get vanished while transforming a structured knowledge base into a "flat" text representation without references backwards. In this paper, we aim for an automatic process dedicated to generating interaction configurations for a conversational platform (Google Dialogflow) from an existing domain-specific knowledge base.Our ultimate goal is to generate chatbot configurations automatically, s.t., the quality and efficiency are increased.
DOI: 10.5517/ccdc.csd.cc1nrndg
title: CCDC 1542076: Experimental Crystal Structure Determination

description: Related Article: Yong Wang, Pascal Pigeon, Wei Li, Jiangkun Yan, Patrick M. Dansette, Mohamed Othman, Michael J. McGlinchey, Gérard Jaouen|2022|Eur.J.Med.Chem.|234|114202|doi:10.1016/j.ejmech.2022.114202
DOI: 10.5517/ccdc.csd.cc25z0x3
title: CCDC 2024855: Experimental Crystal Structure Determination

description: Related Article: Abhineet Verma, Nattamai Bhuvanesh, Joseph Reibenspies, Satyen Saha, Sailaja S. Sunkari|2022|Spectrochim.Acta,Part A|274|121121|doi:10.1016/j.saa.2022.121121
DOI: 10.5517/ccdc.csd.cc272ycn
title: CCDC 2058411: Experimental Crystal Structure Determination

description: Related Article: Panting Shou, Jiao Li, Panpan Zhang, Yingying Wei, Mengxia Yan, Mengting Zhang, Kaiyuan Feng, Nengming Lin, Huajun Zhao, Bo Yang|2022|Fitoterapia|158|105169|doi:10.1016/j.fitote.2022.105169
DOI: 10.5517/ccdc.csd.cc283c6z
title: CCDC 2088600: Experimental Crystal Structure Determination

description: Related Article: Bo Lin, Yunfei Yao, Yangjie Huang, Zhiqiang Weng|2022|Org.Lett.|24|2055|doi:10.1021/acs.orglett.2c00637
DOI: 10.5517/ccdc.csd.cc2886yq
title: CCDC 2093273: Experimental Crystal Structure Determination

description: Related Article: Denis Dardić, Nils Böhringer, Alberto Plaza, Florian Zubeil, Juliane Pohl, Svenja Sommer, Leo Padva, Jonathan Becker, Maria A. Patras, Mona-Katharina Bill, Michael Kurz, Luigi Toti, Sven W. Görgens, Sören M. M. Schuler, André Billion, Oliver Schwengers, Paulus Wohlfart, Alexander Goesmann, Norbert Tennagels, Andreas Vilcinskas, Peter E. Hammann, Till F. Schäberle, Armin Bauer|2022|Org.Chem.Front.|9|1604|doi:10.1039/D1QO01652K
DOI: 10.5517/ccdc.csd.cc2886zr
title: CCDC 2093274: Experimental Crystal Structure Determination

description: Related Article: Denis Dardić, Nils Böhringer, Alberto Plaza, Florian Zubeil, Juliane Pohl, Svenja Sommer, Leo Padva, Jonathan Becker, Maria A. Patras, Mona-Katharina Bill, Michael Kurz, Luigi Toti, Sven W. Görgens, Sören M. M. Schuler, André Billion, Oliver Schwengers, Paulus Wohlfart, Alexander Goesmann, Norbert Tennagels, Andreas Vilcinskas, Peter E. Hammann, Till F. Schäberle, Armin Bauer|2022|Org.Chem.Front.|9|1604|doi:10.1039/D1QO01652K
DOI: 10.5517/ccdc.csd.cc28870t
title: CCDC 2093275: Experimental Crystal Structure Determination

description: Related Article: Denis Dardić, Nils Böhringer, Alberto Plaza, Florian Zubeil, Juliane Pohl, Svenja Sommer, Leo Padva, Jonathan Becker, Maria A. Patras, Mona-Katharina Bill, Michael Kurz, Luigi Toti, Sven W. Görgens, Sören M. M. Schuler, André Billion, Oliver Schwengers, Paulus Wohlfart, Alexander Goesmann, Norbert Tennagels, Andreas Vilcinskas, Peter E. Hammann, Till F. Schäberle, Armin Bauer|2022|Org.Chem.Front.|9|1604|doi:10.1039/D1QO01652K
DOI: 10.5517/ccdc.csd.cc28hf29
title: CCDC 2100190: Experimental Crystal Structure Determination

description: Related Article: Alberto Llobat, Jorge Escorihuela, Carmen Ramírez de Arellano, Santos Fustero, Mercedes Medio-Simón|2022|Org.Biomol.Chem.|20|2433|doi:10.1039/D2OB00340F
DOI: 10.5517/ccdc.csd.cc28z89v
title: CCDC 2114457: Experimental Crystal Structure Determination

description: Related Article: Yong Wang, Pascal Pigeon, Wei Li, Jiangkun Yan, Patrick M. Dansette, Mohamed Othman, Michael J. McGlinchey, Gérard Jaouen|2022|Eur.J.Med.Chem.|234|114202|doi:10.1016/j.ejmech.2022.114202
DOI: 10.5517/ccdc.csd.cc28z8fz
title: CCDC 2114461: Experimental Crystal Structure Determination

description: Related Article: Yong Wang, Pascal Pigeon, Wei Li, Jiangkun Yan, Patrick M. Dansette, Mohamed Othman, Michael J. McGlinchey, Gérard Jaouen|2022|Eur.J.Med.Chem.|234|114202|doi:10.1016/j.ejmech.2022.114202
DOI: 10.5517/ccdc.csd.cc297l8f
title: CCDC 2122454: Experimental Crystal Structure Determination

description: Related Article: Ling-Zhi Sun, Xuan Yang, Nan-Nan Li, Meng Li, Qin Ouyang, Jian-Bo Xie|2022|Org.Lett.|24|1883|doi:10.1021/acs.orglett.2c00056
DOI: 10.5517/ccdc.csd.cc297l9g
title: CCDC 2122455: Experimental Crystal Structure Determination

description: Related Article: Ling-Zhi Sun, Xuan Yang, Nan-Nan Li, Meng Li, Qin Ouyang, Jian-Bo Xie|2022|Org.Lett.|24|1883|doi:10.1021/acs.orglett.2c00056
DOI: 10.5517/ccdc.csd.cc29826w
title: CCDC 2122917: Experimental Crystal Structure Determination

description: Related Article: Li-Jie Gu, Hua-Bin Han, Zhan-Wei Bu, Qi-Lin Wang|2022|Org.Lett.|24|2008|doi:10.1021/acs.orglett.2c00464
DOI: 10.5517/ccdc.csd.cc2982b0
title: CCDC 2122921: Experimental Crystal Structure Determination

description: Related Article: Li-Jie Gu, Hua-Bin Han, Zhan-Wei Bu, Qi-Lin Wang|2022|Org.Lett.|24|2008|doi:10.1021/acs.orglett.2c00464
DOI: 10.5517/ccdc.csd.cc2982d2
title: CCDC 2122923: Experimental Crystal Structure Determination

description: Related Article: Li-Jie Gu, Hua-Bin Han, Zhan-Wei Bu, Qi-Lin Wang|2022|Org.Lett.|24|2008|doi:10.1021/acs.orglett.2c00464
DOI: 10.5517/ccdc.csd.cc2982j6
title: CCDC 2122927: Experimental Crystal Structure Determination

description: Related Article: Li-Jie Gu, Hua-Bin Han, Zhan-Wei Bu, Qi-Lin Wang|2022|Org.Lett.|24|2008|doi:10.1021/acs.orglett.2c00464
DOI: 10.6084/m9.figshare.19425656
title: COMSOL project file for CED simulation - Catheter tip outside tumour

description: Baseline tumour microenvironment conditions (Hydraulic conductivity of the tumour interstitial space, k=2×10<sup>-13</sup> m<sup>2</sup>/(Pa.s); Diameter of vessel wall pores, Do=100 nm).Catheter tip is located outside the tumour region.<br>
DOI: 10.5517/ccdc.csd.cc29djbl
title: CCDC 2127199: Experimental Crystal Structure Determination

description: Related Article: Kateryna Kravets, Mykola Kravets, Helena Butkiewicz, Sandra Kosiorek, Volodymyr Sashuk, Oksana Danylyuk|2022|CrystEngComm|24|2213|doi:10.1039/D2CE00232A
DOI: 10.5517/ccdc.csd.cc29djcm
title: CCDC 2127200: Experimental Crystal Structure Determination

description: Related Article: Kateryna Kravets, Mykola Kravets, Helena Butkiewicz, Sandra Kosiorek, Volodymyr Sashuk, Oksana Danylyuk|2022|CrystEngComm|24|2213|doi:10.1039/D2CE00232A
DOI: 10.5517/ccdc.csd.cc29wsll
title: CCDC 2141870: Experimental Crystal Structure Determination

description: Related Article: Bo Lin, Yunfei Yao, Yangjie Huang, Zhiqiang Weng|2022|Org.Lett.|24|2055|doi:10.1021/acs.orglett.2c00637
DOI: 10.5517/ccdc.csd.cc29wsmm
title: CCDC 2141871: Experimental Crystal Structure Determination

description: Related Article: Bo Lin, Yunfei Yao, Yangjie Huang, Zhiqiang Weng|2022|Org.Lett.|24|2055|doi:10.1021/acs.orglett.2c00637
DOI: 10.6084/m9.figshare.19425653
title: COMSOL project file for CED simulation - Catheter tip inside tumour

description: Baseline tumour microenvironment conditions (Hydraulic conductivity of the tumour interstitial space, k=2×10<sup>-13</sup> m<sup>2</sup>/(Pa.s); Diameter of vessel wall pores, Do=100 nm). <br>Catheter tip is located inside the tumour region.
DOI: 10.6084/m9.figshare.11663574.v10
title: COCI Scholix dataset of all the citation data

description: This dataset contains all the citation data (in Scholix format) included in COCI, released on 29 January 2022.<br>This version of the dataset contains:<br>1,271,360,867 citations;71,337,645 bibliographic resources.The size of the zipped archive is 33.59 GB, while the size of the unzipped Scholix file is 1.13 TB.<br>Additional information about COCI can be retrieved in the official webpage.<br>
DOI: 10.6084/m9.figshare.6741431.v13
title: COCI CSV dataset of the provenance information of all the citation data

description: This dataset contains the provenance information (in CSV format) of all the citation data included in COCI, released on 29 January 2022. In particular, each line of the CSV file defines a citation, and includes the following information:<b>[field "oci"]</b> the Open Citation Identifier (OCI) for the citation;<b>[field "agent"]</b> the name of the agent that have created the citation data;<b>[field "source"]</b> the URL of the source dataset from where the citation data have been extracted;<b>[field "datetime"]</b> the creation time of the citation data.The size of the zipped archive is 18.2 GB, while the size of the unzipped CSV file is 286.5 GB.<br>Additional information about COCI can be retrieved in the official webpage.
DOI: 10.6084/m9.figshare.6741446.v13
title: COCI N-Triples dataset of the provenance information of all the citation data

description: This dataset contains the provenance information (in N-Triples format) of all the citation data included in COCI, released on the 29 January 2022. In particular, any citation in the dataset includes the following provenance information:<b>[citation IRI]</b> the Open Citation Identifier (OCI) for the citation, defined in the final part of the URL identifying the citation (https://w3id.org/oc/index/coci/ci/[OCI]);;<b>[property "prov:wasAttributedTo"]</b> the IRI of the agent that have created the citation data;<b>[property "prov:hadPrimarySource"]</b> the IRI of the source dataset from where the citation data have been extracted;<b>[property "prov:generatedAtTime"]</b> the creation time of the citation data.The size of the zipped archive is 67.16 GB, while the size of the unzipped N-Triples file is 2.89 TB.<br>Additional information about COCI can be retrieved in the official webpage.
DOI: 10.34740/kaggle/dsv/3164215
title: Truckers_tweets
DOI: 10.34740/kaggle/dsv/3363196
title: Vietnam SARS-CoV-2 | COVID-19 Data
DOI: 10.5281/zenodo.6377480
title: Dataset for detecting the electrical behavior of photovoltaic panels from RGB images

description: The dynamic reconfiguration and maximum power point tracking in large-scale photovoltaic (PV) systems require a large number of voltage and current sensors. In particular, the reconfiguration process requires a pair of voltage/current sensors for each panel, which introduces costs, increases size and reduces reliability of the installation. A suitable solutions for reducing the number of sensors is to adopt image-based solution to estimate the electrical characteristics of the PV panels, but the lack of reliable data with large diversity of irradiance and shading conditions is a major problem in this topic. Therefore, this paper presents dataset correlating RGB images and electrical data of PV panels with different irradiance and shading conditions. The dataset was designed to support the design of image-based estimators of electrical data, which could be used to replace large arrays of sensors. The paper also describes the measurement platform used to collect the data, which helps to replicate the experiments in different geographical locations.