cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7867845
title: Dataset for Requirements Classification in Traceability Link Recovery Datasets

description: The dataset contains a gold standard for classifying parts of requirements in five traceability link recovery benchmark datasets. <strong>Classification</strong> For aspect classification: functional aspects (<strong>F</strong>) quality aspects (<strong>Q</strong>) For concerns in functional requirements (c.f. NoRBERT publication): <strong>Function</strong>: A function that a system shall perform <strong>Behavior</strong>: Behavior, the system displays or reactions that are triggered by one or more stimuli <strong>Data</strong>: A data item or data structure that shall be part of a system's state <strong>UserRelated</strong>: Behavior of the user or functionality of the system attributable to the user <strong>Datasets</strong> The dataset comprises preprocessed requirements of the eTour, iTrust, SMOS, eAnci and LibEST datasets. As SMOS and eAnci's original requirements were written in Italian, the dataset comprises automatically translated versions of the requirements to English. The datasets were retrieved from the website of the Center of Excellence for Software & Systems Traceability (CoEST). Attribution for the datasets: The original eTour dataset was provided for the TEFSE challenge at 6th International Workshop on Traceability in Emerging Forms of Software Engineering (TEFSE), 2011 and was retrieved from http://coest.org/ The iTrust dataset was retrieved from http://coest.org/ The original SMOS and eAnci datasets can be attributed to Gethers et al., On integrating orthogonal information retrieval methods to improve traceability recovery. In 2011 27th IEEE International Conference on Software Maintenance (ICSM), Sep. 2011 and were retrieved from http://coest.org/ The LibEST dataset can be attributed to Moran et al., Improving the Effectiveness of Traceability Link Recovery using Hierarchical Bayesian Networks. In 2020 IEEE/ACM 42nd International Conference on Software Engineering (ICSE), May 2020 and was retrieved from https://gitlab.com/SEMERU-Code-Public/Data/icse20-comet-data-replication-package
DOI: 10.5281/zenodo.8338927
title: MODIS MCD12Q1 Land Cover and Land Use Time Series Global Mosaics 2001-2022 (500 m)

description: <strong>General Description</strong> The yearly land use and land cover dataset is derived from MCD12Q1 v061. This data provides an yearly mosaics of land use and land cover data from 2001 to 2022 in cloud optimized Geotiff (COG) format. This dataset includes layers of land cover type 1 (t1), 2 (t2), and 5 (t5), land cover property 1 (p1) and 2 (p2), land cover property assessment 1 (p1a) and 2 (p2a), and land cover quality control (qc). <strong>Data Details</strong> <strong>Time period:</strong> 2001–2022 <strong>Type of data:</strong> Land cover and land use <strong>How the data was collected or derived:</strong> Derived from MCD12Q1 v061 using Google Earth Engine. <strong>Statistical methods used:</strong> None <strong>Limitations or exclusions in the data:</strong> The dataset does not include data for Antarctica. <strong>Coordinate reference system:</strong> EPSG:4326 <strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (-180.00000, -62.00081, 179.99994, 87.37000) <strong>Spatial resolution:</strong> 1/120 d.d. = 0.008333333 (1km) <strong>Image size:</strong> 86,400 x 35,849 <strong>File format:</strong> Cloud optimized Geotiff. <strong>Support</strong> If you discover a bug, artifact, or inconsistency, or if you have a question please use some of the following channels: Technical issues and questions about the code: GitLab Issues General questions and comments: LandGIS Forum <strong>Name convention</strong> To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are: generic variable name: lc = Land cover variable procedure combination: mcd12q1v061.t1 = MCD12Q1 v061 LC Type1 band Position in the probability distribution / variable type: c = class | p = probability Spatial support: 500m Depth reference: s = surface Time reference begin time: 20010101 = 2001-01-01 Time reference end time: 20011231 = 2001-12-31 Bounding box: go = global (without Antarctica) EPSG code: epsg.4326 = EPSG:4326 Version code: v20230818 = creation date
DOI: 10.5281/zenodo.8338928
title: MODIS MCD12Q1 Land Cover and Land Use Time Series Global Mosaics 2001-2022 (500 m)

description: <strong>General Description</strong> The yearly land use and land cover dataset is derived from MCD12Q1 v061. This data provides an yearly mosaics of land use and land cover data from 2001 to 2022 in cloud optimized Geotiff (COG) format. This dataset includes layers of land cover type 1 (t1), 2 (t2), and 5 (t5), land cover property 1 (p1) and 2 (p2), land cover property assessment 1 (p1a) and 2 (p2a), and land cover quality control (qc). <strong>Data Details</strong> <strong>Time period:</strong> 2001–2022 <strong>Type of data:</strong> Land cover and land use <strong>How the data was collected or derived:</strong> Derived from MCD12Q1 v061 using Google Earth Engine. <strong>Statistical methods used:</strong> None <strong>Limitations or exclusions in the data:</strong> The dataset does not include data for Antarctica. <strong>Coordinate reference system:</strong> EPSG:4326 <strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (-180.00000, -62.00081, 179.99994, 87.37000) <strong>Spatial resolution:</strong> 1/120 d.d. = 0.008333333 (1km) <strong>Image size:</strong> 86,400 x 35,849 <strong>File format:</strong> Cloud optimized Geotiff. <strong>Support</strong> If you discover a bug, artifact, or inconsistency, or if you have a question please use some of the following channels: Technical issues and questions about the code: GitLab Issues General questions and comments: LandGIS Forum <strong>Name convention</strong> To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are: generic variable name: lc = Land cover variable procedure combination: mcd12q1v061.t1 = MCD12Q1 v061 LC Type1 band Position in the probability distribution / variable type: c = class | p = probability Spatial support: 500m Depth reference: s = surface Time reference begin time: 20010101 = 2001-01-01 Time reference end time: 20011231 = 2001-12-31 Bounding box: go = global (without Antarctica) EPSG code: epsg.4326 = EPSG:4326 Version code: v20230818 = creation date
DOI: 10.5281/zenodo.8367523
title: MODIS MCD12Q1 Land Cover and Land Use Time Series Global Mosaics 2001-2022 (500 m)

description: <strong>General Description</strong> The yearly land use and land cover dataset is derived from MCD12Q1 v061. This data provides an yearly mosaics of land use and land cover data from 2001 to 2022 in cloud optimized Geotiff (COG) format. This dataset includes layers of land cover type 1 (t1), 2 (t2), and 5 (t5), land cover property 1 (p1) and 2 (p2), land cover property assessment 1 (p1a) and 2 (p2a), and land cover quality control (qc). <strong>Data Details</strong> <strong>Time period:</strong> 2001–2022 <strong>Type of data:</strong> Land cover and land use <strong>How the data was collected or derived:</strong> Derived from MCD12Q1 v061 using Google Earth Engine. <strong>Statistical methods used:</strong> None <strong>Limitations or exclusions in the data:</strong> The dataset does not include data for Antarctica. <strong>Coordinate reference system:</strong> EPSG:4326 <strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (-180.00000, -62.00081, 179.99994, 87.37000) <strong>Spatial resolution:</strong> 1/120 d.d. = 0.008333333 (1km) <strong>Image size:</strong> 86,400 x 35,849 <strong>File format:</strong> Cloud optimized Geotiff. <strong>Support</strong> If you discover a bug, artifact, or inconsistency, or if you have a question please use some of the following channels: Technical issues and questions about the code: GitLab Issues General questions and comments: LandGIS Forum <strong>Name convention</strong> To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are: generic variable name: lc = Land cover variable procedure combination: mcd12q1v061.t1 = MCD12Q1 v061 LC Type1 band Position in the probability distribution / variable type: c = class | p = probability Spatial support: 500m Depth reference: s = surface Time reference begin time: 20010101 = 2001-01-01 Time reference end time: 20011231 = 2001-12-31 Bounding box: go = global (without Antarctica) EPSG code: epsg.4326 = EPSG:4326 Version code: v20230818 = creation date
DOI: 10.21227/vdye-1q86
title: Collaborative robot true trajectories and pose estimations

description: This is a PART of the dataset used in our paper titled "Detecting Anomalous Robot Motion in Collaborative Robotic Manufacturing Systems".Abstract of the paper: Anomalous robot motions caused by cyber attacks and inherent defects can lead to task failures as well as harmful accidents in collaborative human-robot workplaces. External sensors are essential for reliably monitoring robot tool paths and detecting any deviation as early as possible, especially in anomalous situations where the robot system and its internal sensors may be out-of-control. However, affordable external sensors usually suffer from poor-quality measurements, necessitating enhancements. In addition to external sensors providing independent monitoring, to effectively detect trajectory deviations, we need anomaly detection methods that can capture complex dynamic patterns in the nonlinear trajectory time-series data. In this work, we propose a framework to accurately estimate robot trajectories during normal robot operations as well as to detect various types of anomalous trajectory changes using an external camera. The proposed framework efficiently incorporates marker-based pose estimation, Long Short-Term Memory (LSTM), and residual control charts. The framework was evaluated in a shared human-robot assembly task. The results show that we can accurately estimate robot trajectories by enhancing camera-based measurements. Moreover, it effectively detects anomalous trajectory changes in their early stages. The motion deviation upon detection assists in determining a safe working distance. The framework also exhibits generalizability to previously unseen trajectory deviations and possesses adaptability to other types of external sensors.
DOI: 10.25345/c5kh0f898
title: MassIVE MSV000092921 - 07282023_Dom_conversion_Chymo_Concentrations
title: Data dependent analysis of Synechococcus sp. digested with Chymotrypsin, and also in presence of Pepstatin and Chymostatin at different concentrations.
DOI: 10.21227/00bx-sf48
title: Collaborative robot true trajectories and pose estimations

description: This is a PART of the dataset used in our paper titled "Detecting Anomalous Robot Motion in Collaborative Robotic Manufacturing Systems".Abstract of the paper: Anomalous robot motions caused by cyber attacks and inherent defects can lead to task failures as well as harmful accidents in collaborative human-robot workplaces. External sensors are essential for reliably monitoring robot tool paths and detecting any deviation as early as possible, especially in anomalous situations where the robot system and its internal sensors may be out-of-control. However, affordable external sensors usually suffer from poor-quality measurements, necessitating enhancements. In addition to external sensors providing independent monitoring, to effectively detect trajectory deviations, we need anomaly detection methods that can capture complex dynamic patterns in the nonlinear trajectory time-series data. In this work, we propose a framework to accurately estimate robot trajectories during normal robot operations as well as to detect various types of anomalous trajectory changes using an external camera. The proposed framework efficiently incorporates marker-based pose estimation, Long Short-Term Memory (LSTM), and residual control charts. The framework was evaluated in a shared human-robot assembly task. The results show that we can accurately estimate robot trajectories by enhancing camera-based measurements. Moreover, it effectively detects anomalous trajectory changes in their early stages. The motion deviation upon detection assists in determining a safe working distance. The framework also exhibits generalizability to previously unseen trajectory deviations and possesses adaptability to other types of external sensors.
DOI: 10.6084/m9.figshare.24175129
title: Age, depositional history and tectonics of the Indo-Myanmar Ranges, Myanmar

description: Zircon U-Pb and Hf-isotope data.
DOI: 10.6084/m9.figshare.24175129.v1
title: Age, depositional history and tectonics of the Indo-Myanmar Ranges, Myanmar

description: Zircon U-Pb and Hf-isotope data.
DOI: 10.6084/m9.figshare.24175132
title: Age, depositional history and tectonics of the Indo-Myanmar Ranges, Myanmar

description: Sample List.
DOI: 10.6084/m9.figshare.24175132.v1
title: Age, depositional history and tectonics of the Indo-Myanmar Ranges, Myanmar

description: Sample List.
DOI: 10.6084/m9.figshare.24175137
title: 20230922_starrydata2

description: All data on Starrydata2, a database of experimental properties in the field of inorganic materials
DOI: 10.6084/m9.figshare.24175137.v1
title: 20230922_starrydata2

description: All data on Starrydata2, a database of experimental properties in the field of inorganic materials
DOI: 10.6084/m9.figshare.24174882
title: 非羊膜动物中逆转录病毒包膜蛋白的遗传多样性的补充材料

description: 非羊膜动物中逆转录病毒包膜蛋白的遗传多样性的补充材料
DOI: 10.6084/m9.figshare.24174882.v1
title: 非羊膜动物中逆转录病毒包膜蛋白的遗传多样性的补充材料

description: 非羊膜动物中逆转录病毒包膜蛋白的遗传多样性的补充材料
DOI: 10.57745/cbuyhe
title: P.vit_221_FalconUnzip.gene.prediction.gff3

description: Gene annotations
DOI: 10.57745/g7jq2d
title: P.vit_221_FalconUnzip.fasta

description: Primary+ Haplotigs
DOI: 10.57745/bujbth
title: P.vit_221_FalconUnzip_MT.fasta

description: Primary+ Haplotigs+mitochondira scaffold
DOI: 10.5281/zenodo.7391669
title: Data Set from RDA WG Discipline-Specific Guidance on DMP - Online Survey

description: Data Set from the Online Survey of the Research Data Alliance's Discipline-Specific Guidance for Data Management Plans Working Group. <br> The data was collected from November 8, 2021 to January 14, 2022. The survey was divided into the following areas after a brief introduction on "Purpose of this survey" and "Use of the information you provide." Demographics Data Description and Collection Data Documentation & Quality Data Archiving, Publishing & Sharing After the Project Guidelines, Principles & Best Practices Follow-up interviews and group discussions The analysis of the online survey was focused on the four areas: Natural Sciences, Life Sciences, Humanities & Social Sciences, and Engineering. The results of the evaluation will be presented in a separate publication. In addition to the data, the variables and values are also published here. The online survey questions can be accessed here: https://doi.org/10.5281/zenodo.7443373 The data were evaluated using R. The code is published under the following DOI: 10.5281/zenodo.7443467 A more detailed analysis and description can be found in the paper "Discipline-specific Aspects in Data Management Planning" submitted to Data Science Journal (2022-12-15).
DOI: 10.5281/zenodo.8367217
title: Dataset and R code from RDA WG Discipline-Specific Guidance on DMP - Online Survey

description: Dataset from the Online Survey of the Research Data Alliance's Discipline-Specific Guidance for Data Management Plans Working Group. <br> The data was collected from November 8, 2021 to January 14, 2022. The survey was divided into the following areas after a brief introduction on "Purpose of this survey" and "Use of the information you provide." Demographics Data Description and Collection Data Documentation & Quality Data Archiving, Publishing & Sharing After the Project Guidelines, Principles & Best Practices Follow-up interviews and group discussions The analysis of the online survey was focused on the four areas: Natural Sciences, Life Sciences, Humanities & Social Sciences, and Engineering. The results of the evaluation will be presented in a separate publication. In addition to the data, the variables and values are also published here. The online survey questions can be accessed here: https://doi.org/10.5281/zenodo.7443373 A more detailed analysis and description can be found in the paper "Discipline-specific Aspects in Data Management Planning" submitted to Data Science Journal (2022-12-15).
DOI: 10.5281/zenodo.7391668
title: Dataset and R code from RDA WG Discipline-Specific Guidance on DMP - Online Survey

description: Dataset from the Online Survey of the Research Data Alliance's Discipline-Specific Guidance for Data Management Plans Working Group. <br> The data was collected from November 8, 2021 to January 14, 2022. The survey was divided into the following areas after a brief introduction on "Purpose of this survey" and "Use of the information you provide." Demographics Data Description and Collection Data Documentation & Quality Data Archiving, Publishing & Sharing After the Project Guidelines, Principles & Best Practices Follow-up interviews and group discussions The analysis of the online survey was focused on the four areas: Natural Sciences, Life Sciences, Humanities & Social Sciences, and Engineering. The results of the evaluation will be presented in a separate publication. In addition to the data, the variables and values are also published here. The online survey questions can be accessed here: https://doi.org/10.5281/zenodo.7443373 A more detailed analysis and description can be found in the paper "Discipline-specific Aspects in Data Management Planning" submitted to Data Science Journal (2022-12-15).
DOI: 10.6084/m9.figshare.24003807.v1
title: PRISMA-P checklist for the publication 'Preventing deaths due to tuberculosis: an assessment of global targets with a protocol for a systematic review and meta-analysis'

description: BACKGROUND: Tuberculosis (TB) is believed to have caused more deaths than any other infection since records began. The “Sustainable Development Goals”, previous “Millennium Development Goals”, World Health Organisation “End TB Strategy” and the second and third “Global Plans to Stop TB” all prioritise(d) key targets to reduce deaths due to TB. However, there seems to be limited research evidence available to inform how this may best be achieved. We therefore aim to summarise, critically appraise, and synthesise the trial evidence that interventions decrease deaths due to TB. METHODS: We will follow the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. We will search the PubMed, Scopus and Web of Science databases for peer reviewed English/Spanish language publications focused on evaluating interventions to reduce deaths due to TB as primary or secondary trial outcomes. We plan to use the following search terms: tuberculosis OR TB; death OR mortality OR fatality OR survival; prevent* OR reduce* OR decrease*; AND trial. Eligible publications will be selected by two independent reviewers and a third will resolve any discrepancies. Key information will be extracted using a shared cloud-based spreadsheet, publications categorised and summarised and critically appraised. Key data will be extracted and synthesised. Meta-analysis will be carried out if there are three or more studies investigating similar interventions with a similar outcome. The quality of trial evidence and any risk of bias will be formally assessed using the Cochrane tools. This systematic review with meta-analysis is registered with the PROSPERO database (record number CRD42023387877). CONCLUSION: We report a protocol for a systematic review of the published literature involving trial evidence assessing whether interventions reduce deaths due to TB and a meta-analysis of the quantitative evidence. We aim to clarify research gaps and to synthesise evidence in order to guide future policy and research.
DOI: 10.5517/ccdc.csd.cc2c0gpk
title: CCDC 2175198: Experimental Crystal Structure Determination

description: Related Article: Haixiang Han, Yuxuan Zhang, Zheng Zhou, Jesse C. Carozza, Zheng Wei, Alexander S. Filatov, Andrey Shevtsov, Artem M. Abakumov, Evgeny V. Dikarev|2023|Inorg.Chem.|62|12931|doi:10.1021/acs.inorgchem.3c01664
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: 10.5517/ccdc.csd.cc2f9t7t
title: CCDC 2243756: Experimental Crystal Structure Determination

description: Related Article: Faiza Habib, Derek A. Tocher, Claire J. Carmalt|2023|CrystEngComm|25|5001|doi:10.1039/D3CE00592E
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: 10.5517/ccdc.csd.cc2g6xg2
title: CCDC 2270764: Experimental Crystal Structure Determination

description: Related Article: Jose de Jesus Velazquez-Garcia, Krishnayan Basuroy, Darina Storozhuk, Joanne Wong, Serhiy Demeshko, Franc Meyer, Robert Henning, Simone Techert|2023|Dalton Trans.|52|12224|doi:10.1039/D3DT02067C
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: 10.5517/ccdc.csd.cc2g6xh3
title: CCDC 2270765: Experimental Crystal Structure Determination

description: Related Article: Jose de Jesus Velazquez-Garcia, Krishnayan Basuroy, Darina Storozhuk, Joanne Wong, Serhiy Demeshko, Franc Meyer, Robert Henning, Simone Techert|2023|Dalton Trans.|52|12224|doi:10.1039/D3DT02067C
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: 10.5517/ccdc.csd.cc2g6xj4
title: CCDC 2270766: Experimental Crystal Structure Determination

description: Related Article: Jose de Jesus Velazquez-Garcia, Krishnayan Basuroy, Darina Storozhuk, Joanne Wong, Serhiy Demeshko, Franc Meyer, Robert Henning, Simone Techert|2023|Dalton Trans.|52|12224|doi:10.1039/D3DT02067C
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.