cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7698941
title: First Steps towards a Risk of Bias Corpus of Randomized Controlled Trials

description: <strong>Abstract</strong> Risk of bias (RoB) assessment of randomized clinical trials (RCTs) is vital to conducting systematic reviews. Manual RoB assessment for hundreds of RCTs is a cognitively demanding, lengthy process and is prone to subjective judgment. Supervised machine learning (ML) can help to accelerate this process but requires a hand-labelled corpus. There are currently no RoB annotation guidelines for randomized clinical trials or annotated corpora. In this pilot project, we test the practicality of directly using the revised Cochrane RoB 2.0 guidelines for developing an RoB annotated corpus using a novel multi-level annotation scheme. We report inter-annotator agreement among four annotators who used Cochrane RoB 2.0 guidelines. The agreement ranges between 0% for some bias classes and 76% for others. Finally, we discuss the shortcomings of this direct translation of annotation guidelines and scheme and suggest approaches to improve them to obtain an RoB annotated corpus suitable for ML. <strong>Methods</strong> The upload contains two zip files and a .json file. plain.html.zip Original corpus (n = 10) in .html format. The corpus was generated using the methodology described in the paper. Each .html file could be opened in any default text editor in any operating system or browser. A .html contains full text divided into several annotatable text parts. ann.json.zip The .zip contains RoB annotations conducted by the authors (R.H., M.S., K.G., R.C.). The annotation files are in .json format. Each .json is divided into two JSON objects and three JSON arrays. annotatable (object): Parts from the full-text document corresponding to the text parts from the plain .html files. metas (object): full-text document label entities (array): contains labelled entities. Each entity is linked to which part of the full-text it is linked to. relations (array) sources (array) annotations-legend.json This .json file contains entity and entity labels encoded to text legends. For example, entity class label "1_2_Yes_Good" is encoded as "e_113". <strong>Resources</strong> The code to parse annotations can be found on GitHub. <strong>Funding</strong> HES-SO Valais-Wallis, Sierre, Switzerland
description: {"references": ["Dhrangadhariya A et al. First Steps towards a Risk of Bias Corpus of Randomized Controlled Trials. In Proceedings Medical Informatics Europe 2023. Gothenburg, Sweden."]}
DOI: 10.5281/zenodo.7924460
title: First Steps towards a Risk of Bias Corpus of Randomized Controlled Trials

description: <strong>Abstract</strong> Risk of bias (RoB) assessment of randomized clinical trials (RCTs) is vital to conducting systematic reviews. Manual RoB assessment for hundreds of RCTs is a cognitively demanding, lengthy process and is prone to subjective judgment. Supervised machine learning (ML) can help to accelerate this process but requires a hand-labelled corpus. There are currently no RoB annotation guidelines for randomized clinical trials or annotated corpora. In this pilot project, we test the practicality of directly using the revised Cochrane RoB 2.0 guidelines for developing an RoB annotated corpus using a novel multi-level annotation scheme. We report inter-annotator agreement among four annotators who used Cochrane RoB 2.0 guidelines. The agreement ranges between 0% for some bias classes and 76% for others. Finally, we discuss the shortcomings of this direct translation of annotation guidelines and scheme and suggest approaches to improve them to obtain an RoB annotated corpus suitable for ML. <strong>Methods</strong> The upload contains two zip files and a .json file. plain.html.zip Original corpus (n = 10) in .html format. The corpus was generated using the methodology described in the paper. Each .html file could be opened in any default text editor in any operating system or browser. A .html contains full text divided into several annotatable text parts. ann.json.zip The .zip contains RoB annotations conducted by the authors (R.H., M.S., K.G., R.C.). The annotation files are in .json format. Each .json is divided into two JSON objects and three JSON arrays. annotatable (object): Parts from the full-text document corresponding to the text parts from the plain .html files. metas (object): full-text document label entities (array): contains labelled entities. Each entity is linked to which part of the full-text it is linked to. relations (array) sources (array) annotations-legend.json This .json file contains entity and entity labels encoded to text legends. For example, entity class label "1_2_Yes_Good" is encoded as "e_113". <strong>Resources</strong> The code to parse annotations can be found on GitHub. <strong>Funding</strong> HES-SO Valais-Wallis, Sierre, Switzerland
description: {"references": ["Dhrangadhariya A et al. First Steps towards a Risk of Bias Corpus of Randomized Controlled Trials. In Proceedings Medical Informatics Europe 2023. Gothenburg, Sweden."]}
DOI: 10.5281/zenodo.7924466
title: First Steps towards a Risk of Bias Corpus of Randomized Controlled Trials

description: <strong>Abstract</strong> Risk of bias (RoB) assessment of randomized clinical trials (RCTs) is vital to conducting systematic reviews. Manual RoB assessment for hundreds of RCTs is a cognitively demanding, lengthy process and is prone to subjective judgment. Supervised machine learning (ML) can help to accelerate this process but requires a hand-labelled corpus. There are currently no RoB annotation guidelines for randomized clinical trials or annotated corpora. In this pilot project, we test the practicality of directly using the revised Cochrane RoB 2.0 guidelines for developing an RoB annotated corpus using a novel multi-level annotation scheme. We report inter-annotator agreement among four annotators who used Cochrane RoB 2.0 guidelines. The agreement ranges between 0% for some bias classes and 76% for others. Finally, we discuss the shortcomings of this direct translation of annotation guidelines and scheme and suggest approaches to improve them to obtain an RoB annotated corpus suitable for ML. <strong>Methods</strong> The upload contains two zip files and a .json file. plain.html.zip Original corpus (n = 10) in .html format. The corpus was generated using the methodology described in the paper. Each .html file could be opened in any default text editor in any operating system or browser. A .html contains full text divided into several annotatable text parts. ann.json.zip The .zip contains RoB annotations conducted by the authors (R.H., M.S., K.G., R.C.). The annotation files are in .json format. Each .json is divided into two JSON objects and three JSON arrays. annotatable (object): Parts from the full-text document corresponding to the text parts from the plain .html files. metas (object): full-text document label entities (array): contains labelled entities. Each entity is linked to which part of the full-text it is linked to. relations (array) sources (array) annotations-legend.json This .json file contains entity and entity labels encoded to text legends. For example, entity class label "1_2_Yes_Good" is encoded as "e_113". <strong>Resources</strong> The code to parse annotations can be found on GitHub. <strong>Funding</strong> HES-SO Valais-Wallis, Sierre, Switzerland
description: {"references": ["Dhrangadhariya A et al. First Steps towards a Risk of Bias Corpus of Randomized Controlled Trials. In Proceedings Medical Informatics Europe 2023. Gothenburg, Sweden."]}
DOI: 10.6084/m9.figshare.22723862
title: <strong>Enhancing STEM Learning with ChatGPT and Bing Chat as Objects‑to‑Think‑With: A Case Study</strong>

description: Log capturing participants' interactions simulating STEM students' learning experiences with ChatGPT and Bing Chat, supporting findings and conclusions of the study "Enhancing STEM Learning with ChatGPT and Bing Chat as Objects to Think With: A Case Study," by Marco Antonio Rodrigues Vasconcelos and Renato P. dos Santos.
DOI: 10.5281/zenodo.7383033
title: SKY-LAUT – Carriage-based laser scanner data from Austrian forest stands

description: This dataset contains 3D point clouds, automatically calculated single tree parameters, reference data, and application videos of carriage-based laserscanning in cable yarding operations. Point clouds from 8 cable corridors and 4 scan varaints are provided in .las format in the folder point_clouds.zip. The individual files are labeled with numeric cable corridor IDs and scan variant IDs. According to standard conventions, a .las file contains a header block, variable-length records, and the point cloud data. The .las files can be read, visualized, and processed with common software programs for point cloud processing (e.g., CloudCompare), and they can also be handled with the free statistical software (e.g., R Foundation for Statistical Computing, Vienna, Austria). The reference and algorithm dataset is provided in a comma-separated values (CSV) file (algorithm_results_reference_data.csv ) and contains the manual and automatic measurements of the single-tree attributes. "stand_id" marks the stand (can be stand_1 or stand_2). "cable_corridor" can range from 1 to 8 and "scan_variant" from 1 to 4. "tree_id_ref" is a continuous id for the trees from the reference data collection. "x_ref", "y_ref", "dbh_ref", "h_ref" and "tree_species" are the coordinates, diameter at breast heigths, tree heights and tree species from the reference data collection. "x_cbls", "y_cbls", "dbh_ref" and "h_cbls" are the coordinates, diameter at breast heigth, tree height and tree species from carriage-based laser scanning point clouds and the automatic algorithm. "dist_to_skyline" is the orthogonal distance from the tree to the skyline. "tree_detection" indicates whether a tree was detected "correct", "non" oder "false" by the automatic algorithm.
DOI: 10.5281/zenodo.7923175
title: SKY-LAUT – Carriage-based laser scanner data from Austrian forest stands

description: This dataset contains 3D point clouds, automatically calculated single tree parameters, reference data, and application videos of carriage-based laserscanning in cable yarding operations. Point clouds from 8 cable corridors and 4 scan varaints are provided in .las format in the folder point_clouds.zip. The individual files are labeled with numeric cable corridor IDs and scan variant IDs. According to standard conventions, a .las file contains a header block, variable-length records, and the point cloud data. The .las files can be read, visualized, and processed with common software programs for point cloud processing (e.g., CloudCompare), and they can also be handled with the free statistical software (e.g., R Foundation for Statistical Computing, Vienna, Austria). The reference and algorithm dataset is provided in a comma-separated values (CSV) file (algorithm_results_reference_data.csv ) and contains the manual and automatic measurements of the single-tree attributes. "stand_id" marks the stand (can be stand_1 or stand_2). "cable_corridor" can range from 1 to 8 and "scan_variant" from 1 to 4. "tree_id_ref" is a continuous id for the trees from the reference data collection. "x_ref", "y_ref", "dbh_ref", "h_ref" and "tree_species" are the coordinates, diameter at breast heigths, tree heights and tree species from the reference data collection. "x_cbls", "y_cbls", "dbh_ref" and "h_cbls" are the coordinates, diameter at breast heigth, tree height and tree species from carriage-based laser scanning point clouds and the automatic algorithm. "dist_to_skyline" is the orthogonal distance from the tree to the skyline. "tree_detection" indicates whether a tree was detected "correct", "non" oder "false" by the automatic algorithm.
DOI: 10.5281/zenodo.7383032
title: SKY-LAUT – Carriage-based laser scanner data from Austrian forest stands

description: This dataset contains 3D point clouds, automatically calculated single tree parameters, reference data, and application videos of carriage-based laserscanning in cable yarding operations. Point clouds from 8 cable corridors and 4 scan varaints are provided in .las format in the folder point_clouds.zip. The individual files are labeled with numeric cable corridor IDs and scan variant IDs. According to standard conventions, a .las file contains a header block, variable-length records, and the point cloud data. The .las files can be read, visualized, and processed with common software programs for point cloud processing (e.g., CloudCompare), and they can also be handled with the free statistical software (e.g., R Foundation for Statistical Computing, Vienna, Austria). The reference and algorithm dataset is provided in a comma-separated values (CSV) file (algorithm_results_reference_data.csv ) and contains the manual and automatic measurements of the single-tree attributes. "stand_id" marks the stand (can be stand_1 or stand_2). "cable_corridor" can range from 1 to 8 and "scan_variant" from 1 to 4. "tree_id_ref" is a continuous id for the trees from the reference data collection. "x_ref", "y_ref", "dbh_ref", "h_ref" and "tree_species" are the coordinates, diameter at breast heigths, tree heights and tree species from the reference data collection. "x_cbls", "y_cbls", "dbh_ref" and "h_cbls" are the coordinates, diameter at breast heigth, tree height and tree species from carriage-based laser scanning point clouds and the automatic algorithm. "dist_to_skyline" is the orthogonal distance from the tree to the skyline. "tree_detection" indicates whether a tree was detected "correct", "non" oder "false" by the automatic algorithm.
DOI: 10.5519/qd.8ab23q6n
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (4479 records)
DOI: 10.5281/zenodo.7895955
title: RattanID - a molecular identification toolkit for rattan palms

description: This repository contains laboratory protocols, reference datasets, auxiliary files (target file and sequencing adapters) and example data for the RattanID molecular identification toolkit (https://github.com/BenKuhnhaeuser/RattanID). It also contains a dataset detailing rattan occurrence records at species level, rattan uses and extinction risk predictions, as well as distribution maps built based on the rattan occurrence records dataset.
DOI: 10.5281/zenodo.7733000
title: RattanID - a molecular identification toolkit for rattan palms

description: This repository contains laboratory protocols, reference datasets, auxiliary files (target file and sequencing adapters) and example data for the RattanID molecular identification toolkit (https://github.com/BenKuhnhaeuser/RattanID). It also contains a dataset detailing rattan occurrence records at species level, rattan uses and extinction risk predictions, as well as distribution maps built based on the rattan occurrence records dataset.
DOI: 10.34740/kaggle/ds/3254744
title: SDHAR: Sensor Dataset Human Activity Recognition
DOI: 10.5281/zenodo.7908639
title: EU-IoTs CSA's SRIA meta-analysis database

description: This database compiles the collection of research topics identified throughout various Strategic Research and Innovation Agendas published by Industry Associations relevant to the IoT ecosystem between 2020 and 2023. The present data along with the authors' classification was used to produce the STRATEGIC TOPICS AND THEMES RELATED TO THE NGIOT section within Deliverable 2.6 NGIoT Roadmap and Policy Recommendations of CSA Project: EU-IoT - The European IoT HUb - Growing a sustainable and comprehensive ecosystem for Next Generation Internet of Things. In performing the meta-analysis the following actions were taken to realise the comparison and data collection across the SRIAs and roadmaps: Within the scope of the identified target communities for the EU-IoT project and NGIoT Initiative7, the latest publications, roadmaps and SRIAs were revised and reviewed. Selected SRIAs to be included met the following criteria: Relevance to the scope of the NGIoT and latterly the Cloud Edge IoT Continuum. o Levelofdetailandstructuredrepresentation. Specificity and action ability of thetopic sprovided. From the selected agendas, individual topics were abstracted and categorised under the following fields to provide a comparable analysis and assessment: Type Priority area: considered to be topics of strategic importance, encompassing multiple technologies and applications. E.g., Constraint- based planning and decision making in complex natural environments. Application: specific implementations of technologies either within a given context or addressing a defined goal. E.g., Data streaming in constraint environments. Technology: a variety of different technical, electronic, or physical systems, assets, devices or algorithms. E.g., Self-configuring and adaptive sensor nodes. Theme: definition of the common priority theme taking a bottom-up approach and aligned with the NGIoT technologies. Position within the EU-IoT framework as described in the previous section: Layer: Tech, Market, Policy & Standards, Skills, All. Context: Human Interface, Far Edge, Near Edge, Infrastructure, Data Spaces, All. Finally, the analysis identified the key trends and themes across the contributing communities and NGIoT framework. In total 645 topics were abstracted, categorised and analysed across two cycles. The resulting database is provided as a public output for further analysis and reuse by the community and construction of future trend mapping. Within this paper, the latest versions of identified agendas were included in the analysis totalling 590 topics.
description: Contact information for questions or support: euiot@bluspecs.com
DOI: 10.5281/zenodo.7908640
title: EU-IoTs CSA's SRIA meta-analysis database

description: This database compiles the collection of research topics identified throughout various Strategic Research and Innovation Agendas published by Industry Associations relevant to the IoT ecosystem between 2020 and 2023. The present data along with the authors' classification was used to produce the STRATEGIC TOPICS AND THEMES RELATED TO THE NGIOT section within Deliverable 2.6 NGIoT Roadmap and Policy Recommendations of CSA Project: EU-IoT - The European IoT HUb - Growing a sustainable and comprehensive ecosystem for Next Generation Internet of Things. In performing the meta-analysis the following actions were taken to realise the comparison and data collection across the SRIAs and roadmaps: Within the scope of the identified target communities for the EU-IoT project and NGIoT Initiative7, the latest publications, roadmaps and SRIAs were revised and reviewed. Selected SRIAs to be included met the following criteria: Relevance to the scope of the NGIoT and latterly the Cloud Edge IoT Continuum. o Levelofdetailandstructuredrepresentation. Specificity and action ability of thetopic sprovided. From the selected agendas, individual topics were abstracted and categorised under the following fields to provide a comparable analysis and assessment: Type Priority area: considered to be topics of strategic importance, encompassing multiple technologies and applications. E.g., Constraint- based planning and decision making in complex natural environments. Application: specific implementations of technologies either within a given context or addressing a defined goal. E.g., Data streaming in constraint environments. Technology: a variety of different technical, electronic, or physical systems, assets, devices or algorithms. E.g., Self-configuring and adaptive sensor nodes. Theme: definition of the common priority theme taking a bottom-up approach and aligned with the NGIoT technologies. Position within the EU-IoT framework as described in the previous section: Layer: Tech, Market, Policy & Standards, Skills, All. Context: Human Interface, Far Edge, Near Edge, Infrastructure, Data Spaces, All. Finally, the analysis identified the key trends and themes across the contributing communities and NGIoT framework. In total 645 topics were abstracted, categorised and analysed across two cycles. The resulting database is provided as a public output for further analysis and reuse by the community and construction of future trend mapping. Within this paper, the latest versions of identified agendas were included in the analysis totalling 590 topics.
description: Contact information for questions or support: euiot@bluspecs.com
DOI: 10.6084/m9.figshare.20348287
title: Bringing albedo to the GHG market

description: A global warming potential of albedo (GWP<sub>A</sub>) is proposed, that represents the carbon dioxide emissions equivalent to a 0.01 increase in albedo over 1 m<sup>2</sup> of horizontal surface. A survey of prior literature suggests GWP<sub>A</sub> ≈ −4.2 kgCO<sub>2</sub>/m<sup>2</sup>. Taking Los Angeles, CA as a test site for urban global warming mitigation actions, a residential “cool roof” project offers approximately seven times as much radiative forcing benefit from albedo change as from GHG reduction of energy efficiency; and a citywide increase to commercial building roof albedo offers radiative forcing benefit equivalent to the first 6½ years of all commercial sector GHG emission reductions proposed in the City of Los Angeles climate action plan. Discussion explores pathways and challenges to making albedo increases fungible with GHG reductions in GHG markets.
DOI: 10.6084/m9.figshare.20348287.v1
title: Bringing albedo to the GHG market

description: A global warming potential of albedo (GWP<sub>A</sub>) is proposed, that represents the carbon dioxide emissions equivalent to a 0.01 increase in albedo over 1 m<sup>2</sup> of horizontal surface. A survey of prior literature suggests GWP<sub>A</sub> ≈ −4.2 kgCO<sub>2</sub>/m<sup>2</sup>. Taking Los Angeles, CA as a test site for urban global warming mitigation actions, a residential “cool roof” project offers approximately seven times as much radiative forcing benefit from albedo change as from GHG reduction of energy efficiency; and a citywide increase to commercial building roof albedo offers radiative forcing benefit equivalent to the first 6½ years of all commercial sector GHG emission reductions proposed in the City of Los Angeles climate action plan. Discussion explores pathways and challenges to making albedo increases fungible with GHG reductions in GHG markets.
DOI: 10.5281/zenodo.7923651
title: Scheduling Maintenance Activities subject to Stochastic Job-Dependent Machine Deterioration

description: Test instances and source code
DOI: 10.5281/zenodo.7923650
title: Scheduling Maintenance Activities subject to Stochastic Job-Dependent Machine Deterioration

description: Test instances and source code
DOI: 10.5281/zenodo.7785320
title: Dataset for the paper High durability and stability of 2D nanofluidic devices for long-term single-molecule sensing

description: Information regarding the Dataset, corresponding to the paper: “Thakur, M., Cai, N., Zhang, M. et al. High durability and stability of 2D nanofluidic devices for long-term single-molecule sensing. npj 2D Mater Appl 7, 11 (2023). https://doi.org/10.1038/s41699-023-00373-5” This folder contains the raw data and complete package of codes used to analyze, view, save, and plot data for the publication titled "High durability and stability of 2D nanofluidic devices for long-term single-molecule sensing". The code folder, "OpenNanopore-nanopore-tools", can be used to plot raw data which corresponds to the figures in the paper and supplementary information.