cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8202985
title: SCALABLE - Social-ecological pathways and gender perspectives for future conservation of biocultural mountain agro-ecosystems - Deliverable 1.1: Database of social perceptions towards impacts and threats

description: <strong>D1.1: Database of social perceptions towards impacts and threats</strong> <strong>.- Social database in case study 1: Almócita, Almería, Spain</strong> This dataset contains the results from a social survey targeting rural local communities over their perceptions on rural abandonment. Respondents were asked about: a) relation with the rural area and human-nature connectedness; b) perception toward rural abandonment and drivers of change; c) specific activities that in the municipality are carried out to face rural abandonment and how those activities are connected and promote different human well-being components. A final section of the survey compiled socio-demographic information to allow the characterization of the respondents. The survey was carried out by face-to-face and the data compilation was using tablets and manually. In this upload you can find 2 files: 1- Database: this file contains the dataset with all the valid responses to the survey. blank spaces mean that the question was left unanswered by the respondents. 2- Document of the face-to-face survey with all the questions and panels used for the social sampling: a) panel of drivers of change; b) panel of activities; and c) panel of contributions. <strong>.- Social database in case study 2: Purchena, Almería, Spain</strong> This dataset contains the results from a social survey targeting rural local communities over their perceptions on human-nature connections and their dimensions, as well as factors for reconnecting with rural areas. Respondents were asked about: a) human-nature connectedness in a rural areas; b) dimensions of human-nature connectedness; and c) reconnection with nature. A final section of the survey compiled socio-demographic information to allow the characterization of the respondents. The survey was carried out by face-to face and online, and the data compilation was manually. In this upload you can find 2 files: 1- Database: this file contains the dataset with all the valid responses to the survey. blank spaces mean that the question was left unanswered by the respondents. 2- Document of the face-to-face survey with all the questions used for the social sampling.
description: "This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 101031168"
DOI: 10.5281/zenodo.7584656
title: A Systematic Literature Review of Machine Learning for Uncovering Software Faults and Failures

description: This data set contains the results of an extensive, systematic literature review on the use of machine learning (ML) for uncovering software faults and failures. Covering the period of 2019 to 2022, this literature review identifies 874 relevant publications, classified into six distinct quality assurance tasks. Results show a compound annual growth rate (CAGR) of relevant publications of 38% over the last five years. This literature review particularly analyzed in how far these relevant papers leverage synergies between different quality assurance tasks. Results show that only 3% of all relevant papers leverage such synergies, indicating ample opportunities for future research. For example, a single type of quality assurance activity may not suffice to deliver the expected software quality. Ideally, one would use a suitable combination of different types of activities – such as combining dynamic testing with static code analysis. Also, leveraging the synergies between different quality assurance activities can increase the effectiveness of the individual activities. For example, having a good estimate of the fault density of a software component (e.g., using deep learning-driven fault prediction techniques) could help optimize and prioritize testing effort and budget.
DOI: 10.5281/zenodo.7584657
title: A Systematic Literature Review of Machine Learning for Uncovering Software Faults and Failures

description: This data set contains the results of an extensive, systematic literature review on the use of machine learning (ML) for uncovering software faults and failures. While existing literature reviews provide important insights into the use of ML for uncovering software faults and failures, they exhibit shortcomings with respect the following two concerns. (1) Precise Terminology: Many terms are used in the software engineering literature -- often interchangeably -- to describe software quality problems, such as bug, error, defect, fault, and failure. Yet, to accurately classify the relevant papers, a precise definition of what software quality problems are addressed is needed. One precise distinction can be made between the cause of a software quality problem and the observation of a software quality problem during system execution. The fundamental approaches followed by quality assurance tasks differ depending on whether they aim to observe a problem or whether they aim to understand and pinpoint the cause of a problem. Using such precise terminology provides a more fine-grained classification of the quality assurance tasks performed to uncover software quality problems than what existing literature reviews provide. We clearly differentiate between the two kinds of software quality problems, calling the cause of a problem a fault and the observed problem a failure, which allows us in turn to classify the relevant papers into distinct types of quality assurance tasks. Covering the period of 2019 to 2022, we identified 874 relevant papers in six distinct quality assurance tasks. (2) Synergies among Quality Assurance Tasks: While existing literature reviews provide some high-level, relevant insights into research progress and remaining gaps in quality assurance, they treat the different quality assurance tasks in isolation, thus ignoring potential synergies that may exist between different quality assurance tasks. Yet, typically not a single type of quality assurance task or technique will suffice to deliver the expected software quality. Ideally, one would use a suitable set and combination of techniques, such as combining testing -- as a dynamic technique -- with static code analysis. In addition, when combining quality assurance techniques, one may increase the effectiveness of the individual techniques; e.g., having a good estimate of the fault density of a component may direct limited testing effort and budget. Covering the period of 2019 to 2022, we identified 26 papers that explicitly leverage synergies, which is only 3% of all relevant papers, indicating ample opportunities for further exploring synergies in future research.
DOI: 10.5281/zenodo.7615631
title: A Systematic Literature Review of Machine Learning for Uncovering Software Faults and Failures

description: This data set contains the results of an extensive, systematic literature review on the use of machine learning (ML) for uncovering software faults and failures. Covering the period of 2019 to 2022, this literature review identifies 874 relevant publications, classified into six distinct quality assurance tasks. Results show a compound annual growth rate (CAGR) of relevant publications of 38% over the last five years. This literature review particularly analyzed in how far these relevant papers leverage synergies between different quality assurance tasks. Results show that only 3% of all relevant papers leverage such synergies, indicating ample opportunities for future research. For example, a single type of quality assurance activity may not suffice to deliver the expected software quality. Ideally, one would use a suitable combination of different types of activities – such as combining dynamic testing with static code analysis. Also, leveraging the synergies between different quality assurance activities can increase the effectiveness of the individual activities. For example, having a good estimate of the fault density of a software component (e.g., using deep learning-driven fault prediction techniques) could help optimize and prioritize testing effort and budget.
DOI: 10.5519/qd.jik9fixb
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (71422 records)
DOI: 10.5281/zenodo.8203242
title: Datasets for ``Electromagnetic conversion into kinetic and thermal energies''

description: <pre>This directory contains an index.html file with links to the run directories and idl plotting routines with secondary data for the other figures for the paper "Electromagnetic conversion into kinetic and thermal energies" by A. Brandenburg and N. Protiti. If anything turns out to be incomplete, please email brandenb@nordita.org. </pre>
DOI: 10.5281/zenodo.8203241
title: Datasets for ``Electromagnetic conversion into kinetic and thermal energies''

description: <pre>This directory contains an index.html file with links to the run directories and idl plotting routines with secondary data for the other figures for the paper "Electromagnetic conversion into kinetic and thermal energies" by A. Brandenburg and N. Protiti. If anything turns out to be incomplete, please email brandenb@nordita.org. </pre>
DOI: 10.5281/zenodo.8203277
title: Environmental sensor data for occupancy detection

description: This dataset consist of measurements from environmental sensors, installed in different rooms of a building under construction. The data were collected during the EU project ASHVIN and can be used for occupancy detection. The dataset includes the following variables: timestamp; date; time; sound level; temperature; relative humidity; dust; pressure; activity; activity (boolean). The columns 'activity' can be used as ground truth for occupancy detection.
DOI: 10.5281/zenodo.8203278
title: Environmental sensor data for occupancy detection

description: This dataset consist of measurements from environmental sensors, installed in different rooms of a building under construction. The data were collected during the EU project ASHVIN and can be used for occupancy detection. The dataset includes the following variables: timestamp; date; time; sound level; temperature; relative humidity; dust; pressure; activity; activity (boolean). The columns 'activity' can be used as ground truth for occupancy detection.
DOI: 10.5281/zenodo.7224991
title: Data and supplementary material for the paper "Development of an IntelliCage based Cognitive Bias Test for Mice."

description: All raw data and the supplementary material related to the paper "Development of an IntelliCage based Cognitive Bias Test for Mice." are available to the scientific public here. A preprint version of the paper will be published on bioRxiv.
description: This work was funded by the DFG (FOR 2591; LE 2356/5-1).
DOI: 10.5281/zenodo.7270516
title: Data and supplementary material for the paper "Development of an IntelliCage based Cognitive Bias Test for Mice."

description: All raw data, R scripts for analysis and the supplementary material related to the paper "Development of an IntelliCage based Cognitive Bias Test for Mice." are available to the scientific public here. A preprint version of the paper will be published on bioRxiv. Version 2 contains R scripts, version 1 does not. A pre-print version can be found at bioRxiv: https://www.biorxiv.org/content/10.1101/2022.10.19.512853v1
description: This work was funded by the DFG (FOR 2591; LE 2356/5-1).
DOI: 10.5281/zenodo.7308210
title: Data and supplementary material for the paper "Development of an IntelliCage based Cognitive Bias Test for Mice."

description: All raw data, R scripts for analysis and the supplementary material related to the paper "Development of an IntelliCage based Cognitive Bias Test for Mice." are available to the scientific public here. A preprint version of the paper will be published on bioRxiv. Version 2 contains R scripts, version 1 does not. Version 3: For better clarity, the data were saved as .ods files. Each .ods file contains the data for one developmental step. The R scripts are still available as .txt files. In addition, an ARRIVE checklist was added. A pre-print version can be found at bioRxiv: https://www.biorxiv.org/content/10.1101/2022.10.19.512853v1
description: This work was funded by the DFG (FOR 2591; LE 2356/5-1).
DOI: 10.5281/zenodo.7224990
title: Data and supplementary material for the paper "Development of an IntelliCage based Cognitive Bias Test for Mice."

description: All raw data, R scripts for analysis and the supplementary material related to the paper "Development of an IntelliCage based Cognitive Bias Test for Mice." are available to the scientific public here. A preprint version of the paper will be published on bioRxiv. Version 2 contains R scripts, version 1 does not. Version 3: For better clarity, the data were saved as .ods files. Each .ods file contains the data for one developmental step. The R scripts are still available as .txt files. In addition, an ARRIVE checklist was added. Version 4: xlsx instead of ods fiels Version 5: Extended supplemet PDF file<br> Added GroupTwo_Entries and GroupThree_Entries txt files A pre-print version can be found at bioRxiv: https://www.biorxiv.org/content/10.1101/2022.10.19.512853v1
description: This work was funded by the DFG (FOR 2591; LE 2356/5-1).
DOI: 10.5281/zenodo.7310180
title: Data and supplementary material for the paper "Development of an IntelliCage based Cognitive Bias Test for Mice."

description: All raw data, R scripts for analysis and the supplementary material related to the paper "Development of an IntelliCage based Cognitive Bias Test for Mice." are available to the scientific public here. A preprint version of the paper will be published on bioRxiv. Version 2 contains R scripts, version 1 does not. Version 3: For better clarity, the data were saved as .ods files. Each .ods file contains the data for one developmental step. The R scripts are still available as .txt files. In addition, an ARRIVE checklist was added. Version 4: xlsx instead of ods fiels A pre-print version can be found at bioRxiv: https://www.biorxiv.org/content/10.1101/2022.10.19.512853v1
description: This work was funded by the DFG (FOR 2591; LE 2356/5-1).
DOI: 10.5281/zenodo.8203397
title: Data and supplementary material for the paper "Development of an IntelliCage based Cognitive Bias Test for Mice."

description: All raw data, R scripts for analysis and the supplementary material related to the paper "Development of an IntelliCage based Cognitive Bias Test for Mice." are available to the scientific public here. A preprint version of the paper will be published on bioRxiv. Version 2 contains R scripts, version 1 does not. Version 3: For better clarity, the data were saved as .ods files. Each .ods file contains the data for one developmental step. The R scripts are still available as .txt files. In addition, an ARRIVE checklist was added. Version 4: xlsx instead of ods fiels Version 5: Extended supplemet PDF file<br> Added GroupTwo_Entries and GroupThree_Entries txt files A pre-print version can be found at bioRxiv: https://www.biorxiv.org/content/10.1101/2022.10.19.512853v1
description: This work was funded by the DFG (FOR 2591; LE 2356/5-1).
DOI: 10.5519/qd.9q25n55x
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (56 records)
DOI: 10.5519/qd.i8m7xgrw
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (70585 records)
DOI: 10.34740/kaggle/dsv/6230328
title: Egypt Gold Prices | Daily Updated
DOI: 10.5281/zenodo.8198722
title: Dataset to publication: ''Magnetstein: An open source tool for quantitative NMR mixture analysis robust to low resolution, distorted lineshapes and peak shifts''

description: MANIQ (plugin for Mnova 14.3.1 software (Mestrelab Research, S. L., Spain)) and ACD/TP (Targeted Profiling tool for ACD/Spectrus 2020.1.1 (Advanced Chemistry Development, Inc., ACD/Labs Ontario, Toronto, ON, Canada)) project files and raw experimental NMR data to: Experiments obtained to Magnetstein with optimized parameters, Magnetstein with default parameters, ACD/TP and MANIQ. Exp. no. Problem Ingredients 1 large intensity differences<br> between peaks α-pinene<br> benzyl benzoate<br> (in CDCl<sub>3</sub>) 2 peak overlap<br> & contamination (extra peaks) imonene<br> α-pinene<br> (in CDCl<sub>3</sub>) 3 low resolution (43 MHz<br> benchtop NMR spectrometer) benzyl benzoate<br> isopropyl myristate<br> limonene<br> pinene 4 lineshape distortion<br> (shim z1 & z2) actate<br> alanine<br> creatine<br> creatinine<br> choline chloride<br> (in D<sub>2</sub>O) 5 peak position<br> mismatch<br> between library<br> and mixture<br> (different<br> temperatures) actate<br> alanine<br> creatine<br> creatinine<br> choline chloride<br> (in D<sub>2</sub>O) 6 test sample α-pinene<br> benzyl benzoate<br> (in CDCl<sub>3</sub>) 7 large intensity<br> differences<br> & peak overlap <br> benzyl benzoate<br> m-anisaldehyde<br> (in CDCl<sub>3</sub>) 8 <br> peak overlap &<br> different solvents<br> for single<br> ingredients (in CDCl<sub>3</sub>)<br> and mixture (DMSO-d<sub>6</sub>) benzyl benzoate<br> m-anisaldehyde 9 ineshape<br> distortion<br> (shim z2) actate<br> alanine<br> creatine<br> creatinine<br> choline chloride<br> (in D<sub>2</sub>O)
DOI: 10.5281/zenodo.8198721
title: Dataset to publication: ''Magnetstein: An open source tool for quantitative NMR mixture analysis robust to low resolution, distorted lineshapes and peak shifts''

description: MANIQ (plugin for Mnova 14.3.1 software (Mestrelab Research, S. L., Spain)) and ACD/TP (Targeted Profiling tool for ACD/Spectrus 2020.1.1 (Advanced Chemistry Development, Inc., ACD/Labs Ontario, Toronto, ON, Canada)) project files and raw experimental NMR data to: Experiments obtained to Magnetstein with optimized parameters, Magnetstein with default parameters, ACD/TP and MANIQ. Exp. no. Problem Ingredients 1 large intensity differences<br> between peaks α-pinene<br> benzyl benzoate<br> (in CDCl<sub>3</sub>) 2 peak overlap<br> & contamination (extra peaks) imonene<br> α-pinene<br> (in CDCl<sub>3</sub>) 3 low resolution (43 MHz<br> benchtop NMR spectrometer) benzyl benzoate<br> isopropyl myristate<br> limonene<br> pinene 4 lineshape distortion<br> (shim z1 & z2) actate<br> alanine<br> creatine<br> creatinine<br> choline chloride<br> (in D<sub>2</sub>O) 5 peak position<br> mismatch<br> between library<br> and mixture<br> (different<br> temperatures) actate<br> alanine<br> creatine<br> creatinine<br> choline chloride<br> (in D<sub>2</sub>O) 6 test sample α-pinene<br> benzyl benzoate<br> (in CDCl<sub>3</sub>) 7 large intensity<br> differences<br> & peak overlap <br> benzyl benzoate<br> m-anisaldehyde<br> (in CDCl<sub>3</sub>) 8 <br> peak overlap &<br> different solvents<br> for single<br> ingredients (in CDCl<sub>3</sub>)<br> and mixture (DMSO-d<sub>6</sub>) benzyl benzoate<br> m-anisaldehyde 9 ineshape<br> distortion<br> (shim z2) actate<br> alanine<br> creatine<br> creatinine<br> choline chloride<br> (in D<sub>2</sub>O)
DOI: 10.58060/68q7-ac72
title: EVA (European Vegetation Archive) and ReSurveyEurope – version 2023-03-24

description: The European Vegetation Archive is a collective database of vegetation plots from Europe and adjacent regions integrated into a single platform. Each plot contains a list of plant species in a specific small area, usually supplemented by an estimate of the cover of each species. EVA consists of individual databases owned by data contributors. Any use of the data from the individual databases is subject to the consent of the data owner. ReSurveyEurope is integrated as a subset of EVA. It contains vegetation-plot data with repeated measurements over time, including data from permanent plots and resurveys of vegetation survey plots.
DOI: 10.58060/d4ve-r773
title: EVA (European Vegetation Archive) and ReSurveyEurope – version 2023-07-16

description: The European Vegetation Archive is a collective database of vegetation plots from Europe and adjacent regions integrated into a single platform. Each plot contains a list of plant species in a specific small area, usually supplemented by an estimate of the cover of each species. EVA consists of individual databases owned by data contributors. Any use of the data from the individual databases is subject to the consent of the data owner. ReSurveyEurope is integrated as a subset of EVA. It contains vegetation-plot data with repeated measurements over time, including data from permanent plots and resurveys of vegetation survey plots.
DOI: 10.58060/t34d-wh18
title: EVA project # 186 – 2023-05-11 The biogeography and evolution of willow species (Salix L.) of the European mountain systems – L. Pittet
title: SELECTION 2023-06-06

description: Final approved selection from European Vegetation Archive (EVA, incl. ReSurveyEurope) database for this EVA project specified with selection date. The EVA data are available on request using procedure described at How to obtain data (https://euroveg.org/eva-database/obtaining-data).
DOI: 10.58060/skgn-kb66
title: EVA project # 185 – 2023-05-11 The combined effect of nitrogen deposition and climate change on dry heathland habitats – H. Krajenbrink
title: SELECTION 2023-06-09

description: Final approved selection from European Vegetation Archive (EVA, incl. ReSurveyEurope) database for this EVA project specified with selection date. The EVA data are available on request using procedure described at How to obtain data (https://euroveg.org/eva-database/obtaining-data).
DOI: 10.58060/qr4b-g979
title: EVA project # 182 – 2023-04-20 safeGUARDing biodivErsity aNd critical ecosystem services across sectors and scales (GUARDEN) – C. Leblanc
title: SELECTION 2023-05-22

description: Final approved selection from European Vegetation Archive (EVA, incl. ReSurveyEurope) database for this EVA project specified with selection date. The EVA data are available on request using procedure described at How to obtain data (https://euroveg.org/eva-database/obtaining-data).
DOI: 10.58060/bpcr-e753
title: EVA project # 181 – 2023-04-06 The European Commission’s Knowledge Centre for Biodiversity – C. Liquete
title: SELECTION 2023-05-16

description: Final approved selection from European Vegetation Archive (EVA, incl. ReSurveyEurope) database for this EVA project specified with selection date. The EVA data are available on request using procedure described at How to obtain data (https://euroveg.org/eva-database/obtaining-data).
DOI: 10.58060/t7db-uc83
title: EVA project # 180 – 2023-03-28 Indicator systems for the reconstruction of Holocene climate controlled for local habitat development – P. Hájková, A. Šolcová, L. Tichý
title: SELECTION 2023-05-09

description: Final approved selection from European Vegetation Archive (EVA, incl. ReSurveyEurope) database for this EVA project specified with selection date. The EVA data are available on request using procedure described at How to obtain data (https://euroveg.org/eva-database/obtaining-data).
DOI: 10.58060/hs97-wc63
title: EVA project # 179 – 2023-03-16 Ecological and phytosociological differentiation in the polyploid complex Tanacetum corymbosum agg. (Asteraceae) in the Central Europe – K. Vantarová
title: SELECTION 2023-04-18

description: Final approved selection from European Vegetation Archive (EVA, incl. ReSurveyEurope) database for this EVA project specified with selection date. The EVA data are available on request using procedure described at How to obtain data (https://euroveg.org/eva-database/obtaining-data).
DOI: 10.58060/p97b-px54
title: EVA project # 178 – 2023-03-08 Functional structure of pioneer plant communities in river sedimentary bars – P. Janssen
title: SELECTION 2023-04-05

description: Final approved selection from European Vegetation Archive (EVA, incl. ReSurveyEurope) database for this EVA project specified with selection date. The EVA data are available on request using procedure described at How to obtain data (https://euroveg.org/eva-database/obtaining-data).