cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21954/ou.rd.22661593
title: Questionnaire: CONNECT-science reflective instrument - with metadata (see file properties)

description: Database Title:Connect-science self-reported instrumentDatabase Description:Students' self-report perceptions reflecting about science connection, science skills, science capital and scientific literacies in the context of open schoolingDatabase Citation:Okada A.; Sherborne T; Panselinas; G.; Bizoi, M; Malagrida, R; Ribeiro, S. Torres P. (2024). CONNECT - inclusive open schooling with engaging and future oriented science. CCBYSAContact email:ale.okada@open.ac.ukDatabase URL:https://ordo.open.ac.uk/projects/CONNECT_-_Inclusive_open_schooling_with_future_oriented_science/125821Information:This database provides the views of 12072 students who participated in CONNECT projectDatabase Methodology used to generated dataQuestionnaire designSemi-structured questionnaire including a combination of open-ended and closed-ended questions.Platform used for a coded questionnaire with feedback and open badgeQualtrixMultilanguage supportTargetlanguage (English, Greek, Portuguese, Romanian, Spanish in this case) toensure that respondents can understand and respond to the questions in theirpreferred language.Questionnaire implementationLogic for sore, feedback and open badge implementedLanguage selectionQualtrics allows respondents to select their preferred language before starting the survey.Data generationThe questionnaire was distributed to the target audience school studentsthrough teachers members of CONNECT project who agreed to contribute to this researchData storageAs respondents submit their responses, Qualtrics stores the data securely in its database infrastructure. Each response is associated with the respondent's unique identifier and includes the language in which the survey was completed.Data analysisExploratory factorial analysis, descriptive analyses and thematic analysis to support mixed methodsExtra InformationCreator of the Instrument used to generate this database:Okada, A. CONNECT-Science self-report instrumentThis database refers to CONNECT project:https://www.connect-science.net/Project description:inclusive open schooling with engaging and future oriented scienceFunder:European Commission No. 872814Questionnaire and database location:https://doi.org/10.21954/ou.rd.c.6668648Questionnaire citation:Okada A. (2024) CONNECT-science to sustainability with inclusive open schooling with engaging and future oriented science. CCBYSA<br>
DOI: 10.21954/ou.rd.22661593.v1
title: Questionnaire: CONNECT-science reflective instrument - with metadata (see file properties)

description: Database Title:Connect-science self-reported instrumentDatabase Description:Students' self-report perceptions reflecting about science connection, science skills, science capital and scientific literacies in the context of open schoolingDatabase Citation:Okada A.; Sherborne T; Panselinas; G.; Bizoi, M; Malagrida, R; Ribeiro, S. Torres P. (2024). CONNECT - inclusive open schooling with engaging and future oriented science. CCBYSAContact email:ale.okada@open.ac.ukDatabase URL:https://ordo.open.ac.uk/projects/CONNECT_-_Inclusive_open_schooling_with_future_oriented_science/125821Information:This database provides the views of 12072 students who participated in CONNECT projectDatabase Methodology used to generated dataQuestionnaire designSemi-structured questionnaire including a combination of open-ended and closed-ended questions.Platform used for a coded questionnaire with feedback and open badgeQualtrixMultilanguage supportTargetlanguage (English, Greek, Portuguese, Romanian, Spanish in this case) toensure that respondents can understand and respond to the questions in theirpreferred language.Questionnaire implementationLogic for sore, feedback and open badge implementedLanguage selectionQualtrics allows respondents to select their preferred language before starting the survey.Data generationThe questionnaire was distributed to the target audience school studentsthrough teachers members of CONNECT project who agreed to contribute to this researchData storageAs respondents submit their responses, Qualtrics stores the data securely in its database infrastructure. Each response is associated with the respondent's unique identifier and includes the language in which the survey was completed.Data analysisExploratory factorial analysis, descriptive analyses and thematic analysis to support mixed methodsExtra InformationCreator of the Instrument used to generate this database:Okada, A. CONNECT-Science self-report instrumentThis database refers to CONNECT project:https://www.connect-science.net/Project description:inclusive open schooling with engaging and future oriented scienceFunder:European Commission No. 872814Questionnaire and database location:https://doi.org/10.21954/ou.rd.c.6668648Questionnaire citation:Okada A. (2024) CONNECT-science to sustainability with inclusive open schooling with engaging and future oriented science. CCBYSA<br>
DOI: 10.5281/zenodo.10875471
title: Dataset for testing unit commitment models of CHCP plants

description: Zipped set of 365 JSON files, one for each day of year 2022, containing fictional but realistic data for a combined heat, power and cooling generation plant:



heat_demand: heat demand in kWh

cooling_demand: cooling demand in kWh

electricity_purchase: price for purchasing electricity in €/MWh

electricity_selling: price for selling electricity in €/MWh

gas_purchase: price for natural gas in €/MWh
DOI: 10.5281/zenodo.10875470
title: Dataset for testing unit commitment models of CHCP plants

description: Zipped set of 365 JSON files, one for each day of year 2022, containing fictional but realistic data for a combined heat, power and cooling generation plant:



heat_demand: heat demand in kWh

cooling_demand: cooling demand in kWh

electricity_purchase: price for purchasing electricity in €/MWh

electricity_selling: price for selling electricity in €/MWh

gas_purchase: price for natural gas in €/MWh
DOI: 10.5281/zenodo.8423404
title: Understanding Fatigue Through Biosignals: A Comprehensive Dataset

description: Fatigue is a multifaceted construct, that represents an important part of human experience. The two main aspects of fatigue are the mental one and the physical one, that often intertwine, intensifying their collective impact on daily life and overall well-being.To soften this impact, understanding and quantifying fatigue is crucial. Physiological data play a pivotal role in the comprehension of fatigue, allowing a precious insight into the level and type of fatigue experienced.

The MePhy dataset includes physiological data gathered while inducing different types of fatigue conditions, in particular mental and physical fatigue. We collected various biosignals closely associated with fatigue (ECG, EDA, EMG and Eye Blinking). Test participants endured a four-part experiment that aimed to elicit mental fatigue, physical fatigue and a combination of both. 

The main folder contains:



MePhy Dataset folder, which contains the dataset;

ReadMe.pdf, which provides more informations about the dataset;

Mental Fatigue Inducing Test folder, which includes the HTML application used to simulate mental fatigue in the test participants. 


 

A more in depth description of the MePhy dataset can be found in the following paper https://doi.org/10.1145/3610977.3637485.

Marta Gabbi, Luca Cornia, Valeria Villani, and Lorenzo Sabattini (2024) Understanding Fatigue Through Biosignals: A Comprehensive Dataset. In Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction (HRI ’24).
DOI: 10.5281/zenodo.8423405
title: Understanding Fatigue Through Biosignals: A Comprehensive Dataset

description: Fatigue is a multifaceted construct, that represents an important part of human experience. The two main aspects of fatigue are the mental one and the physical one, that often intertwine, intensifying their collective impact on daily life and overall well-being.To soften this impact, understanding and quantifying fatigue is crucial. Physiological data play a pivotal role in the comprehension of fatigue, allowing a precious insight into the level and type of fatigue experienced.

The MePhy dataset includes physiological data gathered while inducing different types of fatigue conditions, in particular mental and physical fatigue. We collected various biosignals closely associated with fatigue (ECG, EDA, EMG and Eye Blinking). Test participants endured a four-part experiment that aimed to elicit mental fatigue, physical fatigue and a combination of both. 

The main folder contains:



MePhy Dataset folder, which contains the dataset;

ReadMe.pdf, which provides more informations about the dataset;

Mental Fatigue Inducing Test folder, which includes the HTML application used to simulate mental fatigue in the test participants. 


 

A more in depth description of the MePhy dataset can be found in the following paper https://doi.org/10.1145/3610977.3637485.

Marta Gabbi, Luca Cornia, Valeria Villani, and Lorenzo Sabattini (2024) Understanding Fatigue Through Biosignals: A Comprehensive Dataset. In Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction (HRI ’24).
DOI: 10.5281/zenodo.10875569
title: Spectral model fitting for all 4XMM-DR11 for all sources

description: Fitting products for all sources: this deliverable includes fits with a simple model (absorbed power law) to the pipeline count rates from emldetect to all detections in the 4XMM DR11 catalogue.
DOI: 10.5281/zenodo.10875570
title: Spectral model fitting for all 4XMM-DR11 for all sources

description: Fitting products for all sources: this deliverable includes fits with a simple model (absorbed power law) to the pipeline count rates from emldetect to all detections in the 4XMM DR11 catalogue.
DOI: 10.5281/zenodo.10017883
title: Cholangiocarcinoma Dataset
DOI: 10.5281/zenodo.10017884
title: Cholangiocarcinoma Dataset
DOI: 10.25345/c52v2cn12
title: MassIVE MSV000094405 - GNPS - Metabolomics of Bacillus subtilis interactions with SynCom
title: Dataset containing extracts from 5 membered synthetic communities containing various Bacillus subtilis mutants as well as monocultures.
DOI: 10.5281/zenodo.10837809
title: raw data from MS : Redefining the winter phenotype in Drosophila suzukii

description: raw data from MS "Redefining the winter phenotype in Drosophila suzukii"
DOI: 10.5281/zenodo.10837810
title: raw data from MS : Redefining the winter phenotype in Drosophila suzukii

description: raw data from MS "Redefining the winter phenotype in Drosophila suzukii"
DOI: 10.5281/zenodo.10478551
title: The choice of the right supporting electrolyte in electrochemical reductions: a principal component analysis

description: Introduction

This dataset contains the raw data as well as an HTML-based visualization of our dataset using Python Bokeh. We have also added a feature to highlight commercially available supporting electrolytes. The data is taken from the PubChem database. For each of the 6650 cations, the known neutral compounds in the PubChem dataset were identified with their corresponding anions. For each of these compounds, the vendor information stored in PubChem was queried.

Directory structure



Raw Data



[raw_data.tar.xz] Compressed directory with the output from the automated feature calculation.

[raw_data.csv] CSV file with the values of the calculated properties of all cations.



Visualization



[pca_qac_tool.html] HTML page with Javascript to display PC1 and PC2.



Tools



[pca_qac_tool.py] Python script to generate the HTML output using Bokeh. Depends on the data_pca_qyc.csv and the data_commercial.json file.

[PubChem_get_Vendor_information.py] Crawler that checks a list of PubChem CIDs for net-neutral compounds and whether they are commercially available.
DOI: 10.5281/zenodo.10875655
title: Source classification for OM SUSS 5.0

description: Since the launch of the XMM in 1999 OM images in the six filters have been obtained which are used to construct the SUSS catalogue and the Epoch of observation in the catalogue spans therefore close to 24 years by now. With a positional accuracy of 0.5" some nearby stars show proper motion and it is useful to compute the Epoch 2000 positions for those. The positions are otherwise reported on the (J2000) ICRS system.The SUSS catalogue has two parts. The first table, called SUMMARY contains the information for each observation. For each observation this table contains the start and end date of the observation, the pointing direction, the exposure time for each of the OM filters and some other parameters. The second table lists all the SOURCES found in all of the images, the magnitudes in different filters, magnitude errors, significance, source extent, quality flags, and a link to the information in the SUMMARY. The epoch of observation can be derived from the start and end time of the observation in the SUMMARY table. Each row in the SOURCES table can be matched to the observation in the SUMMARY and thus be given the epoch of observation. Once the epoch for an observation is known, the observation can be matched to the Gaia DR3 list of high propermotion objects transformed to the time of the SUSS observation. In order to do this, the following procedure was used: (1) the sources with proper motions larger than 25 mas/yr and absolute PM/error larger than 10 were extracted from the Gaia DR3 catalogue. (2) for each SUSS observation the Gaia DR3 sources within 15 arcminutes of the center of that field were selected. (3) the selected Gaia DR3 were transformed to the epoch of the SUSS observation. (4) the SUSS sources were then matched using XArches[2] to those equal-epoch sources from Gaia-DR3. XArches also provides a probability that of a good match between a SUSS and Gaia DR3 source. Only sources with a probability of good match greater than 0.5 were retained. (5) the Epoch 2000 positions were computed for the matches. (6) after all SUSS observations were processed, the positions of sources which had not been identified as having a high PM were added to the Epoch 2000 positions, so that for all SUSS sources Epoch 2000 position were defined. For clarification, all SUSS positions were J2000, even if at the observed Epoch.
DOI: 10.5281/zenodo.10875656
title: Source classification for OM SUSS 5.0

description: Since the launch of the XMM in 1999 OM images in the six filters have been obtained which are used to construct the SUSS catalogue and the Epoch of observation in the catalogue spans therefore close to 24 years by now. With a positional accuracy of 0.5" some nearby stars show proper motion and it is useful to compute the Epoch 2000 positions for those. The positions are otherwise reported on the (J2000) ICRS system.The SUSS catalogue has two parts. The first table, called SUMMARY contains the information for each observation. For each observation this table contains the start and end date of the observation, the pointing direction, the exposure time for each of the OM filters and some other parameters. The second table lists all the SOURCES found in all of the images, the magnitudes in different filters, magnitude errors, significance, source extent, quality flags, and a link to the information in the SUMMARY. The epoch of observation can be derived from the start and end time of the observation in the SUMMARY table. Each row in the SOURCES table can be matched to the observation in the SUMMARY and thus be given the epoch of observation. Once the epoch for an observation is known, the observation can be matched to the Gaia DR3 list of high propermotion objects transformed to the time of the SUSS observation. In order to do this, the following procedure was used: (1) the sources with proper motions larger than 25 mas/yr and absolute PM/error larger than 10 were extracted from the Gaia DR3 catalogue. (2) for each SUSS observation the Gaia DR3 sources within 15 arcminutes of the center of that field were selected. (3) the selected Gaia DR3 were transformed to the epoch of the SUSS observation. (4) the SUSS sources were then matched using XArches[2] to those equal-epoch sources from Gaia-DR3. XArches also provides a probability that of a good match between a SUSS and Gaia DR3 source. Only sources with a probability of good match greater than 0.5 were retained. (5) the Epoch 2000 positions were computed for the matches. (6) after all SUSS observations were processed, the positions of sources which had not been identified as having a high PM were added to the Epoch 2000 positions, so that for all SUSS sources Epoch 2000 position were defined. For clarification, all SUSS positions were J2000, even if at the observed Epoch.
DOI: 10.34658/rdb.ff0gm8/lr2lwh
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title: uxnmr.info

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title: prosol_History

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title: pulseprogram

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DOI: 10.5281/zenodo.10875593
title: Three-dimensional water exchanges in the shelf circulation system of the Northern South China Sea under climatic modulation from ENSO

description: Three-dimensional water exchanges in the shelf circulation system of the Northern South China Sea under climatic modulation from ENSO
DOI: 10.5281/zenodo.10875594
title: Three-dimensional water exchanges in the shelf circulation system of the Northern South China Sea under climatic modulation from ENSO

description: Three-dimensional water exchanges in the shelf circulation system of the Northern South China Sea under climatic modulation from ENSO
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