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11,879 results for “participant”
Resting state with closed eyes for patients with depression and healthy participants
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MRI data of 40 adult participants in response to a cue induced craving task following food fasting, social isolation and baseline (within-subject design)
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[Dataset] Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects - Raw Data
<p><strong>Explanation/Overview:</strong></p> <p>Corresponding raw data for the analyses described in D3.3 (can be found here), which are the result of our research that culminated into the publication "Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects", a conference paper for the conference CollabTech 2022: <a href="https://link.springer.com/book/10.1007/978-3-031-20218-6">Collaboration Technologies and Social Computing</a> and published as part of the <a href="https://link.springer.com/bookseries/558">Lecture Notes in Computer Science</a> book series (LNCS,volume 13632) <a href="https://link.springer.com/chapter/10.1007/978-3-031-20218-6_5">here</a>. Usernames have been anonymised.</p> <p>The raw data is in the <code>.json</code> format and can be read by most languages/tools. It is recommended to import the data into a MongoDB to work with it.</p> <p><strong>Purpose:</strong></p> <p>The purpose of this dataset is to provide the basis for possible further examinations, involving additional (not yet analysed) features such as the content of the comments etc. and also new ways of extracting networks.</p> <p><strong>Relatedness:</strong></p> <p>The data of the different projects was derived from the forums of 7 Zooniverse projects based on similar discussion board features. The projects are: 'Galaxy Zoo', 'Gravity Spy', 'Seabirdwatch', 'Snapshot Wisconsin', 'Wildwatch Kenya', 'Galaxy Nurseries', 'Penguin Watch'.</p> <p><strong>Content:</strong></p> <p>The dataset contains three files:</p> <ul> <li><code>Comments.json</code> <ul> <li>contains the basic data representation with multiple fields (e.g., <code>time_created</code>, <code>user_login</code>). Each data field represents a comment.</li> </ul> </li> <li><code>Discussions.json</code> <ul> <li><code></code>contains all discussions. Each data field is a discussion, with multiple fields (e.g., <code>comments_count</code>, <code>user_login</code>)</li> </ul> </li> <li><code>Projects.json</code> <ul> <li><code></code>contains all projects. Each data field is a project, with multiple fields (e.g., <code>project_id</code>, <code>description</code>)</li> </ul> </li> </ul> <p><strong>Grouping:</strong></p> <p>The projects (and thus the corresponding discussions and comments) were collected on the basis of common forum features such as the discussion boards.</p>
Wearable data and self reported fatigue scores from a remote observational study in Sjogren's disease, SLE and healthy participants
<p>Fatigue is a subjective, complex, and multi-faceted phenomenon, commonly experienced as tiredness. However, pathological fatigue is a major debilitating symptom associated with overwhelming feelings of physical and mental exhaustion. To date, there is no consensus about reliable quantitative assessments of fatigue.</p> <p>We collected observational data for a period of one month from 296 participants (healthy volunteers, Sjogren’s Syndrome, and Systemic Lupus Erythematosus patients) in the United States. Data comprised continuous multimodal digital data from Fitbit, including heart rate, physical activity, and sleep daily features, and app-based daily and weekly questions (e.g., pain, mood, general physical activity, and fatigue). When matching both sensor data and PROs, and excluding missing data, the dataset contains data from 183 subjects and 3950 recording days.</p> <p>The analysis of the association of digital data to self-reported fatigue was published at <em><strong>Rao C., et. al. (2023), Association of digital measures and self-reported fatigue: a remote observational study in healthy participants and participants with chronic inflammatory rheumatic disease, Frontiers in Digital Health</strong></em>.</p> <p>Demographics, digital parameters, and other information on this dataset can be found in the aforementioned manuscript and related supplementary material. Details on the data files can be found under README.txt.</p>
Production line dataset for task scheduling and energy optimization - Demand Response Participation
<p>Using the previous dataset at <<a href="https://zenodo.org/record/4106746">https://zenodo.org/record/4106746</a>> it was simulated an announcement of a demand response program at period 757, describing a demand response event from period 937 (Friday at 21:00h) to 960 (Friday at 23:00h) , where each period represents five minutes. The demand response program imposed a limit consumption, during its event, of 2.5 kWh. The announcement of the demand response allowed the use of the proposed solution to limit the energy consumption. For that, the algorithm described in section 3.3 was executed at period 769 (Friday at 7:00h).</p> <p>The API can be found at <<a href="http://www.gecad.isep.ipp.pt/api/spear/%3E">http://www.gecad.isep.ipp.pt/api/spear/</a>></p> <p>File Description:</p> <ul> <li>Input_JSON_Demand_Response_Optimization - JSON input data for the demand response participation</li> <li>Output_JSON_Demand_Response_Optimization - JSON output data for the demand response participation</li> <li>Output_Statistics_Demand_Response_Optimization - Excel output demand response participation statistics</li> <li>Comparison_Output_Statistics_Demand_Response - Excel output statistics comparing the before and after the demand response participation</li> </ul>
Database on the History of Participation and Engagement
<p>The dataset presents a timeline and database of sustainability-related, innovative, and inclusive participation and engagement practices in Europe since the early days of digitalisation. The data reported here was assembled from Democratic Innovations collected from publicly accessible sources (Partecipedia, OECD's database of Representative Deliberate Processes and Institutions, Knowledge Network on Climate Assemblies (KNOCA), G1000, and the International Observatory on Participatory Democracy), with a specific focus on their contribution to social and environmental sustainability. By mapping the dynamics of implementation of Democratic Innovations across Europe, the dataset provides valuable insights into the potential for these innovations to foster more inclusive and resilient societies in line with social and environmental development goals. Each of the cases collected here was categorised on specific criteria, like geographical distribution, scales of governance, policy areas, citizen involvement, and digitalization.<br><br>INCITE-DEM is funded by the European Union (INCITE-DEM, GA nº 101094258). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or of the European Research Executive Agency (REA). Neither the European Union nor the granting authority (REA) can be held responsible for them</p>
Survey Study about Motivation for Participants in Citizen Science Projects
<p>The survey study about motivation for participants in Citizen Science projects was designed within the ongoing H2020 project named <a href="https://actionproject.eu/">ACTION</a> (pArticipatory sCience Toolkit agaInst pollutiON). Volunteers participate to citizen science initiatives for multiple reasons: personal enjoyment, desire for improvement or achievement, establishment of personal relationships, care for the environment, etc.<br> Studying motivation and investigating the factors influencing people participation to citizen science projects is an essential aspect in the analysis of citizen science communities. Understanding the reasons that foster people to engage can support the successful design and implementation of effective participant involvement tasks, as well as pave the way for long-term engagement.<br> The goal of the study is to analyse the motivation to participate of a specific citizen science community and the structure of the survey proposed shuold be customised considering the topic of the activities.</p> <p><br> This research object describes the studies performed within the ACTION project to investigate motivations of different citizen science communities: the TESS Network (<a href="https://tess.stars4all.eu/">https://tess.stars4all.eu/</a>); the 6 ACTION pilots (<a href="https://actionproject.eu/citizen-science-pilots">https://actionproject.eu/citizen-science-pilots</a>) Mapping Mobility, Open Soil Atlas, Water Sentinels, Restart Data Workbench, Wow Nature, Walk Up Aniene.<br> The surveys were designed and administered through Coney (<a href="https://coney.cefriel.com">https://coney.cefriel.com</a>) and made available as linked data exploiting the Survey Ontology (<a href="https://w3id.org/survey-ontology">https://w3id.org/survey-ontology</a>).</p> <p>The research object adopts the <a href="https://www.researchobject.org/ro-crate/1.0/">RO-Crate</a> specification. Files made available within the research object are:</p> <ul> <li><em>*-procedure.ttl</em> contains the RDF representation of the <strong>template</strong> structure of the conversational survey (questions, answers, etc.) using the <a href="https://w3id.org/survey-ontology">Survey Ontology</a></li> <li>*-<em>mean-var-motivating-questions.csv </em>contains the computed mean and average for each question considered (observable variables) comparing all the surveys performed</li> <li>*-<em>mean-var-motivating-factor.csv </em>contains the computed mean and average for each motivation factor considered (latent variables) comparing all the surveys performed</li> <li>*-<em>correlation-factors-global-motivation.csv </em>contains the correlation analysis between each motivation factor and the global motivation comparing all the surveys performed</li> </ul> <p>The Research Object also references all the RO-Crates describing the different survey motivation studies in details.</p>
Survey of participant experience in workshop for testing IGP software setup: supplemental dataset for SimAUD 2018
<p>This is a supplemental dataset for a SimAUD 2018 paper. For the context of the dataset, plots, and description text given here, please refer to the paper:</p> <blockquote> <p><strong>Heinrich, M.K., Zahadat, P., Harding, J., et al. Using interactive evolution to design behaviors for non-deterministic self-organized construction. In <em>Proc. of SimAUD</em> (2018). <em>In print</em>.</strong></p> </blockquote> <p>These survey results <em><strong>(see attached file dataset_survey-responses)</strong></em>, are regarding the experience of participants in a workshop testing the <em>Integrated Growth Projection</em> software setup, including an implementation of the <em>Vascular Morphogenesis Controller</em>, and the Interactive Evolution software <em>Biomorpher</em>.</p> <p>The full-time one-week workshop was held as part of the normal coursework of the Master's degree program <em>CITAstudio: Computation in Architecture</em>, in the Institute of Architecture and Technology, at [KADK] The Royal Danish Academy, School of Architecture, Copenhagen, Denmark. It was part of the first semester of the 2017-2018 school year. Workshop participants were current Master's students in the <em>CITAstudio </em>program. The workshop teaching was led by Mary Katherine Heinrich and Phil Ayres, with guest teaching by Payam Zahadat and John Harding, overall program teaching supervision by Paul Nicholas, and teaching assistance by Sebastian Gatz.</p> <p><strong>Survey method:</strong></p> <p>The workshop participants gave survey responses anonymously.</p> <p>Survey responses were collected via Google Forms (https://www.google.com/forms/about/). At the start of the survey, participants gave permissions for use and publication, and verified that they participated in the workshop and had not previously taken the survey. The platform discourages duplicate responses by requiring an email sign-in (which is not visible to the surveyor).</p> <p>Although workshop participants gave permission for survey results to be published before taking the survey, the participants were unaware of the specific intended context and purpose of publishing, prior to taking the survey. Authors of the related paper who were workshop participants had no contact with the process of survey preparation, analysis of its results, or writing of related paper sections. </p> <p>There were 26 workshop participants. Participants were architects or architectural designers. </p> <p>Participants were asked about 1) their prior experience, 2) their understanding of topics before and after the workshop, 3) the helpfulness of specific software aspects for their understanding and their project work, and 4) their likelihood to use specific software aspects in the future.</p> <p>In addition to looking at the full surveyed group, we compare experience sub-groups. Participants select relevant tasks that they have previously completed, from a provided list. They are placed in the <em>Less Experience</em> sub-group if they select one or no tasks, and in the <em>More Experience</em> sub-group if they select two or more. </p> <p><strong>Survey results:</strong></p> <p>Close to two-thirds of workshop participants submitted survey responses (16 of 26, or 61.5%), with at least two respondents per group. One respondent indicated workshop absence; their responses were removed. One respondent indicated that they did not understand two questions, so those two responses were removed. All responses were submitted within 18 days of workshop end.</p> <p>Attached file:<em><strong> Plot_1</strong></em>, caption:</p> <blockquote> <p>Plot 1: <em>Participants' scoring of their understanding of the topics "self-organization" and "Interactive Evolution" respectively, comparing scores before and after the workshop.</em></p> </blockquote> <p>Attached file:<em><strong> Plot_2</strong></em>, caption:</p> <blockquote> <p>Plot 2: <em>(Left) Participants' scoring of their likelihood to use certain aspects of the software setup again, if they were to design a non-deterministic self-organizing behavior, and (right) participants' indications of the helpfulness of those same software aspects.</em></p> </blockquote> <p>Responses regarding understanding <em><strong>(see attached file, Plot_1)</strong></em> give evidence that the <em>Integrated Growth Projection</em> software setup helped participants of both experience levels improve their understanding of related topics. Those with less prior experience improved their understanding more than others, and understanding of "Interactive Evolution" improved slightly more than understanding of "self-organization." </p> <p>Responses regarding the usefulness of certain software aspects <em><strong>(see attached file, Plot_2)</strong></em> give evidence that: 1) Interactive Evolution helped participants to understand and design a non-deterministic self-organizing behavior <em><strong>(see Plot_2, a)</strong></em>; 2) visualization of the environment and simultaneous viewing of multiple results helped them to understand and design such behaviors <em><strong>(see Plot_2, b and c)</strong></em>; and 3) the <em>Integrated Growth Projection</em>'s features of environment visualization and simultaneous results <em>inside</em> the artificial selection preview windows of the IE setup helped them to evolve behaviors to solve their chosen tasks<em> <strong>(see Plot_2, d and e)</strong></em>. </p> <p>____________________________________</p> <p>The research work involved here is part of EU project<em> flora robotica</em>.<br> <a href="http://www.florarobotica.eu/">http://www.florarobotica.eu/</a><br> Project<em> flora robotica</em> has received funding from the European Union's Horizon 2020 research and innovation program under the FET grant agreement, no. 640959.</p>
Types, open citations, closed citations, publishers, and participation reports of Crossref entities
<p>This publication contains several datasets that have been used in the paper "Crowdsourcing open citations with CROCI – An analysis of the current status of open citations, and a proposal" submitted to the <a href="https://www.issi2019.org/">17th International Conference on Scientometrics and Bibliometrics (ISSI 2019)</a>, available at <a href="https://opencitations.wordpress.com/2019/02/07/crowdsourcing-open-citations-with-croci/">https://opencitations.wordpress.com/2019/02/07/crowdsourcing-open-citations-with-croci/</a>.</p> <p>Additional information about the analyses described in the paper, including the code and the data we have used to compute all the figures, is available as a Jupyter notebook at <a href="https://github.com/sosgang/pushing-open-citations-issi2019/blob/master/script/croci_nb.ipynb">https://github.com/sosgang/pushing-open-citations-issi2019/blob/master/script/croci_nb.ipynb</a>. The datasets contain the following information.</p> <p><strong>non_open.zip:</strong> it is a zipped (~5 GB unzipped) CSV file containing the numbers of open citations and closed citations received by the entities in the Crossref dump used in our computation, dated October 2018. All the entity types retrieved from Crossref were aligned to one of following five categories: journal, book, proceedings, dataset, other. The open CC0 citation data we used came from the CSV dump of <a href="https://doi.org/10.6084/m9.figshare.6741422.v3">most recent release of COCI dated 12 November 2018</a>. The number of closed citations was calculated by subtracting the number of open citations to each entity available within COCI from the value “is-referenced-by-count” available in the Crossref metadata for that particular cited entity, which reports all the DOI-to-DOI citation links that point to the cited entity from within the whole Crossref database (including those present in the Crossref ‘closed’ dataset).</p> <p>The columns of the CSV file are the following ones:</p> <ul> <li><em>doi:</em> the DOI of the publication in Crossref;</li> <li><em>type:</em> the type of the publication as indicated in Crossref;</li> <li><em>cited_by:</em> the number of open citations received by the publication according to COCI;</li> <li><em>non_open:</em> the number of closed citations received by the publication according to Crossref + COCI.</li> </ul> <p><strong>croci_types.csv:</strong> it is a CSV file that contains the numbers of open citations and closed citations received by the entities in the Crossref dump used in our computation, as collected in the previous CSV file, alligned in five classes depening on the entity types retrieved from Crossref: <em>journal</em> (Crossref types: journal-article, journal-issue, journal-volume, journal), <em>book</em> (Crossref types: book, book-chapter, book-section, monograph, book track, book-part, book-set, reference-book, dissertation, book series, edited book), <em>proceedings</em> (Crossref types: proceedings-article, proceedings, proceedings-series), <em>dataset</em> (Crossref types: dataset), <em>other</em> (Crossref types: other, report, peer review, reference-entry, component, report-series, standard, posted-content, standard-series).</p> <p>The columns of the CSV file are the following ones:</p> <ul> <li><em>type:</em> the type publication between "journal", "book", "proceedings", "dataset", "other";</li> <li><em>label:</em> the label assigned to the type for visualisation purposes;</li> <li><em>coci_open_cit</em>: the number of open citations received by the publication type according to COCI;</li> <li><em>crossref_close_cit:</em> the number of closed citations received by the publication according to Crossref + COCI.</li> </ul> <p><strong>publishers_cits.csv:</strong> it is a CSV file that contains the top twenty publishers that received the greatest number of open citations. The columns of the CSV file are the following ones:</p> <ul> <li><em>publisher</em>: the name of the publisher;</li> <li><em>doi_prefix</em>: the list of DOI prefixes used assigned by the publisher;</li> <li><em>coci_open_cit</em>: the number of open citations received by the publications of the publisher according to COCI;</li> <li><em>crossref_close_cit</em>: the number of closed citations received by the publications of the publishers according to Crossref + COCI;</li> <li><em>total_cit</em>: the total number of citations received by the publications of the publisher (= <em>coci_open_cit</em> + <em>crossref_close_cit</em>).</li> </ul> <p><strong>20publishers_cr.csv: </strong>it is a CSV file that contains the numbers of the contributions to open citations made by the twenty publishers introduced in the previous CSV file as of 24 January 2018, according to the data available through the Crossref API. The counts listed in this file refers to the number of publications for which each publisher has submitted metadata to Crossref that include the publication’s reference list. The categories 'closed', 'limited' and 'open' refer to publications for which the reference lists are not visible to anyone outside the Crossref Cited-by membership, are visible only to them and to Crossref Metadata Plus members, or are visible to all, respectively. In addition, the file also record the total number of publications for which the publisher has submitted metadata to Crossref, whether or not those metadata include the reference lists of those publications.</p> <p>The columns of the CSV file are the following ones:</p> <ul> <li><em>publisher: </em>the name of the publisher;</li> <li><em>open: </em>the number of publications in Crossref with an 'open' visibility for their reference lists;</li> <li><em>limited: </em>the number of publications in Crossref with an 'limited' visibility for their reference lists;</li> <li><em>closed: </em>the number of publications in Crossref with an 'closed' visibility for their reference lists;</li> <li><em>overall_deposited:</em> the overall number of publications for which the publisher has submitted metadata to Crossref.</li> </ul>
Backpain exercise therapy remodels human epigenetic profiles in buccal and human peripheral blood mononuclear cells: An exploratory study in young male participants
<pre><strong>###### Files description #####</strong><br> <strong>Notes</strong>. 1) "BT" refers to before therapy and "AT" to after therapy. 2) 0 refers to FALSE and 1 to TRUE for binary variables. The provided files have tab-separated columns except the .RDS which is and R output of the mixOmics DIABLO integration analysis. <strong># Questionnaire</strong> > participants_categories.tsv: per participant (rows), output of the clustering with the participant ("ID") category ("category") per class<br> ("class") > questionnaire_agility_metrics.tsv: questionnaire and agility metrics per participant (rows) for the participants ("ID") with at least one paired AT+BT data in one type of biological sample (indicated in the columns "swab", "PBMC", and "plasma") <strong># PTMs</strong> Samples´ names are encoded as PBMC_AT_8_batch1, i.e. cells origin_time upon therapy_ID_batch (we removed _batch column suffix for the <br>processed files). NA indicates an undetected intensity. > raw_PBMC_light_labelled_intensities.tsv: raw intensity of light/endogenous peptides (row) by precursor per sample (column) from PBMC > raw_swab_light_labelled_intensities.tsv: idem from buccal cells > raw_PBMC_heavy_labelled_intensities.tsv: raw intensity of light/endogenous peptides (row) by precursor per sample (column) from PBMC > raw_swab_heavy_labelled_intensities.tsv: idem from buccal cells > raw_PBMC_heavynormalized_intensities.tsv: raw intensity of light peptides normalized by heavy peptides intensity (row) by precursor per <br>sample (column) > raw_swab_heavynormalized_labelled_intensities.tsv: idem from buccal cells > processed_cleaned_PBMC_log2intensities.tsv: processed (heavy normalized, imputed, batch-corrected) intensity of peptides aggregated by modification (PTM, row) by precursor per sample (column) after log2-transformation. The relative abundances are computed from this file. Rows without me/ac suffix represents the amount of unmodified peptide for the considered site. > processed_cleaned_swab_log2intensities.tsv: idem from buccal cells > rel_abundance_PTM_PBMC.tsv: relative abundance computed per precursor, e.g. for a given sample, the H3_K4+H3_K4me1+H3_K4me2+H3_K4me3 <br>relative abundance values must sum to 100, with the relative abundance of H3_K4 representing the absence of modified K4. > rel_abundance_PTM_swab.tsv: idem from buccal cells > tests_from_rel_abundance_PTM_swab_PBMC.tsv: per type of samples ("Sample.origin", i.e.swab of PBMC) and per PTM (rows, "PTM"), report <br>the output of classic (p-values, adjusted with Benjamini-Hochberg (BH), or Benjamini-Yekutieli procedure (BY), from raw and arcsin square <br>root transformed percentage) and PLS-DA tests (VIP - Variable Importance score - and its 95% confidence interval). The percentage of change<br>of each PTM after therapy relative tobefore therapy is reported in "perc_change.AT.over.BT" column. The "is_candidate" indicates if the PTM has been considered as a hit in the swab or PBMC. <strong># Plasma</strong> Samples´ names are encoded as PLASMA_AT_8_batch1, i.e. cells origin_time upon therapy_ID_batch. NA indicates an undetected intensity. > raw_plasma_maxquant_log2ibaq_intensities.tsv: raw data from protein group MaxQuant file. The iBAQ columns are used in later steps. > processed_cleaned_plasma_log2intensities.tsv: processed (imputed, batch-corrected) intensity of protein groups after log2-transformation. > tests_from_intens_plasma.tsv: per protein group ("Proteins.ID"), report the output of classic (p-values, adjusted Benjamini-Hochberg (BH),<br>or Benjamini-Yekutieli procedure (BY), from log2-transformed intensities) and PLS-DA tests (VIP and its 95% confidence interval). The log2 <br>fold change after therapy relative to before therapy is reported in "log2FC.AT.over.BT" column. The "is_candidate" indicates if the protein group has been considered as a hit. <strong># Integration</strong> > circos_input: output of DIABLO analysis with correlation threshold set to 0.7. Use the readRDS R function to open.</pre> <p> </p>
Placebo nasal spray protects female participants from experimentally induced sadness and concomitant changes in autonomic arousal (Open Data and Open Materials)
<p><strong>Open Data and Open Materials of: Placebo nasal spray protects female participants from experimentally induced sadness and concomitant changes in autonomic arousal. <em>Journal of Affective Disorders</em>. </strong></p> <p><em>Background:</em> To investigate the powerful placebo effects in antidepressant drug trials and their mechanisms, recent pioneering experimental studies showed that expectation manipulation combined with an active placebo attenuated induced sadness. In the present study, we aimed at extending these findings by assessing the psychophysiological response in addition to mere self-report.</p> <p><em>Methods:</em> One hundred thirteen healthy female students were randomly assigned to a drug expectation group (active placebo, positive treatment expectation), placebo expectation group (active placebo, no treatment expectation), or a no-treatment group (no placebo, no treatment expectation). After placebo intake, sadness was induced by self-deprecating statements using the Velten method combined with sad music, including a rumination phase. Sadness was measured using the Positive and Negative Affect Schedule Expanded Form (PANAS-X). Heart rate and skin conductance were assessed continuously.</p> <p><em>Results:</em> After mood induction and after rumination, self-reported sadness was significantly lower, and skin conductance level was significantly higher, in the drug expectation group than in the no-treatment group. The mood induction was further accompanied by a heart rate deceleration within all groups.</p> <p><em>Limitations: </em>Generalizability is limited by sample selectivity and focusing on sadness as a symptom of depression, exclusively.</p> <p><em>Conclusion:</em> Expectation-induced placebo effects significantly influenced sadness-correlated changes in autonomic arousal, and not only subjectively reported sadness, indicating that placebo effects in the context of affect are not merely due to subjective response bias. The systematic modification of treatment expectation could be utilized in clinical practice to optimize current therapeutic approaches to improve mood regulation.</p>
Not So Weak-PICO: Leveraging weak supervision for Participants, Interventions, and Outcomes recognition for systematic review automation
<p>EBM-PICO is a widely used dataset with PICO annotations at two levels: span-level or coarse-grained and entity-level or fine-grained. Span-level annotations encompass the full information about each class. Entity-level annotations cover the more fine-grained information at the entity level, with PICO classes further divided into fine-grained subclasses. For example, the coarse-grained Participant span is further divided into participant age, gender, condition and sample size in the randomised controlled trial. This dataset comes pre-divided into a training set (n=4,933) annotated through crowd-sourcing and an expert annotated gold test set (n=191) for evaluation.</p> <p>The <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6174533/bin/NIHMS988059-supplement-Appendix.pdf">EBM-PICO annotation guidelines</a> caution about variable annotation quality. <a href="http://ceur-ws.org/Vol-2429/paper1.pdf">Abaho et al.</a> developed a framework to post-hoc correct EBM-PICO outcomes annotation inconsistencies. <a href="https://arxiv.org/pdf/1904.09557.pdf">Lee et al.</a> studied annotation span disagreements suggesting variability across the annotators. Low annotation quality in the training dataset is excusable, but the errors in the test set can lead to faulty evaluation of the downstream ML methods. We evaluate 1% of the EBM-PICO training set tokens to gauge the possible reasons for the fine-grained labelling errors and use this exercise to conduct an error-focused PICO re-annotation for the EBM-PICO gold test set. The file 'test_ebm_correctedlabels.tsv' has error corrected EBM-PICO gold test set.</p> <p> </p> <p>The upload also contains two zip files containing labelling sources mentioned in the Distant-PICO paper. </p> <ol> <li>ds_cto_dict.zip: contains the four distant supervision dictionaries (P: participant.txt, I = intervention.txt, intervetion_syn.txt, O: outcome.txt) generated from clinicaltrials.gov using the methodology described in Distant-CTO. </li> <li>handcrafted_dictionaries.zip: contains three files <ul> <li>gender_sexuality.txt: contains a list of possible genders and sexual orientations found across the web. The list is not comprehensive.</li> <li>endpoints_dict.txt: contains outcome names and the names of questionnaires used to measure outcomes assembled from PROM questionnaires and PROMs.</li> <li>comparator_dict: contains a list of idiosyncratic comparator terms like a sham, saline, placebo, etc., compiled from the literature search. The list is not comprehensive.</li> </ul> </li> </ol>
[Dataset] Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects
<p><strong>Explanation/Overview:</strong></p> <p>Corresponding dataset for the analyses and results achieved in the CS Track project in the research line on participation analyses, which is also reported in the publication "Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects", a conference paper for the conference CollabTech 2022: <a href="https://link.springer.com/book/10.1007/978-3-031-20218-6">Collaboration Technologies and Social Computing</a> and published as part of the <a href="https://link.springer.com/bookseries/558">Lecture Notes in Computer Science</a> book series (LNCS,volume 13632) <a href="https://link.springer.com/chapter/10.1007/978-3-031-20218-6_5">here</a>. The usernames have been anonymised.</p> <p><strong>Purpose:</strong></p> <p>The purpose of this dataset is to provide the basis to reproduce the results reported in the associated deliverable, and in the above-mentioned publication. As such, it <strong>does not</strong> represent <strong>raw data</strong>, but rather files that already include certain analysis steps (like calculated degrees or other SNA-related measures), ready for analysis, visualisation and interpretation with R.</p> <p><strong>Relatedness:</strong></p> <p>The data of the different projects was derived from the forums of 7 Zooniverse projects based on similar discussion board features. The projects are: 'Galaxy Zoo', 'Gravity Spy', 'Seabirdwatch', 'Snapshot Wisconsin', 'Wildwatch Kenya', 'Galaxy Nurseries', 'Penguin Watch'.</p> <p><strong>Content:</strong></p> <p>In this Zenodo entry, several files can be found. The structure is as follows (<code>files</code> and <strong>folders </strong>and<strong> </strong><em>descriptions</em>).</p> <ul> <li><code>corresponding_calculations.html</code> <ul> <li><em>Quarto-notebook to view in browser</em></li> </ul> </li> <li><code>corresponding_calculations.qmd</code> <ul> <li><em>Quarto-notebook to view in RStudio</em></li> </ul> </li> <li><strong>assets</strong> <ul> <li><strong>data</strong> <ul> <li><strong>annotations</strong> <ul> <li><code>annotations.csv</code> <ul> <li><em>List of annotations made per day for each of the analysed projects</em></li> </ul> </li> </ul> </li> <li><strong>comments</strong> <ul> <li><code>comments.csv </code> <ul> <li><em>Total list of comments with several data fields (i.e., comment id, text, reply_user_id)</em></li> </ul> </li> </ul> </li> <li><strong>rolechanges</strong> <ul> <li><code>478_rolechanges.csv</code> <ul> <li><em>List of roles per user to determine number of role changes </em></li> </ul> </li> <li><code>1104_rolechanges.csv</code> <ul> <li><em>...</em></li> </ul> </li> <li><code>...</code></li> </ul> </li> <li><strong>totalnetworkdata</strong> <ul> <li><strong>Edges</strong> <ul> <li><code>478_edges.csv</code> <ul> <li><em>Network data (edge set) for the given projects (without time slices)</em></li> </ul> </li> <li><code>1104_edges.csv</code> <ul> <li><em>...</em></li> </ul> </li> <li><code>...</code></li> </ul> </li> <li><strong>Nodes</strong> <ul> <li><code>478_nodes.csv</code> <ul> <li><em>Network data (node set) for the given projects (without time slices)</em></li> </ul> </li> <li><code>1104_nodes.csv</code> <ul> <li><em>...</em></li> </ul> </li> <li><code>...</code></li> </ul> </li> </ul> </li> <li><strong>trajectories</strong> <ul> <li><em>Network data (edge and node sets) for the given projects and all time slices (Q1 2016 - Q4 2021)</em></li> <li><strong>478</strong> <ul> <li><strong>Edges</strong> <ul> <li> <p><code>edges_4782016_q1.csv</code></p> </li> <li> <p><code>edges_4782016_q2.csv</code></p> </li> <li> <p><code>edges_4782016_q3.csv</code></p> </li> <li> <p><code>edges_4782016_q4.csv</code></p> </li> <li> <p><code>...</code></p> </li> </ul> </li> <li><strong>Nodes</strong> <ul> <li><code>nodes_4782016_q1.csv</code></li> <li> <p><code>nodes_4782016_q4.csv</code></p> </li> <li> <p><code>nodes_4782016_q3.csv</code></p> </li> <li> <p><code>nodes_4782016_q2.csv</code></p> </li> <li> <p><code>...</code></p> </li> </ul> </li> </ul> </li> <li> <p><strong>1104</strong> </p> <ul> <li> <p><strong>Edges</strong> </p> <ul> <li> <p><code>...</code></p> </li> </ul> </li> <li> <p><strong>Nodes</strong> </p> <ul> <li> <p><code>...</code></p> </li> </ul> </li> </ul> </li> <li> <p>...</p> </li> </ul> </li> </ul> </li> <li><strong>scripts</strong> <ul> <li><code>datavizfuncs.R</code> <ul> <li><em>script for the data visualisation functions, automatically executed from within </em><code>corresponding_calculations.qmd</code></li> </ul> </li> <li><code>import.R</code> <ul> <li><em>script for the import of data, automatically executed from within </em><code>corresponding_calculations.qmd</code></li> </ul> </li> </ul> </li> </ul> </li> <li><strong>corresponding_calculations_files</strong> <ul> <li>f<em>iles for the html/qmd view in the browser/RStudio</em></li> </ul> </li> </ul> <p><strong>Grouping:</strong></p> <p>The data is grouped according to given criteria (e.g., <code>project_title </code>or <code>time</code>). Accordingly, the respective files can be found in the data structure</p>
Internet use: participating in social networks [percentage of individuals] processed Eurostat data [CEEMID indicator]
<p>The indicator '<strong>Internet use: participating in social networks (creating user profile, posting messages or other contributions to facebook, twitter, etc.) [percentage of individuals]</strong>' from the Eurostat statistical product <em>Individuals who used the internet, frequency of use and activities.</em></p> <p>- NUTS2013 regional codes are recoded to NUTS2016<br> - missing data is handled with last observation carry forward, next observation carry back, linear interpolation<br> -NUTS2 areas are imputed when only NUTS1 level data is available. <br> <br> The original dataset is available here:<br> <a href="https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=isoc_r_iuse_i&lang=en">https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=isoc_r_iuse_i&lang=en</a></p> <p>More about CEEMID: <a href="http://ceemid.eu">www.ceemid.eu</a><br> Get in touch: <a href="http://danielantal.eu/#contact">danielantal.eu/#contact</a></p>
Household information for houses in Kasungu district participating in the Maladrone study, 2021
<p>Each row in the dataset contains information on each household participating in the Maladrone study, 2021. The information is as follows:</p><p>uniqueid: Unique ID assigned to the household, comprising of two letters corresponding to the community (ML, CK, CP) and the study house number.</p><p>under_5: Number of people under the age of 5 who live in the house at the time of asking.</p><p>over_5: Number of people over the age of 5 who live in the house at the time of asking.</p><p>under_5<i>_</i>rwt: Number of people under the age of 5 who usually sleep in the room where the CDC light trap was set.</p><p>over_5<i>_</i>rwt: Number of people over the age of 5 who usually sleep in the room where the CDC light trap was set.</p><p>number_mosquito<i>_</i>nets: Number of mosquito nets in the household.</p><p>mosquito_nets_rwt: Number of mosquito nets usually used in the room where the CDC light trap was set.</p><p>under_5<i>_</i>nets: Number of under 5s in the household who usually slept under a net.</p><p>over_5_nets: Numer of over 5s in the household who usually slept under a net.</p><p>unde_5_nets_rwt: Number of under 5s in the household who usually slept under a net in the room where the CDC light trap was set.</p><p>over_5_nets_rwt: Number of under 5s in the household who usually slept under a net in the room where the CDC light trap was set.</p><p>roof_type: Main material used for the roof of the house. Choices were iron sheet, thatched or tiled.</p><p>eaves: Whether the eaves of the house were open, partally open, or closed.</p><p>windows: Whether the windows of the house were open, partally open, or closed.</p>
Gene expression count matrix for 4 T cell subtypes from ROSMAP participants
<p><span>Peripheral blood mononuclear cells (PBMCs) from participants in the Rush Religious Orders Study/Memory and Aging Project (ROSMAP) were isolated by Ficoll gradient centrifugation, then sorted by high-speed flow cytometry into the following T cell subtypes:<span> </span>CD4+CD45RO-, CD4+CD45RO+, CD8+CD45RO-, and CD8+CD45RO+.<span> </span>Total RNA was extracted using buffer TCL (Qiagen), then RNA-seq libraries were prepared according to the Single Cell RNA Barcoding and Sequencing method originally developed for single-cell RNA-seq</span><span>, adapted for extracted total RNA.<span> </span>RNA libraries were collected on a single 384-well plate and sequenced on the Illumina HiSeq </span><span>using the High-throughput 3<span>’</span> Digital Gene Expression (DGE) library</span><span>.<span> The "RNA count matrix" file is the raw counts from the 384-well plate, while the "ROSMAP_Tcell_DGE_PlateMap" file contains metadata for the wells on the plate, by well position.</span></span></p>
Harmonized Cultural Access & Participation Dataset for Music
<p>Changes since the last version: in the .csv export there was a naming problem.</p> <p>- `visit_concert`: This is a standard CAP variables about visiting frequencies, in numeric form. <br> - `fct_visit_concert`: This is a standard CAP variables about visiting frequencies, in categorical form. <br> - `is_visit_concert`: binary variable, 0 if the person had not visited concerts in the previous 12 months.<br> - `artistic_activity_played_music`: A variable of the frequency of playing music as an amateur or professional practice, in some surveys we have only a binary variable (played in the last 12 months or not) in other we have frequencies. We will convert this into a binary variable. <br> - `fct_artistic_activity_played_music`: The `artistic_activity_played_music` in categorical representation.<br> - `artistic_activity_sung`: A variable of the frequency of singing as an amateur or professional practice, like played_muisc. Because of the liturgical use of singing, and the differences of religious practices among countries and gender, this is a significantly different variable from played_music.<br> - `fct_artistic_activity_sung`: The `artistic_activity_sung` variable in categorical representation.<br> - `age_exact`: The respondent’s age as an integer number. <br> - `country_code`: an ISO country code<br> - `geo`: an ISO code that separates Germany to the former East and West Germany, and the United Kingdom to Great Britain and Northern Ireland, and Cyprus to Cyprus and the Turiksh Cypriot community.[we may leave Turkish Cyprus out for practical reasons.]<br> - `age_education`: This is a harmonized education proxy. Because we work with the data of more than 30 countries, education levels are difficult to harmonize, and we use the Eurobarometer standard proxy, age of leaving education. It is a specially coded variable, and we will re-code them into two variables, `age_education` and `is_student`. <br> - `is_student`: is a dummy variable for the special coding in age_education for “still studying”, i.e. the person does not have yet a school leaving age. It would be tempting to impute `age` in this case to `age_education`, but we will show why this is not a good strategy.<br> - `w`, `w1`: Post-stratification weights for the 15+ years old population of each country. Use `w1` for averages of `geo` entities treating Northern Ireland, Great Britain, the United Kingdom, the former GDR, the former West Germany, and Germany as geographical areas. Use `w` when treating the United Kingdom and Germany as one territory.<br> - `wex`: Projected weight variable. For weighted average values, use `w`, `w1`, for projections on the population size, i.e., use with sums, use `wex`.<br> - `id`: The identifier of the original survey.<br> - `rowid``: A new unique identifier that is unique in all harmonized surveys, i.e., remains unique in the harmonized dataset.</p>
Can a knee sleeve influence ground reaction forces and knee joint power during a step-down hop in participants following ACL reconstruction? Discrete and time-continuous datasets
<p>Using a cross-over design, we estimated GRF and knee kinematics and kinetics during a step-down hop for 30 participants (age 26.1 [SD 6.7] years, 14 women) following ACL reconstruction (median 16 months post-surgery) with and without wearing a knee sleeve. In a subsequent randomised clinical trial, participants in the ‘Sleeve Group’ (n=9) then wore the sleeve for 6 weeks at least 1 hour daily, while a ‘Control Group’ (n=9) did not wear the sleeve. Statistical parametric mapping (SPM) was used to compare (1) GRF trajectories in the three planes as well as knee joint power between three conditions at baseline (uninjured side, unsleeved injured and sleeved injured side); (2) within-participant changes for GRF and knee joint power trajectories from baseline to follow-up between groups. We also compared discrete peak GRFs and power, rate of (vertical) force development, and mean knee joint power in the first 5% of stance phase. Time-continuous and discrete data are included in this dataset.</p>
Joint Optimization of Production and Maintenance for Cost-effective Manufacturing and Demand Response Participation Dataset - Machine Breakdown Event
<p>Using the previous dataset at <<a href="https://zenodo.org/record/4106746">https://zenodo.org/record/4106746</a>> an announcement of a machine breakdown event was simulated on Friday at 6:00, describing that machine MAQ119 could breakdown at any moment, detected using a predictive maintenance system. Accordingly, the proposed scheduler imposed a machine available frames constraint, during its event, of 0 usable frames, thus removing the machine from production. The proposed genetic algorithm was executed for 1 hour at period 769 (Friday at 7:00) until the remainder of the schedule’s time window. Also, the predefined optimization weights were 1 for total cost and 0 for machine occupancy deviation.</p> <p> </p> <p>File Description:</p> <ul> <li>Input_JSON_Machine_Breakdown_Optimization - JSON input data for the machine breakdown event</li> <li>Output_JSON_Machine_Breakdown_Optimization - JSON output data for the machine breakdown event</li> <li>Output_Statistics_Machine_Breakdown_Optimization - Excel output machine breakdown event statistics</li> </ul>
Joint Optimization of Production and Maintenance for Cost-effective Manufacturing and Demand Response Participation Dataset - Maintenance Optimization
<p>Using the previous dataset at <<a href="https://zenodo.org/record/4106746">https://zenodo.org/record/4106746</a>> a maintenance optimization scenario was formulated to validate the scheduler's ability to schedule tasks as well as maintenance activities while also minimizing the total costs. Accordingly, it was considered an optimization weight of 1 for the total cost and 0 for machine occupancy deviation, as well as a 2-hour execution time for the genetic algorithm. Each maintenance activity, for every machine, has a duration of 6 hours and 10 minutes, with a labor cost of 3,22 EUR/hour during the stipulated maintenance hours and a monetary penalty, that doubles the cost (i.e., 6.44 EUR/hour) if done out of maintenance hours.</p> <p> </p> <p>File Description:</p> <ul> <li>Input_JSON_Maintenance_Optimization - JSON input data for the maintenance optimization</li> <li>Output_JSON_Maintenance_Optimization - JSON output data for the maintenance optimization</li> <li>Output_Statistics_Maintenance_Optimization - Excel output maintenance optimization statistics</li> </ul>
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