Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
47,155
datasets available to search
ShareScore release 0.9.0
Dataset results
47,155 results for “evaluations”
The development and evaluation of an online application to assist in the extraction of data from graphs for use in systematic reviews (data repository)
<p>These are the data we generated in our evaluation of the graphical user interface.</p> <p>Please see our publication on Wellcome Open Research for information about the evaluations.</p>
A Layer-averaged Nonhydrostatic Dynamical Framework on an Unstructured Mesh for Global and Regional Atmospheric Modeling: Model Description, Baseline Evaluation and Sensitivity Exploration
<p>Selected model output data for supporting this paper.</p> <p>List of Files:</p> <p>2dtracer.tar.gz: correlated tracer test</p> <p>rh3d.tar.gz: 3D Rossby-Haurwitz Wave</p> <p>modon.tar.gz: Colliding Modons</p> <p>jwss.tar.gz: Jablonowski-Williamson Baroclinic Steady State</p> <p>jwbw_1d.tar.gz: 1D data output from Jablonowski-Williamson Baroclinic Wave</p> <p>jwbw_2d.tar.gz: 2D data output from Jablonowski-Williamson Baroclinic Wave</p> <p>dcmip31.tar.gz: DCMIP3-1 nonhydrostatic gravity wave</p> <p>Klemp15.tar.gz: Nonhydrostatic Mountain Waves in Klemp et al. 2015</p> <p>held-suarez.tar.gz: Held-Suarez dry climate (post-processed data for plotting, the raw daily data are too large to upload)</p> <p>jwbwvr.tar.gz: Variable-Resolution modeling of the Jablonowski-Williamson Baroclinic Wave</p> <p> </p> <p>see https://doi.org/10.5281/zenodo.3544795 for a companion work</p> <p>References:</p> <p>Zhang, Y., J. Li, R. Yu, S. Zhang, Z. Liu, J. Huang, and Y. Zhou, 2019: A Layer-Averaged Nonhydrostatic Dynamical Framework on an Unstructured Mesh for Global and Regional Atmospheric Modeling: Model Description, Baseline Evaluation, and Sensitivity Exploration. <em>Journal of Advances in Modeling Earth Systems</em>, <strong>11,</strong> 1685-1714.</p>
Colrobot Safety Sensor Evaluation
<p>Evaluation videos for the IFF safety sensor.</p> <p>The benchmark is divided into two scenarios. In the first one, people walk past the side of the van [scenario 1]. In the second scenario a person moves into the van [scenario 2]. The third video is a reference video to compare the process times.</p>
ASN 2016 evaluation with open data
<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://arxiv.org/abs/1902.03287">https://arxiv.org/abs/1902.03287</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 at <a href="https://github.com/sosgang/asn2016-issi2019">https://github.com/sosgang/asn2016-issi2019</a>. The datasets contain the following information.</p> <p><strong>[asncv | dblp | dblp_asncv]-data.csv:</strong> these CSV files contains all the final data used for the experiment with three conditions as described in the paper. The columns of the CSV file are the following ones (each row represents a particular candidate's application):</p> <ul> <li><em>level:</em> the professor level (1: Full Professor, 2: Associate Professor) to which the candidate wants to get the habilitation;</li> <li><em>session_date:</em> the date of the session for submitting the application;</li> <li><em>journal_number_open:</em> the number of journal article published by the candidate according to the open data available;</li> <li><em>citation_number_open:</em> the number of citations received by candidate's articles according to the open data available;</li> <li><em>h_index_open:</em> the h-index of the candidate according to the open data available;</li> <li><em>journal_number_real:</em> the number of journal article published by the candidate according to the official ASN 2016 data;</li> <li><em>citation_number_real:</em> the number of citations received by candidate's articles according to the official ASN 2016 data;</li> <li><em>h_index_real:</em> the h-index of the candidate according to the the official ASN 2016 data.</li> </ul> <p>All the other CSV files are intermediated data used to calculate the aforementioned one.</p>
RAGE - Data Evaluation HATCH pilot 2
<p><strong>General description: </strong>Data from the second Evaluation of the HATCH game - pilot round 2</p> <p><strong>Topic</strong><br> ACM CSS 2012: Human-centered computing - Human computer interaction (HCI) - User studies<br> PsycINFO Classification: 3530 Curriculum & Programs & Teaching Methods</p> <p><strong>Name entitites</strong><br> Organizational information: Hull College of Further Education, supported by Graz University of Technology<br> Geographical information: United Kingdom<br> Time information: 2018</p> <p><strong>Types</strong>: Excel</p> <p><strong>RAGCS target group:</strong>target groups - demand side - end users - adults - college</p> <p><strong>Evaluation dimensions</strong><br> Evaluation object: HATCH - The Creative Entrepreneur game<br> Methodology/design: Pre-post test design<br> Evaluation variables: entrepreneurial skills, usability, enjoyment, usefulness, flow</p> <p><strong>Instruments:</strong> Entrepreneurial Skills Questionnaire - ESQ; Game User Experience Satisfaction Scale - GUESS (Phan, Keebler, & Chaparro, 2016), subscales Usability/Playability and Enjoyment; Intrinsic Motivation Inventory - IMI (Ryan, 1982), subscale Value/Usefulness; Flow Short Scale - FSS (Rheinberg et al., 2003)</p> <p><strong>Knowledge/skill elements</strong><br> RAGCS skills: cognitive skills - understanding, remembering, applying, creating<br> ESCO skills: entrepreneurship (http://data.europa.eu/esco/skill/658605f2-1c95-49f0-bd98-0af7b15ad0b0); show entrepreneurial spirit (http://data.europa.eu/esco/skill/bdcf429c-5ccf-4c3d-bb61-4c987573a35e); business processes (http://data.europa.eu/esco/skill/9e84a506-df06-4be3-874a-fa01293e3dd5)<br> <br> <strong>Relationships</strong>: D8.4 Second RAGE Evaluation Report<br> Related dataset: <a href="https://doi.org/10.5281/zenodo.2577143">10.5281/zenodo.2577143</a></p>
RAGE pilot data from 2nd evaluation of the Sports Team Manager game on soft skills for employability
<p><strong>General description: </strong>The dataset includes data from the second evaluation pilot which tested the Sports team manager game developed by PlayGen for the Okkam use case.</p> <p><strong>Topic</strong><br> ACM CSS 2012: Human Computer Interaction (HCI) design and evaluation methods<br> PsycINFO Classification: 3620 Personnel Management & Selection & Training; 2228 Occupational & Employment Testing</p> <p><strong>Name entitites</strong><br> Organizational information: OKKAM, in collaboration with University of Trento<br> Geographical information: Italy<br> Time information: December 2017- November 2018</p> <p><strong>Types</strong>: Excel</p> <p><strong>RAGCS target group:</strong> end users: other user groups</p> <p><strong>Evaluation dimensions</strong><br> Evaluation object: Sports Team Manager game<br> Methodology/design: within subjects design for learning<br> Evaluation variables: usability, user experience, learning</p> <p><strong>Instruments:</strong> 1. Questionnaire on Usability Game User Experience Satisfaction Scale (GUESS; Phan, Keebler, & Chaparro, 2016) – Usability subscale; 2. questionnaire on User Experience including 3 subscales: Enjoyment (GUESS -Enjoyment subscale); Usefulness (Intrinsic Motivation Questionnaire, IMI; Ryan, 1982) - Subscale Value/Usefulness; Flow (Flow Short Scale, FSS, Rheinberg et al., 2003; Vollmeyer & Rheinberg, 2006); 3. Pre-post questionnaire on learning; 4. Focus interview</p> <p><strong>Knowledge/skill elements</strong><br> RAGCS skills: cognitive skills: evaluating, analysing; affective skills: interpersonal skills<br> ESCO skills: social interaction: <a href="http://data.europa.eu/esco/skill/8f18f987-33e2-4228-9efb-65de25d03330">http://data.europa.eu/esco/skill/8f18f987-33e2-4228-9efb-65de25d03330</a>; accept constructive criticism: <a href="http://data.europa.eu/esco/skill/a311ab20-75df-4aff-8016-3142c5659d30">http://data.europa.eu/esco/skill/a311ab20-75df-4aff-8016-3142c5659d30</a>; work in teams: <a href="http://data.europa.eu/esco/skill/60c78287-22eb-4103-9c8c-28deaa460da0">http://data.europa.eu/esco/skill/60c78287-22eb-4103-9c8c-28deaa460da0</a>; negotiate compromise: <a href="http://data.europa.eu/esco/skill/7954861c-86d4-4529-afbb-2c23dab9ac74">http://data.europa.eu/esco/skill/7954861c-86d4-4529-afbb-2c23dab9ac74</a>; lead others: <a href="http://data.europa.eu/esco/skill/75d8e5d9-bef3-418b-9011-01bff9f27207">http://data.europa.eu/esco/skill/75d8e5d9-bef3-418b-9011-01bff9f27207</a>; motivate others: <a href="http://data.europa.eu/esco/skill/e2d44a9b-f28c-489e-9861-b654b5ded507">http://data.europa.eu/esco/skill/e2d44a9b-f28c-489e-9861-b654b5ded507</a>; support colleagues: <a href="http://data.europa.eu/esco/skill/95a41cf5-4037-4c96-91a8-c34b41637224">http://data.europa.eu/esco/skill/95a41cf5-4037-4c96-91a8-c34b41637224</a>; manage time: <a href="http://data.europa.eu/esco/skill/d9013e0e-e937-43d5-ab71-0e917ee882b8">http://data.europa.eu/esco/skill/d9013e0e-e937-43d5-ab71-0e917ee882b8</a>; make decisions: <a href="http://data.europa.eu/esco/skill/d62d2b4c-a6f8-439e-8a1b-4f29ab5f2c47">http://data.europa.eu/esco/skill/d62d2b4c-a6f8-439e-8a1b-4f29ab5f2c47</a>; develop strategies to solve problems: <a href="http://data.europa.eu/esco/skill/7a8fb784-67fa-41e9-a75c-6b491d91f800">http://data.europa.eu/esco/skill/7a8fb784-67fa-41e9-a75c-6b491d91f800</a>; evaluate information: <a href="http://data.europa.eu/esco/skill/7dd94ad3-13d6-43fe-8b94-51fcbf67ced9">http://data.europa.eu/esco/skill/7dd94ad3-13d6-43fe-8b94-51fcbf67ced9</a><br> <br> <strong>Relationships</strong>: D8.4 Second RAGE Evaluation Report<br> Related dataset: <a href="https://doi.org/10.5281/zenodo.1209206">10.5281/zenodo.1209206</a></p>
RAGE - Data Evaluation Watercooler pilot 2
<p><strong>General description: </strong>Data from the second Evaluation of the Watercooler game - pilot round 2</p> <p><strong>Topic</strong><br> ACM CSS 2012: Human-centered computing - Human computer interaction (HCI) - User studies<br> PsycINFO Classification: 3530 Curriculum & Programs & Teaching Methods</p> <p><strong>Name entitites</strong><br> Organizational information: Hull College of Further Education, supported by Graz University of Technology<br> Geographical information: United Kingdom<br> Time information: 2018</p> <p><strong>Types</strong>: Excel</p> <p><strong>RAGCS target group:</strong>target groups - demand side - end users - adults - college</p> <p><strong>Evaluation dimensions</strong><br> Evaluation object: Watercooler game<br> Methodology/design: Pre-post test design<br> Evaluation variables: groupwork skills, usability, enjoyment, usefulness, flow</p> <p><strong>Instruments:</strong> Groupwork Skills Questionnari - GSQ (Cumming, Woodcock, Sooley, Holland & Burns, 2014); Game User Experience Satisfaction Scale - GUESS (Phan, Keebler, & Chaparro, 2016), subscales Usability/Playability and Enjoyment; Intrinsic Motivation Inventory - IMI (Ryan, 1982), subscale Value/Usefulness; Flow Short Scale - FSS (Rheinberg et al., 2003)</p> <p><strong>Knowledge/skill elements</strong><br> RAGCS skills: affective skills - interpersonal; affectiv skills - internalising values, responding to phenomena, receiving phenomena<br> ESCO skills: teamwork principles (http://data.europa.eu/esco/skill/a5b0cd5c-e13a-4ab3-8d93-4d242adcfb01I); communication principles (http://data.europa.eu/esco/skill/fe30a4b0-1a99-4fc7-afff-b162b8c834ba); plan teamwork (http://data.europa.eu/esco/skill/75e48971-3242-4ea5-a1ba-4deec48ad41c); work in teams (http://data.europa.eu/esco/skill/60c78287-22eb-4103-9c8c-28deaa460da0)<br> <br> <strong>Relationships</strong>: D8.4 Second RAGE Evaluation Report<br> Related dataset: <a href="https://doi.org/10.5281/zenodo.2577070">10.5281/zenodo.2577070</a></p>
Reproducibility Package for "Reproducible research and GIScience: an evaluation using AGILE conference papers"
<p>Data and code for analysis and plots used in the manuscript "Reproducible research and GIScience: an evaluation using AGILE conference papers": <a href="https://doi.org/10.7287/peerj.preprints.26561v1">https://doi.org/10.7287/peerj.preprints.26561v1</a></p> <p>The deposited archived includes a <a href="https://en.wikipedia.org/wiki/Docker_(software)">Dockerfile</a> and an <a href="http://rmarkdown.rstudio.com/">R Markdown</a> document suitable for use with <a href="http://mybinder.org/">Binder</a>: <a href="https://mybinder.org/v2/gh/nuest/reproducible-research-and-giscience/6">https://mybinder.org/v2/gh/nuest/reproducible-research-and-giscience/6</a></p> <p>The version tag of this repository matches the <a href="https://git-scm.com/book/en/v2/Git-Basics-Tagging">git tag</a> on the code repository at <a href="https://github.com/nuest/reproducible-research-and-giscience">https://github.com/nuest/reproducible-research-and-giscience</a>, except version <code>6-fixed</code> which matches the tag <code>6</code>.</p> <p> </p>
RAGE - Data Evaluation Watercooler pilot 1
<p><strong>General description: </strong>Data from the first Evaluation of the Watercooler game - pilot round 1</p> <p><strong>Topic</strong><br> ACM CSS 2012: Human-centered computing - Human computer interaction (HCI) - User studies<br> PsycINFO Classification: 3530 Curriculum & Programs & Teaching Methods</p> <p><strong>Name entitites</strong><br> Organizational information: Hull College of Further Education, supported by Graz University of Technology<br> Geographical information: United Kingdom<br> Time information: 2017</p> <p><strong>Types</strong>: Excel</p> <p><strong>RAGCS target group:</strong>target groups - demand side - end users - adults - college</p> <p><strong>Evaluation dimensions</strong><br> Evaluation object: Watercooler game<br> Methodology/design: Pre- and post-game questionnaire (no repeated measures)<br> Evaluation variables: gaming preferences and habits, attitude towards games, learning self-assessment, user experience</p> <p><strong>Instruments:</strong> questionnaire items for analysis of the participants’ gaming patterns and relating to their perception of applied games; self-reflection and subjective evaluation of the game’s effectiveness as a tool in teaching and learning</p> <p><strong>Knowledge/skill elements</strong><br> RAGCS skills: affective skills - interpersonal; affectiv skills - internalising values, responding to phenomena, receiving phenomena<br> ESCO skills: teamwork principles (http://data.europa.eu/esco/skill/a5b0cd5c-e13a-4ab3-8d93-4d242adcfb01I); communication principles (http://data.europa.eu/esco/skill/fe30a4b0-1a99-4fc7-afff-b162b8c834ba); plan teamwork (http://data.europa.eu/esco/skill/75e48971-3242-4ea5-a1ba-4deec48ad41c); work in teams (http://data.europa.eu/esco/skill/60c78287-22eb-4103-9c8c-28deaa460da0)<br> <br> <strong>Relationships</strong>: D8.3 First RAGE Evaluation Report<br> Related dataset: <a href="https://doi.org/10.5281/zenodo.2564931">10.5281/zenodo.2564931</a></p>
RAGE - Data Evaluation HATCH pilot 1
<p><strong>General description: </strong>Data from the first Evaluation of the HATCH game - pilot round 1</p> <p><strong>Topic</strong><br> ACM CSS 2012: Human-centered computing - Human computer interaction (HCI) - User studies<br> PsycINFO Classification: 3530 Curriculum & Programs & Teaching Methods</p> <p><strong>Name entitites</strong><br> Organizational information: Hull College of Further Education, supported by Graz University of Technology<br> Geographical information: United Kingdom<br> Time information: 2017</p> <p><strong>Types</strong>: Excel</p> <p><strong>RAGCS target group:</strong> target groups - demand side - end users - adults - college</p> <p><strong>Evaluation dimensions</strong><br> Evaluation object: HATCH - The Creative Entrepreneur game<br> Methodology/design: Pre-post test design<br> Evaluation variables: gaming preferences and habits, attitude towards games, learning self-assessment, user experience</p> <p><strong>Instruments:</strong> questionnaire items for analysis of the participants’ gaming patterns and relating to their perception of applied games; self-reflection and subjective evaluation of the game’s effectiveness as a tool in teaching and learning</p> <p><strong>Knowledge/skill elements</strong><br> RAGCS skills: cognitive skills - understanding, remembering, applying, creating<br> ESCO skills: entrepreneurship (http://data.europa.eu/esco/skill/658605f2-1c95-49f0-bd98-0af7b15ad0b0); show entrepreneurial spirit (http://data.europa.eu/esco/skill/bdcf429c-5ccf-4c3d-bb61-4c987573a35e); business processes (http://data.europa.eu/esco/skill/9e84a506-df06-4be3-874a-fa01293e3dd5)<br> <br> <strong>Relationships</strong>: D8.3 First RAGE Evaluation Report<br> Related dataset: <a href="https://doi.org/10.5281/zenodo.2564917">10.5281/zenodo.2564917</a></p>
Critical droplet properties for particles containing 6 pollenkitts evaluated with 4 Köhler models for the study "Cloud condensation nuclei activity of six pollenkitts and the influence of their surface activity" by Prisle et al. (2019)
<p>Critical droplet supersaturation and surface tension, calculated with 3 thermodynamic Köhler models presented in Prisle et al., <em>Surfactants in cloud droplet activation: mixed organic-inorganic particles</em>, Atmospheric Chemistry and Physics, 10, 5663-5683, doi:10.5194/acp-10-5663-2010, and with the model presented in Prisle et al., <em>A simple representation of surface active organic aerosol in cloud droplet formation</em>, Atmospheric Chemistry and Physics, 11, 4073-4083, doi: 10.5194/acp-11-4073-2011, for particles of varying size comprising 6 pollenkitts and mixtures with ammonium sulfate.</p>
RAGE Summative Ecosystem Evaluation
<p><strong>General description: </strong>Data from the system evaluation of the Social Network Mediator integration into the RAGE Ecosystem Portal</p> <p><strong>Topic</strong><br> ACM CSS 2012: ACM CSS 2012: Human-centered computing - Human computer interaction (HCI) - User studies, Usability testing</p> <p><strong>Name entitites</strong><br> Organizational information: Graz University of Technology, Forschungsinstitut für Telekommunikation und Kooperation<br> Geographical information: Europe</p> <p>Time information: May-June 2018</p> <p><strong>Types</strong>: Excel file</p> <p><strong>RAGCS target group:</strong> target groups - supply side - research institutions; industry participants</p> <p><strong>Evaluation dimensions</strong><br> Evaluation object: features and functions of the Social Networking Platforms and Groupware Platforms along with the Visual Interactive Asset Map integrated into the RAGE Ecosystem portal<br> Evaluation variables: usefulness, usability, quality of the user interface, quality of the tutorial, demographics, added value and other features</p> <p><strong>Instruments:</strong> USE questionnaire (usefulness, satisfaction, ease of use; Lund, 2001); items adopted from the System Usability Scale - SUS (Brooke, 1996); items adapted from Thielsch & Stegemöller (2012); questionnaire items defined for the purpose of the evaluation</p> <p><strong>Relationships</strong>: D8.4 - Second RAGE Evaluation Report<br> Related datasets: <a href="https://doi.org/10.5281/zenodo.1209192">10.5281/zenodo.1209192</a>, <a href="https://doi.org/10.5281/zenodo.1209198">10.5281/zenodo.1209198</a>.</p>
Evaluation of Envision Rating System for Underground Transportation Infrastructure
<p>Credit-by-credit evaluation of the Envision rating system for representative underground transportation projects. Related paper presented at the 2019 International Conference for Sustainable Infrastructure.</p>
Design, Synthesis and Biological Evaluation of 7-Chloro-9H-pyrimido[4,5-b]indole-based Glycogen synthase kinase-3β inhibitors
<p>The dataset related to the publication:</p> <p>Andreev et al. Design, Synthesis and Biological Evaluation of 7 Chloro-9H-pyrimido[4,5-b]indole-based Glycogen synthase kinase-3β inhibitors.<br> <br> The files include:</p> <p>1) Movies of the MD simulations of compounds 14b (<strong>Cmpd14b.mpg</strong>) and 24 (<strong>Cmpd24.mpg</strong>).</p> <p>2) Raw trajectory files of the Desmond MD simulations of the compounds 14b (<strong>14b.zip</strong>), 24 (<strong>24.zip</strong>) and 24 (3a<em>R</em>, 7a<em>S</em>) (<strong>24_3aR_7aS.zip</strong>) (contains out.cms and the full trj files).</p> <p>3) Output conformations of the QM Tautomer & Conformation Predictor of Maestro (Schrödinger, LLC, New York, NY, 2019) of the compounds<br> 14b (<strong>Cmpd14b_QMconftauto_output.mae</strong>; <strong>Cmpd14b_QMconftauto_output.sdf</strong>), <br> 24 (<strong>Cmpd24_QMconftauto_output.mae</strong>; <strong>Cmpd24_QMconftauto_output.sdf</strong>) and <br> 24 (3a<em>R</em>, 7a<em>S</em>) (<strong>Cmpd24_3aR_7aS_QMconftauto_output.mae</strong>; <strong>Cmpd24_3aR_7aS_QMconftauto_output.sdf</strong>)</p> <p>4) Jaguar pKa calculation (conformations) output-files for compounds 14b, 14c, 14d, 14e, 14f, 14g, 14h, 14i, 14j, 14k, 14l, 14m, 14n <strong>pKa.zip</strong> (contains conformation in .mae file and Final weighted pKa value in .out file).</p>
Development, evaluation and application of NicheNet: datasets
<p>Here you can find the datasets used for development, validation and application of NicheNet, a method that links ligands to target genes to model intercellular communictation. The software to use NicheNet (i.e. R package nichenetr) is available at:</p> <p><a href="https://github.com/saeyslab/nichenetr">https://github.com/saeyslab/nichenetr</a></p> <p>Short description of individual files:</p> <p>* data for development of NicheNet: </p> <p>- lr_network.rds: network containing ligand-receptor interactions</p> <p>- signaling_network.rds: network containing signaling interactions</p> <p>- gr_network.rds: network containing gene regulatory interactions</p> <p>- weighted networks.rds: integrated weighted ligand-signaling and gene regulatory network</p> <p>* data for use of NicheNet:</p> <p>- ligand_tf_matrix.rds: matrix containing ligand-gene signaling scores</p> <p>- tf_target_matrix.rds: matrix containing gene-gene regulatory scores</p> <p>- ligand_target_matrix.rds: matrix containing ligand-target gene regulatory potential scores</p> <p>* datasets for validation of NicheNet:</p> <p>- expression_settings.rds: 111 expression datasets profiling the transcriptional response to a ligand</p> <p>* datasets to demonstrate application possibilities of NicheNet:</p> <p>- pemt_signature.txt: p-EMT geneset for which upstream regulators will be predicted</p> <p>- hnscc_expression.rds: single-cell expression data of malignant and non-malignant cells from head and neck squamous cell carcinoma tumors</p>
ENVRIplus task 15.3 workshop evaluations
<p>Score sheets from evaluations forms for training workshops on Research Infrastrcuture management, held in Zandvoort (NL) on 14/15 May 2018 and Milan (IT) on 4/5/6 February 2019.</p>
Confirmation Bias in Web-Based Search: A Randomized Online Study on the Effects of Expert Information and Social Tags on Information Search and Evaluation
<p>ABSTRACT</p> <p>Background: The public typically believes psychotherapy to be more effective than pharmacotherapy for depression treatments. This is not consistent with current scientific evidence, which shows that both types of treatment are about equally effective.</p> <p>Objective: The study investigates whether this bias towards psychotherapy guides online information search and whether the bias can be reduced by explicitly providing expert information (in a blog entry) and by providing tag clouds that implicitly reveal experts’ evaluations.</p> <p>Methods: A total of 174 participants completed a fully automated Web-based study after we invited them via mailing lists. First, participants read two blog posts by experts that either challenged or supported the bias towards psychotherapy. Subsequently, participants searched for information about depression treatment in an online environment that provided more experts’ blog posts about the effectiveness of treatments based on alleged research findings. These blogs were organized in a tag cloud; both psychotherapy tags and pharmacotherapy tags were popular. We measured tag and blog post selection, efficacy ratings of the presented treatments, and participants’ treatment recommendation after information search.</p> <p>Results: Participants demonstrated a clear bias towards psychotherapy (mean 4.53, SD 1.99) compared to pharmacotherapy (mean 2.73, SD 2.41; <em>t</em><sub>173</sub>=7.67, <em>P</em><.001, <em>d</em>=0.81) when rating treatment efficacy prior to the experiment. Accordingly, participants exhibited biased information search and evaluation. This bias was significantly reduced, however, when participants were exposed to tag clouds with challenging popular tags. Participants facing popular tags challenging their bias (n=61) showed significantly less biased tag selection (<em>F</em><sub>2,168</sub>=10.61, <em>P</em><.001, partial eta squared=0.112), blog post selection (<em>F</em><sub>2,168</sub>=6.55, <em>P</em>=.002, partial eta squared=0.072), and treatment efficacy ratings (<em>F</em><sub>2,168</sub>=8.48, <em>P</em><.001, partial eta squared=0.092), compared to bias-supporting tag clouds (n=56) and balanced tag clouds (n=57). Challenging (n=93) explicit expert information as presented in blog posts, compared to supporting expert information (n=81), decreased the bias in information search with regard to blog post selection (<em>F</em><sub>1,168</sub>=4.32, <em>P</em>=.04, partial eta squared=0.025). No significant effects were found for treatment recommendation (<em>P</em>s>.33).</p> <p>Conclusions: We conclude that the psychotherapy bias is most effectively attenuated—and even eliminated—when popular tags implicitly point to blog posts that challenge the widespread view. Explicit expert information (in a blog entry) was less successful in reducing biased information search and evaluation. Since tag clouds have the potential to counter biased information processing, we recommend their insertion.</p>
Mowgli: DBMS Elasticity Evaluation Data Sets
<p>These data sets contain the complete DBMS evaluation data created by the <a href="https://omi-gitlab.e-technik.uni-ulm.de/mowgli/getting-started">Mowgli</a> framework for the <em>calibration</em> and <em>elasticity</em> phases.</p> <p><em>Calibration phase:</em> performance/latency metrics for growing workload intensities issued against a fixed three node DBMS cluster.</p> <p><em>Elasticity phase:</em> performance/latency metrics during an elastic scale-out adaptation (i.e. adding one additional DBMS node to the cluster at runtime) for different workload intensities.</p>
Casting about in the Dark - Artifact Evaluation
<p>Companion dataset artifact used in the paper "Casting about in the Dark".</p> <p>This version incorporates some additional minor changes to the artifact, related to the additionally sampled cast instances. This version is the one used in the final version of the paper.</p> <p>---</p> <p>Abstract:</p> <p>The main goal of a static type system is to prevent certain kinds of errors from happening at run time. A type system is formulated as a set of constraints that gives any expression or term in a program a well-defined type. Yet mainstream programming languages are endowed with type systems that provide the means to circumvent their constraints through <em>casting</em>.</p> <p>We want to understand how and when developers escape the static type system to use dynamic typing. We empirically study how casting is used by developers in more than seven thousand Java projects. We find that casts are widely used (8.7% of methods contain at least one cast) and that 50% of casts we inspected are not guarded locally to ensure against potential run-time errors.</p> <p>To help us better categorize use cases and thus understand how casts are used in practice, we identify 25 cast-usage patterns, recurrent programming idioms using casts to solve a specific issue. This knowledge can be: (a) a recommendation for current and future language designers to make informed decisions (b) a reference for tool builders, <em>e.g.</em>,<br> by providing more precise or new refactoring analyses, (c) a guide for researchers to test new language features, or to carry out controlled programming experiments, and (d) a guide for developers for better practices.</p>
11 km RACMO2 data used in CESM2 Greenland SMB evaluation paper
<p>Post-processed RACMO2.3p2 data used to carry out part of the analysis in the paper titled “Present day Greenland ice sheet climate and surface mass balance in CESM2”, currently in review with JGR Earth Surface.</p> <p>This data stems from a RACMO2.3p2 regional climate simulation over Greenland at 11 km which was averaged over the period 1961-1990. This data is further described in Noël et al., 2018 : <a href="https://doi.org/10.5194/tc-12-811-2018">https://doi.org/10.5194/tc-12-811-2018</a></p> <p>This data is published for archiving purposes only. Data requests for the latest version of RACMO2 output can be made free of charge to Brice Noël (B.P.Y.Noel@uu.nl) and Michiel van den Broeke (M.R.vandenBroeke@uu.nl). In your request, please specify the variables of interest, time period and time frequency, and area of interest.</p> <p>NETCDF NAMING CONVENTION<br> <strong>_ymonmean</strong> : climatological mean over period<br> <strong>_ymonstd</strong> : climatological standard deviation over period</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.