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.
2,129
datasets available to search
ShareScore release 0.9.0
Dataset results
2,129 results for “scores”
Expression-based polygenic score from the amygdala 5HTT gene network
<p>This pipeline intends to facilitate the calculation of biologically informed polygenic scores from collected genomic data. This template can be adapted to create other expression-based polygenic risk scores. Data is 1) step by step description and 2) a list of genes that compose the gene network.</p>
Instructional Design Models Score Ranking
<p>Here we present selected 17 instructional design models (IDMs) from our previous study (Cajnko, M. M., Pavlin, M., Likozar, B., Vasilakis, C., Tsovou, S., Chaundry, S. R., Kapal, D., van Leeuwen, M., Miloshevski, V., Toman, Y., Deniz, B., Şensoy Mercan, N., Pavitola, L., Memmedova, S., Verdiyeva, L., Sarsar, F., & Andiç Çakır, Ö. (2024). Instructional Design Models [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10900874) that were quantitatively evaluated by 20 experts from different countries (Azerbaijan, Greece, Ireland, Italy, Slovenia, Spain, and Türkiye) according to their suitability for the use in the engineering education. Evaluation was performed based on 12 statements, where each statement was quantitatively evaluated with values from 0 to 3 (0: Absolutely not suitable; 1: Partially suitable structure; 2: Moderately suitable structure; 3: Suitable structure), resulting in the total score of 36 for each model. The 12 statements were: </p> <p>1. Suitability for engineering education;<br>2. Suitability for virtual laboratories studies;<br>3. Suitability for technology integration;<br>4. Suitability for project-based work;<br>5. Suitability for active learning strategies;<br>6. Suitability for students with any disability;<br>7. Suitability for interdisciplinary design;<br>8. Suitability for cross-cultural practices;<br>9. Suitability for student feedback;<br>10. Suitability for instructor feedback;<br>11. Suitability for assessment and evaluation;<br>12. Suitability for reusability.</p> <p>The preliminary score of each IDM was obtained by averaging the scores of 20 experts and the standard deviation was calculated. Next, the scores of each IDM that lied outside of the average score ± 2*standard deviation was eliminated and the new average value was calculated. This process was repeated until all the remaining scores lied withing average score ± 2*standard deviation. The final score of all IDMs is reported on the first page of this dataset, while scoring of each expert are reported on subsequent pages.</p> <p>The five best scored IDMs are Instructional design model for unified eLearning, Assure model, Agile instructional design, Kemp, Morrison, and Ross model – Effective instructional design model, and ADDIE.</p> <p>This created dataset has been added as a database to the VILLAGE project web page (<a href="https://www.thevillageproject.eu/idmodelsdatabase/">https://www.thevillageproject.eu/idmodelsdatabase/</a>)</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>
SJR Dolphin SCA: Degradation Scores, OL Length and Identifications of Otoliths Collected from Bottlenose Dolphin Stomachs
Otoliths were collected from stomach contents of stranded bottlenose dolphins (Tursiops erebennus) in the St. Johns River in Jacksonville, Florida. Otoliths were analyzed by a panel of 3 reviewers to determine the level of otolith degradation that occurred during digestive processes. Otoliths with scores ≤ 3 were measured. Otolith length measurements were used to estimate the size of most species by applying standard regression equations developed from fish species collected from a nearby water system, the Indian River Lagoon, and for one species, equations developed from violet gobies collected from the St. Johns River. These equations enabled estimation of the mass of each prey species in each dolphin’s stomach, then the calculation of their relative proportions of reconstructed mass across all stomachs. For otolith identification purposes, a panel of 4 reviewers assigned each otolith with a family-level and species-level identification. Each identification was given a confidence code ranging from 1 (no confidence) to 4 (certainty). When the average code for all reviewers was < 3, the otolith was considered unidentified. If two of the reviewers agreed with the “weight” reviewer and all gave scores ≥ 3, the score of the outlying reviewer was discarded. Identification was assigned when the average confidence code was ≥ 3. The minimum number of species per dolphin stomach was determined by counting the left and right otoliths for each species separately, using the higher count as the minimum prey number. Unidentified species were counted, and half of their sum was considered the minimum prey number. The frequency of occurrence (%FO, or proportion of stomachs in which a species was detected) and numerical proportion (%N, or proportion of a given species pooled across all stomach samples) of each prey species were then calculated.
Presence/absence of new snow-fall scored from time-lapse photography collected near Toolik Field Station, Alaska, summers 2012-2016
This data set describes the presence/absence of new snowfall approximated daily using time -lapse photography images near Toolik Field Station during summers from 2012 to 2016 under National Science Foundation (NSF) Office of Polar Programs ARC 0908444 (to Laura Gough), ARC 0908602 (to Natalie Boelman), and ARC 0909133 (to John Wingfield). Additional cameras funded by other grants were also used for scoring including multiple Toolik EDC timelapse images taken at Toolik, Atigun Ridge, and Imnavait. Additional data were scored from time lapse photography taken by the Deegan and Urban labs (Office of Polar Programs #0902153 and #1417664). All data are associated with publication DOI: 10.1111/jav.01712.
GREEN-VARAN scores resources (CADD GRCh37)
<p>Processed CADD scores to be used with GREEN-VARAN</p> <p>This dataset contains the GRCh37 version for CADD v.1.4.</p> <p>See: <a href="https://cadd.gs.washington.edu/">https://cadd.gs.washington.edu/</a></p> <p>If you use CADD score annotations with GREEN-VARAN don't forget to cite also the original CADD paper.</p>
GREEN-VARAN scores resources (FATHMM-XF GRCh38)
<p>Processed FATHMM-XF non-coding scores to be used with GREEN-VARAN</p> <p>This dataset contains the GRCh38 version for FATHMM-XF v2.3 non-coding annotations.</p> <p>See: <a href="http://fathmm.biocompute.org.uk/">http://fathmm.biocompute.org.uk/</a></p> <p>If you use FATHMM-XF score annotations with GREEN-VARAN don't forget to cite also the original FATHMM-XF paper.</p>
GREEN-VARAN scores resources (EIGEN GRCh38)
<p>Processed EIGEN and EIGEN-PC scores to be used with GREEN-VARAN</p> <p>This dataset contains the GRCh38 version for EIGEN v1.1 non-coding annotations, obtained by coordinates liftover.</p> <p>See: <a href="http://www.columbia.edu/~ii2135/eigen.html">http://www.columbia.edu/~ii2135/eigen.html</a></p> <p>If you use EIGEN score annotations with GREEN-VARAN don't forget to cite also the original EIGEN paper.</p>
GREEN-VARAN scores resources (ncER)
<p>Processed ncER scores to be used with GREEN-VARAN</p> <p>This dataset contains the GRCh37 and GRCh38 versions for ncER v2 single-base resolution annotations. GRCh38 file is obtained by coordinates liftover.</p> <p>Original scores obtained from: https://github.com/TelentiLab/ncER_datasets </p> <p>Original publication: https://www.nature.com/articles/s41467-019-13212-3</p> <p>If you use ncER score annotations with GREEN-VARAN don't forget to cite also the original ncER paper.</p>
ReMM score
<p>The <strong>Re</strong>gulatory <strong>M</strong>endelian <strong>M</strong>utation (ReMM) score was created for relevance prediction of non-coding variations (SNVs and small InDels) in the human genome (hg19/hg38) in terms of Mendelian diseases.</p> <p> </p> <p><strong>Usage</strong></p> <p>The ReMM score is genome position wise (nucleotide changes are neglected). We precomputed all positions in the human genome (hg19 and hg38 release) and stored the values in a tabix file (1-based). The scores ranging from 0 (non-deleterious) to 1 (deleterious).</p> <p>If you want to use the ReMM score together with the Genomiser, please have a look at the <a href="https://exomiser.github.io/Exomiser/">Exomiser framework manual</a></p> <p>For more information, direct VCF file scoring, and an API please have a look at our website: <a href="https://remm.bihealth.org">https://remm.bihealth.org</a></p> <p><strong>ReMM score changelog</strong></p> <p>0.4:</p> <ul> <li>Features: <ul> <li>For missing values using genome mean of feature for sequence and conservaton features. 1 for p-value. All other features have zero as missing value</li> <li>Updating DGVCount to 02/25/2020 on hg19/hg38</li> <li>Update dbVARCount to 10/20/2021 on hg19/hg38</li> <li>Update ISCApath to 11/03/2021 on hg19/hg38</li> <li>Replace tfbsConsSites with UCSC table encRegTfbsClustered on hg19/hg38</li> </ul> </li> <li>Software: <ul> <li>Using parSMURF for training</li> </ul> </li> <li>Complete retraining of hg19 and hg38 builds (hg19: AUROC=0.993; AUPRC=0.394; hg38: AUROC=0.996; AUPRC=0.610)</li> </ul> <p>0.3.1.post1:</p> <ul> <li>New hg38 release. Completely retrained on the new genome build. <ul> <li>Training data: <ul> <li>Liftover positives. No change in size.</li> <li>Negatives used from CADD v1.4 GRCh38 (human derived), filtered as described in the original paper (<a href="https://doi.org/10.1016/j.ajhg.2016.07.005">https://doi.org/10.1016/j.ajhg.2016.07.005</a>). Size slightly different (hg38: 13,902,234; hg19: 14,755,199) .</li> </ul> </li> <li>Features <ul> <li>Same size as in hg19: 26 features.</li> <li>We tried to use the same features as in hg19. Sometimes new versions of data have to be used (e.g. DGV, ISCA, dbVAR).</li> </ul> </li> <li>Training was done with the parSMURF implementation of hyperSMURF. <ul> <li>Same hg19 parameters are used.</li> </ul> </li> <li>Metrics via 10-fold cytoband cross-validation (same cytoband to fold map): <ul> <li>Area under the ROC curve: 0.996 (hg19: 0.989, see <a href="https://doi.org/10.1016/j.ajhg.2016.07.005">https://doi.org/10.1016/j.ajhg.2016.07.005</a>)</li> <li>Area under the precision recall curve: 0.548 (hg19: 0.441, see <a href="https://doi.org/10.1016/j.ajhg.2016.07.005">https://doi.org/10.1016/j.ajhg.2016.07.005</a>)</li> </ul> </li> </ul> </li> <li>Scores for hg19 in this release are the same as version 0.3.1. Only the files have been renamed.</li> </ul> <p>0.3.1:</p> <ul> <li>Bugfix of region chr17:79759050-81195210. Region is missing in older versions.</li> </ul> <p>0.3:</p> <ul> <li>First official public version.</li> <li>Values for positions in training data are computed by cytoband-aware 10 fold cross-validation.</li> <li>Other position scores are compted by a generalized model of all training data.</li> <li>This version was used in the Genomiser publication (Smeley et.al. A Whole-Genome Analysis Framework for Effective Identification of Pathogenic Regulatory Variants in Mendelian Disease. AHJG. 2016)</li> </ul>
Eigen scores for human genome assembly GRCh38 Part 4 (Chr1 - Chr2)
<p>Eigen is a spectral approach to the functional annotation of genetic variants in coding and noncoding regions. Eigen makes use of a variety of functional annotations in both coding and noncoding regions (such as protein function scores, evolutionary conservation scores, and epigenetic annotations from ENCODE and Roadmap Epigenomics projects), and combines them into one single measure of functional importance. Eigen is an unsupervised approach, and, unlike many existing methods, is not based on any labelled training data. Eigen produces estimates of predictive accuracy for each functional annotation score, and subsequently uses these estimates of accuracy to derive the aggregate functional score for variants of interest as a weighted linear combination of individual annotations.</p>
Eigen scores for human genome assembly GRCh38 Part 3 (Chr3 - Chr5)
<p>Eigen is a spectral approach to the functional annotation of genetic variants in coding and noncoding regions. Eigen makes use of a variety of functional annotations in both coding and noncoding regions (such as protein function scores, evolutionary conservation scores, and epigenetic annotations from ENCODE and Roadmap Epigenomics projects), and combines them into one single measure of functional importance. Eigen is an unsupervised approach, and, unlike many existing methods, is not based on any labelled training data. Eigen produces estimates of predictive accuracy for each functional annotation score, and subsequently uses these estimates of accuracy to derive the aggregate functional score for variants of interest as a weighted linear combination of individual annotations.</p>
Scored protein-protein interactions accompanying "A pan-plant protein complex map reveals deep conservation and novel assemblies"
<p><a href="http://plants.proteincomplexes.org/static/data/panplant_cfms_scores_annot.txt.gz">All scored pairwise protein-protein interactions with CF-MS scores (3,076,999 unique pairwise interactions)</a></p> <ul> <li>Description: Scores between Orthogroups with the corresponding CF-MS score and eggNOG generated orthogroup descriptions.</li> <li>Note: Only the highest scoring pairs are considered significant. A CF-MS score >= 0.509 corresponds to 10% FDR, >= 0.207 corresponds to 50% FDR</li> <li>Format: OrthogroupID1 [tab] OrthogroupID2 [tab] Score [tab] Annotation1 [tab] Annotation2</li> </ul>
GREEN-VARAN scores resources (DANN GRCh37)
<p>Processed DANN scores to be used with GREEN-VARAN</p> <p>This dataset contains the GRCh37 version for DANN.</p> <p>See: <a href="https://academic.oup.com/bioinformatics/article/31/5/761/2748191">https://academic.oup.com/bioinformatics/article/31/5/761/2748191</a></p> <p>If you use DANN score annotations with GREEN-VARAN don't forget to cite also the original DANN paper.</p>
Credit Score USP Example
<p>The granting of credit, in recent years, has become one of the main sources of<br> revenues for banks. In this area, a great difficulty encountered by the manager<br> (decision maker) is the establishment of criteria that allow to carry out a credit analysis<br> that aggregates greater security for the operation and, consequently, assist in reducing<br> the default of the sector. In order to minimize this difficulty found in credit analysis, the<br> study of case of this dissertation has as its basic proposal the elaboration of a model<br> that, according to perception of the granting manager himself, help him to evaluate the<br> performance of clients credit borrowers, in those criteria deemed important by the<br> manager. For construction model, the Multicriteria Decision Support Methodology<br> (MCDA-Construtivista) was considered the most appropriate, due to its ability to<br> integrate both objective and subjective elements, thus providing an excellent<br> opportunity for generating knowledge and promotion of understanding related to<br> problems of this nature. The utilization model proposed here, it will provide an<br> opportunity to assess the borrower's performance those criteria considered important<br> by the decision-maker, thus contributing to aggregate greater agility and security for<br> the act of granting credit.</p>
Combined RSEI Water Scores and American Community Survey, 2011–2021
<p>This dataset was created for a master's thesis in geography at the University of Utah. County-level RSEI scores and county demographics were combined for a spatiotemporal regression analysis. Counties in the contiguous United States and years 2011–2021 are included.</p> <p><em>Risk-Screening Environmental Indicators</em>. Annual, county-level RSEI risk scores for surface water were obtained from the EPA RSEI model through the <a href="https://edap.epa.gov/public/extensions/EasyRSEI_AllYears/EasyRSEI_AllYears.html">EasyRSEI Dashboard</a>. RSEI scores are <a>defined as the relative potential for health risks, given the estimated exposure calculated by the EPA</a>. A binary risk indicator was created, signifying whether the county had any risk in a given year.</p> <p><em>American Community Survey 5-year estimates</em>. County-level population characteristics were collected as five-year estimates from the American Community Survey, with annual estimates for the survey end year applying to each year from 2011–2021. We used the R tidycensus package to collect variables and geometry from the US Census Bureau API. Variables included <a>total population (scaled by 10,000), percent racial composition (American Indian or Alaska Native, or AIAN; Asian; Black or African American; Native Hawaiian or Other Pacific Islander, or NHPI; Some Other Race; Two or More Races; and White), and percent ethnic composition (Hispanic or Latinx). </a>Each census-defined racial category was collected as non-Hispanic or Latinx, such that each group was mutually exclusive.</p>
CADD-SV - A framework to score the effects of structural variants in health and disease
<p>Required annotation data-set to run the CADD-SV framework; a method to retrieve and integrate a wide set of annotations to predict the effects of SVs. Pre-scored variants as well as additional information on used features.<br> A webserver for online scoring as well as data downloads is available at: https://cadd-sv.bihealth.org/<br> Source code for CADD-SV is available at GitHub: https://github.com/kircherlab/CADD-SV</p>
SCoRe-LFC: Platform data on crowd collaboration in higher education
<p>SCoRe (short for Student Crowd Research) was a joint research project between the Universities of Bremen (UB), Hamburg (UHH) and Kiel (CAU), the Macromedia University of Applied Sciences (HMM) and the Ghostthinker GmbH (GT). The overall aim of the project was to develop a digital learning and research environment as well as didactic scenarios that foster collaborative processes of research-based learning in large groups of students (crowd). The main subject area was research for sustainable development. Towards this end, the project consortium drew on the partners’ expertise on advanced video-technologies (HHM), virtual collaboration in interdisciplinary and largescale groups (CAU), research-based learning (UHH) and education and research for sustainable development (UB). To achieve its goals, the project adopted a design-based research approach. The Project started in Oct. 2018 and was funded for 3.5 years by the Federal Ministry of Education and Research (BMBF) in a funding scheme on digital higher education.</p> <p>The work in the department of media-pedagogy and educational computer sciences at Kiel University was focused on the sub-project „SCoRe - learning and researching in the crowd“. The sub-project was aimed at the development, implementation and evaluation of pedagogical and organizational measures for the seeding, coordination and orchestration of collaborative research and learning processes in crowd scenarios. Particular emphasis was placed on crowd-specific characteristics of productive knowledge work in large and interdisciplinary groups.</p> <p>This dataset contains interaction data as well as textual content data. As ongoing development of the software platform led to a continuous integration of new features into the platform itself as well as changes to the data collection functions, making this an evolving dataset. Some inconsistencies exist due to software bugs.</p> <p><strong><a href="https://scorelfc.github.io/gestaltungsbericht3/img/datastructure.png">Platform data structure diagram</a> </strong></p> <p>Further Readings to gain an understanding of the platform and its interaction posbilities (in german):</p> <p><a href="https://scorelfc.github.io/gestaltungsbericht2">Design Report Prototype 2</a></p> <p><a href="https://scorelfc.github.io/gestaltungsbericht3">Design Report Prototype 3</a></p> <p> </p> <p><strong>Contained Files</strong></p> <table> <tbody> <tr> <td> <p><strong>Filename</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>annotations.csv</p> </td> <td> <p>Annotations (comment and/or drawings on the video) of video files</p> </td> </tr> <tr> <td> <p>content.csv</p> </td> <td> <p>Content of <a href="https://scorelfc.github.io/gestaltungsbericht3/umsetzungen/u03/">sections</a></p> </td> </tr> <tr> <td> <p>events.csv</p> </td> <td> <p>All events triggered by user interaction</p> </td> </tr> <tr> <td> <p>media.csv</p> </td> <td> <p>Uploaded <a href="https://scorelfc.github.io/gestaltungsbericht3/umsetzungen/u03/">images and videos</a></p> </td> </tr> <tr> <td> <p>messages.csv</p> </td> <td> <p><a href="https://scorelfc.github.io/gestaltungsbericht3/umsetzungen/u08/">Chat messages</a></p> </td> </tr> <tr> <td> <p>sequences.csv</p> </td> <td> <p><a href="https://scorelfc.github.io/gestaltungsbericht3/umsetzungen/u21/">Sequences</a> of video files</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Columns</strong></p> <p>(not all are present in each file. 0, “null” or “none” might mean not applicable)</p> <table> <tbody> <tr> <td> <p><strong>Column name</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Format</strong></p> </td> </tr> <tr> <td> <p>Index (empty column name) </p> </td> <td> <p>unique identifier of the corresponding event in the original dataset</p> </td> <td> <p>UUID (int on rare occasions)</p> </td> </tr> <tr> <td> <p>Actor-Name</p> </td> <td> <p>Unique identifier of an actor – “MA” identifies project staff </p> </td> <td> <p>string</p> </td> </tr> <tr> <td> <p>Annotation-ID</p> </td> <td> <p>Unique identifier of an annotation</p> </td> <td> <p>int</p> </td> </tr> <tr> <td> <p>Annotation-Text</p> </td> <td> <p>Label of an annotation</p> </td> <td> <p>string</p> </td> </tr> <tr> <td> <p>Version-ID</p> </td> <td> <p>Unique identifier of a version of an auditable object (e.g. a section)</p> </td> <td> <p>int</p> </td> </tr> <tr> <td> <p>Version-Changelog</p> </td> <td> <p>Changelog message on saving a new version of a section</p> </td> <td> <p>string</p> </td> </tr> <tr> <td> <p>Case-ID</p> </td> <td> <p>Unique identifier of a case (if applicable, coded by research team)</p> </td> <td> <p>String </p> </td> </tr> <tr> <td> <p>Media-Caption</p> </td> <td> <p>Title of a media file (image, video)</p> </td> <td> <p>string</p> </td> </tr> <tr> <td> <p>Media-ID</p> </td> <td> <p>Unique identifier of a media file (image, video)</p> </td> <td> <p>int</p> </td> </tr> <tr> <td> <p>Media-Timestamp</p> </td> <td> <p>Timestamp in a video</p> </td> <td> <p>int</p> </td> </tr> <tr> <td> <p>Message-ID</p> </td> <td> <p>Unique identifier of a chat message</p> </td> <td> <p>int</p> </td> </tr> <tr> <td> <p>Message-Text</p> </td> <td> <p>Content of a chat-message</p> </td> <td> <p>string</p> </td> </tr> <tr> <td> <p>Object-Type</p> </td> <td> <p>Type of an object an action refers to</p> </td> <td> <p>string</p> </td> </tr> <tr> <td> <p>Project-ID</p> </td> <td> <p>Unique identifier of a project</p> </td> <td> <p>int</p> </td> </tr> <tr> <td> <p>Research-Task-Type</p> </td> <td> <p>Type of research task (if applicable, coded by research team, see table below)</p> </td> <td> <p>string</p> </td> </tr> <tr> <td> <p>Section-Content</p> </td> <td> <p>Content of a section (in a specific version) </p> </td> <td> <p>string</p> </td> </tr> <tr> <td> <p>Section-Outline-Level</p> </td> <td> <p>Outline level of a section (in a specific version)</p> </td> <td> <p>int</p> </td> </tr> <tr> <td> <p>Section-ID</p> </td> <td> <p>Unique identifier of a section</p> </td> <td> <p>int</p> </td> </tr> <tr> <td> <p>Section-Index</p> </td> <td> <p>Position of a section in the project (in a specific version)</p> </td> <td> <p>int</p> </td> </tr> <tr> <td> <p>Section-Status</p> </td> <td> <p>Status of a section</p> </td> <td> <p>int</p> </td> </tr> <tr> <td> <p>Section-Title</p> </td> <td> <p>Title of a section (in a specific version)</p> </td> <td> <p>string</p> </td> </tr> <tr> <td> <p>Sequence-Description</p> </td> <td> <p>Description of a video sequence</p> </td> <td> <p>string</p> </td> </tr> <tr> <td> <p>Sequence-Duration</p> </td> <td> <p>Length of a video sequence</p> </td> <td> <p>int</p> </td> </tr> <tr> <td> <p>Sequence-ID</p> </td> <td> <p>Unique identifier of a sequence</p> </td> <td> <p>int</p> </td> </tr> <tr> <td> <p>Sequence-Timestamp</p> </td> <td> <p>Timestamp of the start of a sequence in a video</p> </td> <td> <p>int</p> </td> </tr> <tr> <td> <p>timestamp</p> </td> <td> <p>timestamp of an event</p> </td> <td> <p>datetime</p> </td> </tr> <tr> <td> <p>Verb</p> </td> <td> <p>Action type of an event (see table below)</p> </td> <td> <p>string</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Verbs</strong></p> <table> <tbody> <tr> <td> <p><strong>Value</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>canceled editing of</p> </td> <td> <p>Actor canceled editing of a section</p> </td> </tr> <tr> <td> <p>clicked</p> </td> <td> <p>Actor clicked a link</p> </td> </tr> <tr> <td> <p>collapsed</p> </td> <td> <p>Actor collapsed a section (hides its content form being viewed)</p> </td> </tr> <tr> <td> <p>compared versions of</p> </td> <td> <p>Actor compared two versions of a section</p> </td> </tr> <tr> <td> <p>created</p> </td> <td> <p>Actor created a new section, video sequence, video annotation, video playback command, project or news</p> </td> </tr> <tr> <td> <p>deleted</p> </td> <td> <p>Actor deleted a section, video sequence, video annotation, video playback command, project or news</p> </td> </tr> <tr> <td> <p>ended</p> </td> <td> <p>Actor played a video hitting its end</p> </td> </tr> <tr> <td> <p>expanded</p> </td> <td> <p>Actor expanded a collapsed section</p> </td> </tr> <tr> <td> <p>inserted</p> </td> <td> <p>Actor inserted a video comment (on occasions instead of created)</p> </td> </tr> <tr> <td> <p>left</p> </td> <td> <p>Actor left a context (e.g. a project, a chat window) by e.g. closing it using platform functions, changing a browser tab, etc.</p> </td> </tr> <tr> <td> <p>mentioned</p> </td> <td> <p>Actor mentioned another actor in a chat message</p> </td> </tr> <tr> <td> <p>opened</p> </td> <td> <p>Actor opened a context (e.g. a project, a chat window) by e.g. accessing it using platform functions or changing a browser tab</p> </td> </tr> <tr> <td> <p>paused</p> </td> <td> <p>Actor paused a video</p> </td> </tr> <tr> <td> <p>played</p> </td> <td> <p>Actor played a video</p> </td> </tr> <tr> <td> <p>read</p> </td> <td> <p>Actor read an activity message or news</p> </td> </tr> <tr> <td> <p>read all messages and activities of</p> </td> <td> <p>Actor used switch to mark all chat and activity messages read</p> </td> </tr> <tr> <td> <p>restored</p> </td> <td> <p>Actor restored a deleted section</p> </td> </tr> <tr> <td> <p>reverted</p> </td> <td> <p>Actor restored a deleted section</p> </td> </tr> <tr> <td> <p>reverted version of</p> </td> <td> <p>Actor reverted a section to an earlier version</p> </td> </tr> <tr> <td> <p>seeked</p> </td> <td> <p>Actor seeked on a video timeline</p> </td> </tr> <tr> <td> <p>sent</p> </td> <td> <p>Actor sent a chat message</p> </td> </tr> <tr> <td> <p>started editing of</p> </td> <td> <p>Actor started editing of a section</p> </td> </tr> <tr> <td> <p>switched</p> </td> <td> <p>Actor switched chat focus between project and section chat</p> </td> </tr> <tr> <td> <p>typed</p> </td> <td> <p>Actor typed into the chat</p> </td> </tr> <tr> <td> <p>updated</p> </td> <td> <p>Actor updated an existing section (changing content, heading, heading-depth or status), video sequence, video annotation, video playback command, project or news</p> </td> </tr> <tr> <td> <p>uploaded</p> </td> <td> <p>Actor uploaded an image or video</p> </td> </tr> <tr> <td> <p>viewed</p> </td> <td> <p>Actor viewed an entity (had it on screen for 5 seconds), e.g. a section or video comment</p> </td> </tr> <tr> <td> <p>viewed history of</p> </td> <td> <p>Actor viewed history of a section</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Project-ID</strong></p> <table> <tbody> <tr> <td> <p><strong>Project-ID</strong></p> </td> <td> <p><strong>Case-IDs</strong></p> </td> <td> <p><strong>Title</strong></p> </td> <td> <p> </p> <p><strong>Type</strong></p> </td> <td> <p><strong>Period</strong></p> </td> </tr> <tr> <td> <p>2</p> </td> <td> <p>a1-a*</p> </td> <td> <p>Urbane Grünflächen</p> </td> <td> <p>Research project</p> </td> <td> <p>1.11.20-31.3.21</p> </td> </tr> <tr> <td> <p>4</p> </td> <td> <p>b1-b*</p> </td> <td> <p>Nachhaltiger Verkehr</p> </td> <td> <p>Research project</p> </td> <td> <p>1.11.20-31.3.21</p> </td> </tr> <tr> <td> <p>166</p> </td> <td> <p>c1-c*</p> </td> <td> <p>UGF - Urbane Grünflächen</p> </td> <td> <p>Research project</p> </td> <td> <p>1.4.21-30.09.21</p> </td> </tr> <tr> <td> <p>168</p> </td> <td> <p> </p> </td> <td> <p>LGS - Foyer</p> </td> <td> <p>Onboarding of students in LGS Projects</p> </td> <td> <p>1.4.21-30.09.21</p> </td> </tr> <tr> <td> <p>188</p> </td> <td> <p> </p> </td> <td> <p>LGS - Reflexionsraum</p> </td> <td> <p>Reflection project for students in LGS Projects</p> </td> <td> <p>1.4.21-30.09.21</p> </td> </tr> <tr> <td> <p>207</p> </td> <td> <p> </p> </td> <td> <p>LGS - Nachhaltiger Konsum</p> </td> <td> <p>Research project</p> </td> <td> <p>1.4.21-30.09.21</p> </td> </tr> <tr> <td> <p>210</p> </td> <td> <p> </p> </td> <td> <p>LGS - Bildungsangebote für nachhaltige Entwicklung</p> </td> <td> <p>Research project</p> </td> <td> <p>1.4.21-30.09.21</p> </td> </tr> <tr> <td> <p>264</p> </td> <td> <p> </p> </td> <td> <p>LGS - Fahrradmobilität in Städten</p> </td> <td> <p>Research project</p> </td> <td> <p>1.4.21-30.09.21</p> </td> </tr> <tr> <td> <p>271</p> </td> <td> <p> </p> </td> <td> <p>Fahrradmobilität in Städten</p> </td> <td> <p>Research project</p> </td> <td> <p>1.10.21-30.11.21</p> </td> </tr> <tr> <td> <p>269</p> </td> <td> <p>e1-e*</p> </td> <td> <p>Kaufentscheidung vs. Nachhaltigkeit</p> </td> <td> <p>Research project</p> </td> <td> <p>1.10.21-30.11.21</p> </td> </tr> <tr> <td> <p>262</p> </td> <td> <p>d1-d*</p> </td> <td> <p>Urbane Grünflächen</p> </td> <td> <p>Research project</p> </td> <td> <p>1.10.21-30.11.21</p> </td> </tr> <tr> <td> <p>199</p> </td> <td> <p> </p> </td> <td> <p>Basiskurs</p> </td> <td> <p>Basic course for onboarding of students on the platform</p> </td> <td> <p>1.10.21-30.11.21</p> </td> </tr> <tr> <td> <p>5</p> </td> <td> <p> </p> </td> <td> <p>Glossar</p> </td> <td> <p>Glossar of definitions</p> </td> <td> <p>persistent</p> </td> </tr> <tr> <td> <p>6</p> </td> <td> <p> </p> </td> <td> <p>Erste Schritte</p> </td> <td> <p>How to start using score-docs platform</p> </td> <td> <p>persistent</p> </td> </tr> <tr> <td> <p>8</p> </td> <td> <p> </p> </td> <td> <p>Testbereich</p> </td> <td> <p>Area for testing score-docs functionalities</p> </td> <td> <p>persistent</p> </td> </tr> <tr> <td> <p>7</p> </td> <td> <p> </p> </td> <td> <p>Hilfestellungen</p> </td> <td> <p>Helpful links </p> </td> <td> <p>persistent</p> <p> </p> </td> </tr> </tbody> </table> <p>Other Project-IDs refer to personal assessment documents of individual students which are not included in content.csv</p> <p><strong>Research-Task-Type</strong></p> <table> <tbody> <tr> <td> <p><strong>Value</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>Erheben</p> </td> <td> <p>Data collection</p> </td> </tr> <tr> <td> <p>Analysieren</p> </td> <td> <p>Case-specific data analysis and sensemaking</p> </td> </tr> <tr> <td> <p>Synthetisieren</p> </td> <td> <p>Cross-case data analysis and sensemaking</p> </td> </tr> <tr> <td> <p>Sonstiges</p> </td> <td> <p>Other, e.g. communication between participants</p> </td> </tr> </tbody> </table>
Communication score lexicon
<p>This lexicon was developed in the context of measuring <em>“openness of communication” in the paper named </em> ''Predicting Openness of Communication in Families with Hereditary Breast and Ovarian Cancer Syndrome: Natural Language Processing Analysis''. To develop an <em>“openness of communication”</em> score we built a lexicon containing words and phrases linked to communication and we classified them as positive or negative.</p> <p>The lexicon contains 532 items (132 unigrams, 215 bigrams, 185 trigrams). Two people independently created the scoring of N-grams in the lexicon as positive or negative. More specifically, they evaluated each item on a 7-point scale on how favorable the items measure <em>“openness of communication”</em>. Scoring values ranged from -3 (extremely strong negative word related to communication) to +3 (extremely strong positive word related to communication).</p>
GenoNet scores for human genome assembly GRCh38
<p>Predicting the functional consequences of genetic variants in non-coding regions is a challenging problem. We propose here a semi-supervised approach, GenoNet, to jointly utilize experimentally confirmed regulatory variants (labeled variants), millions of unlabeled variants genome-wide, and more than a thousand cell/tissue type-specific epigenetic annotations to predict functional consequences of non-coding variants.</p> <p><strong>Format</strong></p> <p>The GenoNet scores are stored in the tab-delimited text files. </p> <p>Each row represents a genomic region with 131 columns. Please find the header line in "genonet.header.txt". </p> <p>The first four columns are chromosome, start coordinate, end coordinate, and a region ID named by positions. Please note that the coordinates are counted in the 0-based UCSC Genome Browser BED format. For example, the following region with a start position 10000 and an end position 10025 includes 25 base pairs within chr1:10001-10025.</p> <p>chr1 10000 10025 chr1_10001_10025</p> <p>Columns 5-131 are the predicted tissue-specific functional effects (GenoNet scores) for the 127 Roadmap tissues. Each column is named by the corresponding epigenome ID. This <a href="https://docs.google.com/spreadsheet/ccc?key=0Am6FxqAtrFDwdHU1UC13ZUxKYy1XVEJPUzV6MEtQOXc&usp=sharing">online spreadsheet</a> includes the information about the 127 Roadmap tissues in detail.</p> <p><strong>Reference</strong><br> Zihuai He, Linxi Liu, Kai Wang, Iuliana Ionita-Laza. A semi-supervised approach for predicting cell type/tissue specific functional consequences of non-coding variation using massively parallel reporter assays. Nature Communications, 2018.</p> <p><strong>Release</strong></p> <p>GRCh37 <a href="https://zenodo.org/record/3336209">https://zenodo.org/record/3336209</a></p> <p>GRCh38 liftover <a href="https://zenodo.org/record/6484230">https://zenodo.org/record/6484230</a></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.