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Using a Hybrid Kano-Importance Questionnaire in the Acquisition of Data Related to Students' Expectations from Online Educational Platforms
<p>This dataset contains the data collected for the assessment of the quality attributes of a new online educational platform. The questionnaire used for data collection the Kano methodology and was designed as a hybrid Kano-importance questionnaire. The purpose of this data collection consists of the analysis of the students’ expectations regarding the features proposed for a new online educational platform. This analysis facilitates the identification of student needs during times of COVID-19 pandemic and post-pandemic times, while a transition to an online educational system was used throughout the world. </p>
Online Repository of the Study "I want to RIDE my e-bicycle!": Supporting Developers Categorizing User Issues of a Mobility-as-a-Service Platform
<p><strong>Online Repository of the Study </strong><em>“I want to RIDE my e-bicycle!": Supporting Developers Categorizing User Issues of a Mobility-as-a-Service Platform</em></p> <p><strong>Introduction</strong></p> <p>In the Mobility-as-a-Service (MaaS) context, e-bikes are important and environmental-friendly transportation resources providing flexibility, time and cost savings, and reducing traffic congestion. Additional to user satisfaction and marketing advantages, the resolution of user-reported issues is regulated in many cities. In order to efficiently solve the issues, it is essential to quickly identify their types (e.g., software- or hardware-related?) to assign them to the responsible team. But for popular e-mobility services, the manual analysis of the reports is inefficient because of its tediousness, high time requirements, and error-proneness. </p> <p>Our empirical study, carried out in the context of a <em>Mobility as a Service </em>start-up company, proposes an approach for the automated identification of relevant concerns reported by users of e-bike services. The company has more than 20,000 private customers across seven different countries and dedicates considerable effort in analyzing user behavior. However, the current manual process of analyzing and triaging user-reported issues hinders MaaS-company’s ability to grow and expand its services. </p> <p>To help MaaS providers identify relevant user-reported issues, In the study, we (i) manually inspect about 3,000 user-reported issues received by the MaaS company; (ii) design a taxonomy modeling the types of relevant issues reported by users; and (iii) propose MaaS-RIDE, an approach to automatically classify the user-reported issues according to the categories of the devised taxonomy. </p> <p>Our results demonstrate that MaaS-RIDE is able to accurately (F-measure ≥ 93%) identify software and hardware user-reported issues. This result is critical for e-bike sharing companies to address such issues in an agile way and achieve the required user satisfaction.</p> <p><strong>Dataset Overview</strong></p> <p>The dataset is composed of the following different sorts of data: </p> <ul> <li> “<em>Data_and_preprocessing</em>” folder <ul> <li>o the user-reported issues data</li> <li>o the user-reported issues data processed as Bag of Words for Machine Learning training. <ul> <li>For this look at the sub-folder “<em>input_data_for_ML</em>” and the following matrices: <ul> <li><em>tf-idf-matrix-of-comment_finals_with_oracle_info_low_level.csv</em></li> <li><em>tf-idf-matrix-of-comment_finals_with_oracle_info.csv</em></li> </ul> </li> <li>Moreover, a sample of selected issues was reported in the replication package: <ul> <li>see file “<em>randomSamples.csv</em>” (due to a non-disclosure agreement with our industrial partner, we are unauthorized to share the whole raw user reports used in our experiments)</li> <li> “RQ1” folder: Types of E-bikes User-reported Issues</li> </ul> </li> </ul> </li> <li> the resulting taxonomy after the analysis of the issues</li> <li> “RQ2” folder: Classifying E-bikes Issue types</li> <li> the trained models </li> <li> the results of the models</li> </ul> </li> </ul> <p>The following sections describe more in detail what each of those folders and files contain.</p> <p><strong>“Data_and_preprocessing” folder</strong></p> <ul> <li><strong>User-reported issues subset.</strong></li> </ul> <p>In an industrial setting, due to privacy reasons, we disclose only an example subset of the user-reported issues, this information is in the file <em>randomSamples.csv</em>.</p> <p>The <em>randomSamples.csv </em>a subset that was generated randomly adding 20 examples using a stratified sampling from the High-level categories and 20 from the Low-level categories. This subset is not exhaustive but serves the purpose of showing the reviewers the kind of issues that this particular industrial set is confronted with. The file contains:</p> <ul> <li> <ul> <li> the Id of the user report; </li> <li> the column "comment_final"<strong> </strong>contains the issue text after the replacement of information that needed anonymization (e.g., vehicle-plates, personal names, addresses and timestamps); </li> <li> the column "High_level_category" contains the selected category from the 5 first level categories of the presented <em>Three-level taxonomy of e-bike user reported issues</em>; </li> <li>• the columns ‘Low_level_category" and "Fine_grained_topic" contain the assigned, if existing, respective category. </li> </ul> </li> <li><strong>Bag of Words Term by Document matrix.</strong></li> </ul> <p>An important input for training the ML models is the Bag of Words representation generated after processing the 2,989 manually-labeled user issues. The result of this process is a Term-by-Document matrix. We share this matrix in the files in the sub-folder <em>input_data_for_ML </em>where they are labeled for High- and Low-level categories. </p> <p>In the <em>tf-idf-matrix-of-comment_finals_with_oracle_info.csv</em> and <em>tf-idf-matrix-of-comment_finals_with_oracle_info_low_level.csv</em> files, the first column refers to the issue “Id”, the last column “oracle” is the labeled category, the rest of the columns represent the terms contained in the 2,989 user-reported issues and in each row the weight of the i−π‘β term contained in the j−π‘β user issue by using the tf-idf score.</p> <p><strong>“RQ1” folder</strong></p> <ul> <li><strong>“Three-level taxonomy of e-bike user-reported issues.pdf<em>” file</em></strong></li> </ul> <p>The taxonomy derives from the manual analysis of the 2,989 user issues. We found that a three-level taxonomy provides significant granularity to the MaaS-company. The taxonomy encompasses 5 High-level categories, 16 Low-level categories, and 15 Low-level subcategories of e-bike user-reported issues. The file <em>Three-level taxonomy of e-bike user-reported issues.pdf</em> presents the taxonomy categories and in the columns “Nr.” and “%” it shows the number of occurrences within the analyzed dataset, and the corresponding percentages.</p> <p><strong>“RQ2” folder</strong></p> <ul> <li><strong>“Trained Models” folder</strong></li> </ul> <p>We provide the trained machine and deep learning models in the sub-folder <em>ML_DL_models</em>. Our approach experimented with classic machine learning models based on the Bag-of-Words approach using SVM, on Word Embeddings using FastText, and Language models leveraging BERT. The SVM and BERT models were trained using the open source low-code data analytics platform KNIME and were used to classify issues corresponding to the first and second levels of the taxonomy from the “RQ1” folder. A 10-fold cross validation strategy was used to assess the classification performance. </p> <p>The fastText model was trained by using default values of parameters (https://fasttext.cc/docs/en/options.html) and a 10-fold cross-validation strategy. With fastText, we classified issues corresponding only to the first level of the taxonomy from “RQ1” folder, since fastText is more effective when more data points are available in the training set (i.e., lower levels in the taxonomy have fewer well-represented issue types).</p> <ul> <li><strong>“Model results” folder</strong></li> </ul> <p>In the sub-folder model_results we provide the tables summarizing the results of using the proposed MaaS-RIDE approach, with which we automatically identify and categorize user-reported issues according to the High-level and Low-level categories of the taxonomy devised in RQ1, which are relevant for the MaaS-company. </p>
Figure 1 in The Tydeoidea (Ereynetidae, Iolinidae, Triophtydeidae and Tydeidae) - An online database in the Wikispecies platform
Figure 1 Diachronic classification of Tydeoidea. Abbreviations: pcp = post-cunliffean period, Pseudot. = Pseudotydeinae, R = Riccardoellinae, s = synonymy, subfam. = subfamilies, Trioph. = Triophtydeidae.
Figure 4 A-B in The Tydeoidea (Ereynetidae, Iolinidae, Triophtydeidae and Tydeidae) - An online database in the Wikispecies platform
Figure 4 A-B β Tetranychus urticae; C β T. viburni; D β Tydeus goetzi; A β Dissecting microscope view; B-C β Facsimile of Koch's figures (same magnification) with some dorsal setae notation added; D β Compound microscope view, Agroscope Changins [Switzerland], routine black chlorazol coloration by Marc Baillod, scale bar = 100 Β΅m. Koch's "Schulterborsten" correspond to scapular setaesc(1 andsc2) plus the subhumeral seta (c3). A β photoghraphy by Gilles San Martin. CC-BY.
Figure 3 in The Tydeoidea (Ereynetidae, Iolinidae, Triophtydeidae and Tydeidae) - An online database in the Wikispecies platform
Figure 3 The number of ereynetid mites described by Fain and by other acarologists (data grouped by decade).
Survey for online registered users of HistoricGraves platform
<div>This survey is being conducted by Eachtra Archaeological Projects as part of INCULTUM (2021-2024), a tourism-oriented HORIZON2020 funded project. The main goal of this survey was to better understand users and usage of the Historic Graves website and how to improve the visitor experience.</div>
Text-fig. 1. "Plant screen" scheme of complete results of the IPR-vegetation analysis derived from the database. in The Integrated Plant Record Vegetation Analysis: Internet Platform And Online Application
Text-fig. 1. "Plant screen" scheme of complete results of the IPR-vegetation analysis derived from the database.
Data and code for "Search Algorithm, Repetitive Information, and Sales on Online Platform"
<p>Data and code for "Search Algorithm, Repetitive Information, and Sales on Online Platform".</p> <p>The R code containsboth code for simulation and code for estimation.</p>
The morphologically glossed Rigveda - The Zurich annotation corpus revised and extended. Hosted by VedaWeb - Online Research Platform for Old Indic Texts.
<p>This file contains morphological and lexicographic annotations for the Rigveda. It was created in the DFG-funded research project Vedaweb and used as source data for the linguistic research platform <a href="https://vedaweb.uni-koeln.de">vedaweb.uni-koeln.de</a>.</p> <p>Prof. Dr. Paul Widmer and Dr. Salvatore Scarlata from the "Institut für Vergleichende Sprachwissenschaft" (Universität Zürich) provided the VedaWeb project a Filemaker file that was later transformed in Cologne into an Excel file. This data contained a version of the Rigveda by Prof. Dr. A. Lubotsky ("Indo-European Linguistics", Leiden University) that had been morphosytactically annotated over the course of more than 10 years at the University of Zurich. It also contained for each token, if available, a reference to an entry in Grassmann's dictionary for the Rigveda.</p> <p> </p> <p><strong>Modifications made by Jakob Halfmann and Natalie Korobzow to the data in 2020:</strong></p> <p>Disambiguation of the relevant categories, if unspecified in Zurich data, according to the Grassmann dictionary (updates from 6th edition partially included up to page 274):</p> <ul> <li>case, gender and number for nouns, pronouns (columns G–I)</li> <li>number, person, mood, tense and voice for verbs (columns I–M) up to line 109216</li> <li>case, gender, number, tense and voice for participles (columns G–I, L–M) up to line 109216</li> <li>absolutives are marked as Abs. in columns N and V</li> <li>Inconsistencies between the original file from Zurich and the Grassmann dictionary as well as internal inconsistencies in Grassmann are noted in column AE, whenever they were noticed.</li> <li>Zurich data was overwritten by conflicting Grassmann data in columns G–M but retained elsewhere.</li> <li>Verb classes according to Whitney (1885) and Jamison (1983) for class 10 in column Y, differences in root spelling between Whitney and Grassmann are noted in column Z. All potential verb classes provided by Whitney are given for every occurrence of the root.</li> <li>Local particles and verbal forms containing them are marked as LP in column AF.</li> <li>Comparatives and superlatives are marked as such in column X and desideratives as Des. in column Y.</li> </ul> <p> </p> <p><strong>Modifications made by Anna Fischer (data transformation, technical realisation) to the data:</strong></p> <p>New structure of data table for linguistic annotations with new column titles:</p> <ul> <li>A - "VERS_NR": renamed column (from "belege::stelleMMSSSRR")</li> <li>B - "PADA_NR": renamed column (from "belege::pada")</li> <li>C - "PADA_TEXT_LUBOTSKY": renamed column (from "belege::lubotskypada")</li> <li>D - "TOKEN_NR_VERS": renamed column (from "belege::wortnummer rc")</li> <li>E - "TOKEN_NR_PADA": renamed column (from "belege::wortnummer pada")</li> <li>F - "FORM": renamed column (from "belege::form")</li> <li>G - "KASUS": renamed column (from "belege::kasus")</li> <li>H - "GENUS": renamed column (from "belege::genus")</li> <li>I - "NUMERUS": renamed column (from "belege::numerus")</li> <li>J - "PERSON": renamed column (from "belege::person")</li> <li>K - "TEMPUS": moved and renamed column (from L "belege::tempus")</li> <li>L - "PRAESENSKLASSE": created new column for present stem class for each form</li> <li>M - "LEMMA_PRAESENSKLASSEN": created column for present stem classes of respective lemma: Moved and renamed column (from Y "formen::zusätzliche merkmale verb"), moved values "Abs." and "Inf." to column P "INFINIT", moved values "Prek." and "si-Ipv." to column N "MOOD", moved value "Des." to column Q "ABGELEITETE_KONJUGATION": moved value "se-Form" to column W "WEITERE_WERTE"</li> <li>N - "MODUS": moved and renamed column (from K "belege::modus")</li> <li>O - "DIATHESE": moved and renamed column (from M "belege::diathese")</li> <li>P - "INFINIT": created new column for infinite forms "Abs.", "Inf.", "Ptz.", "ta-Ptz.", "na-Ptz."</li> <li>Q - "ABGELEITETE_KONJUGATION": created new column for secondary conjugation "Des.", "Int.", "Kaus."</li> <li>R - "GRADUS": created new column for degree: "Comp.", "Sup."</li> <li>S - "LOKALPARTIKEL": moved and renamed column (from AF "LP")</li> <li>T - "LEMMA_ZÜRICH": moved and renamed column (from AA "lemmata klassisch::lemma")</li> <li>U - "LEMMA_ZÜRICH_LEMMATYP": moved and renamed column (from AB "lemmata klassisch::lemmatyp")</li> <li>V - "LEMMA_ZÜRICH_BEDEUTUNG": moved and renamed column (from AC "lemmata klassisch::bedeutung")</li> <li>W - "WEITERE_WERTE": created new column for all miscellaneous values: e.g. "Hyperchar.", "n-haltig", "se-Form"</li> <li>X - "KOMMENTAR": created new column merging former columns Z "formen::HELPformbestimmung", AD "lemmata klassisch::HELPbedeutung" and AE "anmerkungen abweichungen"</li> </ul> <p>Columns that were removed due to redundant information:</p> <ul> <li>"formen::zusätzliche merkmale nomen": values "superlative" And "comparative" were renamed "sup." and "comp." and moved to new column R "GRADUS", all other values were moved to new column for miscellaneous W "WEITERE_WERTE"</li> <li>"belege::belegbestimmung summe simpel": values "Ptz.", "ta-Ptz." and "na-Ptz." were moved to new new column P "INFINIT"</li> <li>"belege::kasus bestof"</li> <li>"belege::genus bestof"</li> <li>"belege::numerus bestof"</li> <li>"belege::person bestof"</li> <li>"belege::modus bestof"</li> <li>"belege::tempus bestof"</li> <li>"belege::diathese bestof"</li> <li>"belege::belegbestimmung bestof summe sophistiziert"</li> </ul> <p> </p> <p><strong>Revisions and additions made by Antje Casaretto to the data in 2023:</strong></p> <ul> <li>F-T: - revision and correction (wherever necessary) of all annotations (books 1-7)</li> <li>G,H,I - disambiguation of case forms, reg. pronouns and nominal forms, if unspecified in Zurich data (books 1-7)</li> <li>L - disambiguation of present stem classes (book 7 and book 1 up to line 21050 vers 01.125.01)</li> <li>M - disambiguation of denominal verbs from primary verbs of the 10th class (books 1-10)</li> <li>N - disambiguation of precative and optative forms wherever possible (books 1-7)</li> <li>Q - new annotations for "Int." (intensives) and "Kaus." (causatives) (books 1-7)</li> </ul> <p> </p> <p dir="ltr"><strong>Revisions and additions made by Antje Casaretto to the data in 2024 with support in data modeling and automation by Anna Fischer:</strong></p> <ul> <li>F-T: revision and correction (wherever necessary) of all annotations (books 8-10)</li> <li>G,H,I: disambiguation of case and gender forms in nominal and pronominal forms, if unspecified in Zurich data (books 8-10)</li> <li>L, M: disambiguation of present stem classes (books 1-10)</li> <li>N: disambiguation of precative and optative forms wherever possible (books 8-10)</li> <li>P: new annotations for "Gdv." (gerundives)</li> <li>Q: new annotations for "Den." (denominatives) (books 1-10) and further annotations of “Kaus.” (causatives) and “Int.” (intensives) (books 8-10)</li> <li>T: revision of lemmatization (books 1-10)</li> <li>V: update of meanings according to revised lemmatization; minimal revision</li> <li>W: revised annotation of ending -se (“se-Form”) (books 1-10); no systematic revision</li> <li>X: no systematic revision</li> <li>A-U: general revision of formal inconsistencies and typing errors (book 1-10)</li> </ul> <p> </p> <p dir="ltr"><strong>Revisions made by Natalie Korobzov and Pascal Coenen to the data in 2024 with computational support by Anna Fischer:</strong></p> <ul> <li>Y - "LEMMA_GRASSMANN_ID": new column for references to Grassmann dictionary (books 1-10) and revision of Grassmann references</li> </ul>
FUNCERT - Registration dynamics and course certification rates on a Massive Open Online Course (MOOC) platform
<p>The FUNCERT data set describes 1 million course registration events collected over a 2-year period from the MOOC platform FUN (France Université Numérique, fun-mooc.fr). Each registration event is associated with a timestamp and an indication of whether a completion certificate was ultimately awarded. It was used in the referenced manuscript to model the impact of multiple course registrations on certificate rates.</p> <p>The data set is provided as a single file (FUNCERT.csv) with the following fields.</p> <p><strong>Time (min)</strong> The time of the registration event reported in minutes elapsed since the beginning of data collection. The time stamp is reported as a relative time only, not an exact calendar date, to enhance anonymization.</p> <p><strong>User ID</strong> A unique ID number for each user of the platform during this period. The number has been generated specifically for this data set and does not correspond to user ID numbers used within the FUN platform.</p> <p><strong>Course ID</strong> Each of the 140 offered courses is assigned a unique and anonymous ID code. In some cases courses were offered multiple times within the data collection period. The same Course ID is used across multiple instances of the same course.</p> <p><strong>Certificate</strong> A boolean variable set to 1 if the registration event is associated with a course completion certificate. Of 140 offered courses, 91 awarded certificates during the study period.</p> <p><strong>Age</strong> User age in years at the time of registration. Field may be left blank if data was not provided.</p> <p><strong>Gender</strong> User gender as selected from the options m and f. Field may be left blank if data was not provided.</p>
ABOME: A Multi-platform Data Repository of Artificially Boosted Online Media Entities
<p><strong>Motivation</strong></p> <p>The rise of online media has enabled users to choose various unethical and artificial ways of gaining social growth to boost their credibility (number of followers/retweets/views/likes/subscriptions) within a short time period. In this work, we present ABOME, a novel data repository consisting of datasets collected from multiple platforms for the analysis of blackmarket-driven collusive activities, which are prevalent but often unnoticed in online media. ABOME contains data related to tweets and users on Twitter, YouTube videos, YouTube channels. We believe ABOME is a unique data repository that one can leverage to identify and analyze blackmarket based temporal fraudulent activities in online media as well as the network dynamics.</p> <p><strong>License</strong></p> <p>Creative Commons License.</p> <p><strong>Description of the dataset</strong></p> <p>In this work, we focused on collecting data from credit-based freemium services. We divide the datasets into two parts:</p> <p><strong>- Historical Data (</strong><strong>historical_anon.zip</strong><strong>)</strong></p> <p>This consists of all the data for Twitter and YouTube from blackmarket services gathered via sequential querying of the website’s URLs between the period March-June, 2019. We collected the metadata of each entity present in the historical data.</p> <p><strong>Twitter:</strong></p> <p>We collected the following fields for retweets and followers on Twitter:</p> <p><code>user_details</code>: A JSON object representing a Twitter user.</p> <p><code>tweet_details</code>: A JSON object representing a tweet.</p> <p><code>tweet_retweets</code>: A JSON list of tweet objects representing the most recent 100 retweets of a given tweet.</p> <ol> <li> <p><a href="https://developer.twitter.com/en/docs/tweets/data-dictionary/overview/user-object">https://developer.twitter.com/en/docs/tweets/data-dictionary/overview/user-object</a><a href="#fnref1">β©οΈ</a></p> </li> <li> <p><a href="https://developer.twitter.com/en/docs/tweets/data-dictionary/overview/tweet-object">https://developer.twitter.com/en/docs/tweets/data-dictionary/overview/tweet-object</a><a href="#fnref2">β©οΈ</a></p> </li> </ol> <p><strong>YouTube:</strong></p> <p>We collected the following fields for YouTube likes and comments:</p> <p><code>is_family_friendly:</code> Whether the video is marked as family friendly or not.</p> <p><code>genre:</code> Genre of the video.</p> <p><code>duration:</code> Duration of the video in ISO 8601 format (duration type). This format is generally used when the duration denotes the amount of intervening time in a time interval.</p> <p><code>description:</code> Description of the video.</p> <p><code>upload_date:</code> Date that the video was uploaded.</p> <p><code>is_paid:</code> Whether the video is paid or not.</p> <p><code>is_unlisted:</code> The privacy status of the video, i.e., whether the video is unlisted or not. Here, the flag <em>unlisted</em> indicates that the video can only be accessed by people who have a direct link to it.</p> <p><code>statistics:</code> A JSON object containing the number of dislikes, views and likes for the video.</p> <p><code>comments:</code> A list of comments for the video. Each element in the list is a JSON object of the text (<em>the comment text</em>) and time (<em>the time when the comment was posted</em>).</p> <p>We collected the following fields for YouTube channels:</p> <p><code>channel_description:</code> Description of the channel.</p> <p><code>hidden_subscriber_count:</code> Total number of hidden subscribers of the channel.</p> <p><code>published_at:</code> Time when the channel was created. The time is specified in ISO 8601 format (YYYY-MM-DDThh:mm:ss.sZ).</p> <p><code>video_count:</code> Total number of videos uploaded to the channel.</p> <p><code>subscriber_count:</code> Total number of subscribers of the channel.</p> <p><code>view_count:</code> The number of times the channel has been viewed.</p> <p><code>kind:</code> The API resource type (e.g., <em>youtube#channel</em> for YouTube channels).</p> <p><code>country:</code> The country the channel is associated with.</p> <p><code>comment_count:</code> Total number of comments the channel has received.</p> <p><code>etag:</code> The ETag of the channel which is an HTTP header used for web browser cache validation.</p> <p>The historical data is stored in five directories named according to the type of data inside it. Each directory contains JSON files corresponding to the data described above. <strong>'historical_sample.zip'</strong> contains a small sample of the historical dataset.</p> <p>- <strong>Time-series Data (time_series_anon.zip)</strong></p> <p>This consists of time-series data (collected every 8 hours) of Twitter users and tweets collected from the blackmarket services between the period of March-June, 2019. We collect the following time-series data for retweets and followers on Twitter:</p> <p><code>user_timeline</code>: This is a JSON list of tweet objects in the user’s timeline, which consists of the tweets posted, retweeted and quoted by the user. The file created at each time interval contains the new tweets posted by the user during each time interval.</p> <p><code>user_followers</code>: This is a JSON file containing the user ids of all the followers of a user that were added or removed from the follower list during each time interval.</p> <p><code>user_followees</code>: This is a JSON file consisting of the user ids of all the users followed by a user, i.e., the followees of a user, that were added or removed from the followee list during each time interval.</p> <p><code>tweet_details</code>: This is a JSON object representing a given tweet, collected after every time interval.</p> <p><code>tweet_retweets</code>: This is a JSON list of tweet objects representing the most recent 100 retweets of a given tweet, collected after every time interval.</p> <p>The time-series data is stored in directories named according to the timestamp of the collection time. Each directory contains sub-directories corresponding to the data described above. <strong>'time_series_sample.zip'</strong> contains a small sample of the time series dataset.</p> <p><strong>Data Anonymization</strong></p> <p>The data is anonymized by removing all Personally Identifiable Information (PII) and generating pseud-IDs corresponding to the original IDs. A consistent mapping between the original and pseudo-IDs is maintained to maintain the integrity of the data.</p> <p> </p>
Chimpanzee identification and social Network construction through an online citizen science platform
<p><span><span><span><span><span><span><span><span><span><span><span>Citizen science has grown rapidly in popularity in recent years due to its potential to educate and engage the public while providing a means to address a myriad of scientific questions. However, the rise in popularity of citizen science has also been accompanied by concerns about the quality of data emerging from citizen science research projects. We assessed data quality in the online citizen scientist platform Chimp&See, which hosts camera trap videos of chimpanzees (<i>Pan troglodytes</i>) and other species across Equatorial Africa. In particular, we compared detection and identification of individual chimpanzees by citizen scientists to that of experts with years of experience studying those chimpanzees. We found that citizen scientists typically detected the same number of individual chimpanzees as experts, but assigned far fewer identifications (IDs) to those individuals. Those IDs assigned, however, were nearly always in agreement with the IDs provided by experts. We applied the data sets of citizen scientists and experts by constructing social networks from each. We found that both social networks were relatively robust and shared a similar structure, as well as having positively correlated individual network positions. Our findings demonstrate that, although citizen scientists produced a smaller data set based on fewer confirmed IDs, the data strongly reflect expert classifications and can be used for meaningful assessments of group structure and dynamics. This approach expands opportunities for social research and conservation monitoring in great apes and many other individually identifiable species.Β </span></span></span></span></span></span></span></span></span></span></span></p>
STD-BERTopic Online music platform data
<p>“STD-BERTopic model has been applied to analyze online music platform data. The dataset consists of various user interactions, including how users engage with friends, follow others, and manage privacy settings. The data also reflects behaviors such as blocking users, re-following previously unfollowed users, and issues related to friend lists and visibility after unfollowing. The model extracts trends and topics from short texts, which reveal insights into user experiences, especially concerning friendship and follow functionalities.”</p>
OPTICS: an interactive online platform for photosensory and bio-functional proteins in optogenetic systems
<p>The cutting-edge technology of optogenetics opens to new ideas for control of cellular bio-functional proteins (CPs) using optogenetic tools (OTs) in spatial and temporal. Over the past decade, hundreds of optogenetic systems (OSs) have been constructed for various applications from living cells to freely moving organisms. In this work, a new database named OPTICS (an interactive online platform for photosensory and bio-functional proteins in optogenetic systems) was thus introduced. Our OPTICS is unique in (i) systematically describing diverse OSs from the perspective of photoreceptor-based classification and mechanism of action; (ii) featuring the detailed biophysical properties and functional data of OSs; (iii) providing the interaction between OT and CP of each OS refers to distinct applications in research, diagnosis, and therapy; and (iv) enabling light response property-based search against all OSs in the database. Since the information of OSs is essential for design of optogenetic controls, the comprehensive data provided in OPTICS lay a solid foundation for the future development of novel OSs. The OPTICS is freely accessible at <a href="https://idrblab.org/optics/">https://idrblab.org/optics/</a>.</p>
An Online-platform to Improve Patient-centered Care During the COVID-19 Pandemic: a GIMEMA Surveillance Program in Hematologic Malignancies
ClinicalTrials.gov study NCT04581187. IPD Sharing: Not stated. Countries: 1. Publications: 1.
RESILIENT : A Self-Managed Online Platform for Victims of Sexual Assault
ClinicalTrials.gov study NCT05811897. IPD Sharing: NO. Countries: 1. Publications: 3.
Online Platform for Healthy Weight Loss (POEmaS)
ClinicalTrials.gov study NCT03435445. IPD Sharing: NO. Countries: 1. Publications: 6.
Tailoring an Online Platform to Promote Evidence-Based Care for Adults With Neurofibromatosis 1 and Low Health Literacy
ClinicalTrials.gov study NCT07233408. IPD Sharing: NO. Countries: 1. Publications: 2.
Group intEgrative MINdfulness Over the Internet Using an Online Platform
ClinicalTrials.gov study NCT04340739. IPD Sharing: NO. Countries: 1. Publications: 1.
ATTACHβ’ Online Platform: Helping Children Vulnerable to Early Adversity
ClinicalTrials.gov study NCT05994027. IPD Sharing: NO. Countries: 1. Publications: 11.
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.