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2,015 results for “context”
ROCOv2: Radiology Objects in COntext Version 2, An Updated Multimodal Image Dataset
<p>Recent advances in deep learning techniques have enabled the development of systems for automatic analysis of medical images. These systems often require large amounts of training data with high quality labels, which is difficult and time consuming to generate.</p> <p>Here, we introduce Radiology Object in COntext Version 2 (ROCOv2), a multimodal dataset consisting of radiological images and associated medical concepts and captions extracted from the PubMed Open Access subset. Concepts for clinical modality, anatomy (X-ray), and directionality (X-ray) were manually curated and additionally evaluated by a radiologist. Unlike MIMIC-CXR, ROCOv2 includes seven different clinical modalities.</p> <p>It is an updated version of the ROCO dataset published in 2018, and includes 35,705 new images added to PubMed since 2018, as well as manually curated medical concepts for modality, body region (X-ray) and directionality (X-ray). The dataset consists of 79,789 images and has been used, with minor modifications, in the concept detection and caption prediction tasks of ImageCLEFmedical 2023. The participants had access to the training and validation sets after signing a user agreement.</p> <p>The dataset is suitable for training image annotation models based on image-caption pairs, or for multi-label image classification using the UMLS concepts provided with each image, e.g., to build systems to support structured medical reporting.</p> <p>Additional possible use cases for the ROCOv2 dataset include the pre-training of models for the medical domain, and the evaluation evaluation of deep learning models for multi-task learning.</p>
Student discourse in small-group collaborative contexts in real-world higher education
<p>This dataset contains anonymised student discourse in small-group collaborative contexts in real-world higher education settings.</p> <p>There are 28 sessions, composed of 10799 utterances.</p> <p>The columns consist of 13 columns:</p> <ul> <li>session: the session number</li> <li>start: the starting time of the utterance</li> <li>end: the ending time of the utterance</li> <li>speaker: anonymised speaker name</li> <li>content: the content of the utterance</li> <li>ep: the episode number of the utterance. An episode refers to a single topic of discussion with multiple utterances.</li> <li>C: a binary value indicating the presence or absence of a cognitive challenge, where 0 means no challenge, and 1 means there is a cognitive challenge.</li> <li>E: a binary value indicating the presence or absence of an emotional/motivational challenge, where 0 means no challenge, and 1 means there is an emotional/motivational challenge.</li> <li>M: a binary value indicating the presence or absence of a metacognitive challenge, where 0 means no challenge, and 1 means there is a metacognitive challenge.</li> <li>T: a binary value indicating the presence or absence of a technical/other challenge, where 0 means no challenge, and 1 means there is a technical/other challenge.</li> <li>TA: a binary value indicating the presence or absence of the regulatory process "task analysis," where 0 means no regulation and 1 means task analysis is present.</li> <li>MC: a binary value indicating the presence or absence of the regulatory process "monitoring/control," where 0 means no regulation and 1 means monitoring/control is present.</li> <li>RA: a binary value indicating the presence or absence of the regulatory process "reflection/adaptation," where 0 means no regulation and 1 means reflection/adaptation is present.</li> </ul> <p>This annotated dataset was used in the following paper to model challenge moments.</p> <p>For more details on how the dataset was generated, please refer to the paper.</p> <div> <div>Suraworachet, W., Seon, J., & Cukurova, M. (2024). Predicting challenge moments from students’ discourse: A comparison of GPT-4 to two traditional natural language processing approaches. <em>Proceedings of the 14th Learning Analytics and Knowledge Conference</em>, 473–485. <a href="https://doi.org/10.1145/3636555.3636905">https://doi.org/10.1145/3636555.3636905</a></div> <div> </div> </div>
Systematic review data on the role of urban planning in the context of sustainability transformations and human-nature connections
<p>This data publication belongs to the following research paper:<br>Harms, P., Hofer, M. & Artmann, M. Planning cities with nature for sustainability transformations — a systematic review. Urban Transform 6, 9 (2024). <br>https://doi.org/10.1186/s42854-024-00066-2 </p> <p>We conducted a systematic literature review according to the PRISMA Statement 2020 (Page et al. 2021). The list shows the steps performed and the names of the corresponding datasets available here:</p> <p>Step A - Identification of Records<br>A_01_PRISMA-protocoll.pdf<br>A_02_searchstring.txt<br>A_03_recordsidentified.ris</p> <p>Step B - Screening of Records<br>B_01_recordsscreened-title-keywords.ris<br>B_02_recordsscreened-abstract.ris<br>B_03_recordsscreened-fulltext.ris<br>B_04_studiesincluded.ris<br>B_05_screeningdecisions-overview.xlsx</p> <p>Step C - Qualitative Analysis<br>C_01_codingscheme.xlsx</p> <p> </p>
Research compendium for 'Refitting the Context: A Reconsideration of Cultural Change among Early Homo sapiens at Fumane Cave through Blade Break Connections, Spatial Taphonomy, and Lithic Technology'
<div> <h3>Compendium DOI:</h3> <p><a href="../doi/10.5281/zenodo.10965413">https://zenodo.org/doi/10.5281/zenodo.10965413</a> </p> </div> <p>The content available at the above provided URL will reproduce the results as documented in the publication. Instead, the files hosted at <a href="https://github.com/ArmandoFalcucci/Refitting-The-Context">https://github.com/ArmandoFalcucci/Refitting-The-Context</a> represent the developmental versions and might have undergone modifications since the paper's publication.</p> <div> <h3>Maintainer of this repository:</h3> </div> <p>Armando Falcucci (<a href="mailto:armando.falcucci@uni-tuebingen.de">armando.falcucci@uni-tuebingen.de</a>)</p> <div> <h3>Published paper:</h3> </div> <p>Armando Falcucci, Domenico Giusti, Filippo Zangrossi, Matteo De Lorenzi, Letizia Ceregatti, Marco Peresani. Refitting the Context: Revisiting the Aurignacian sequence at Fumane Cave through blade fragment connections, spatial taphonomy, and lithic technology. <em>Journal of Paleolithic Archaeology</em> (2024). DOI: <a href="https://doi.org/10.1007/s41982-024-00203-0" rel="nofollow">10.1007/s41982-024-00203-0</a></p> <div> <h3>Abstract:</h3> </div> <p>High-resolution stratigraphic frameworks are crucial for unraveling the biocultural processes behind the dispersals of Homo sapiens across Europe. Detailed technological studies of lithic assemblages retrieved from multi-stratified sequences allow archaeologists to precisely model the chrono-cultural dynamics of the early Upper Paleolithic. However, it is of paramount importance to verify the integrity of these assemblages before building explanatory models of cultural change. In this study, multiple lines of evidence suggest that the stratigraphic sequence of Fumane Cave in northeastern Italy experienced minor post-depositional reworking, establishing it as a pivotal site for exploring the earliest stages of the Aurignacian. By conducting a systematic search for break connections between blade fragments and applying spatial analysis techniques, we identified three well-preserved areas of the excavation containing assemblages suitable for renewed archaeological investigations. Subsequent technological analyses, incorporating attribute analysis, reduction intensity, and multivariate statistics, have allowed us to discern the spatial organization of the site during the formation of the Protoaurignacian palimpsest A2–A1. Moreover, diachronic comparisons between three successive stratigraphic units prompted us to reject the hypothesis of techno-cultural continuity of the Protoaurignacian in northeastern Italy after the onset of the Heinrich Event 4. Based on the variability of the lithic and osseous artifacts, the most recent assemblage analyzed, D3b alpha, is now ascribed to the Early Aurignacian, aligning the evidence from Fumane with the current understanding of the development of the Aurignacian across Europe. Overall, this study demonstrates the high effectiveness of the break connection method when combined with detailed spatial analysis and lithic technology, providing a methodological tool particularly amenable to be applied to sites excavated in the past with varying degrees of recording accuracy.</p> <div> <h3>Keywords:</h3> </div> <p>Protoaurignacian; Early Aurignacian; Lithics; Refittings; Assemblage integrity; Spatial analysis; Italy</p> <div> <h3>Overview of contents and how to reproduce:</h3> </div> <p>Within this repository, various folders house data (<code>data</code>), code (<code>script</code>), and output files (<code>output</code>) pertinent to the paper. The data folder encompasses the blank and core datasets from the Aurignacian of Fumane Cave and the dataset of the blade fragment connection study. To replicate the results, download the entire repository and employ <code>Refitting-The-Context.Rproj</code> and open the folder <code>script</code>. For ensuring reproducibility, the <code>renv</code> package (v. 1.0.3) was utilized, following the procedures detailed in its vignette. All analyses and visualizations in the paper were conducted using R 4.3.1 on Microsoft Windows 10.0.19045 (64-bit). As the necessary packages are available in the <code>renv</code> folder, they are not explicitly listed here.</p> <div> <h3>Licenses:</h3> </div> <p>Code: <strong>MIT</strong> <a href="http://opensource.org/licenses/MIT" rel="nofollow">http://opensource.org/licenses/MIT</a>, copyright holder: Armando Falcucci (2024).</p> <p>Data and intellectual work: <strong>Creative Commons Attribution 4.0 International License</strong> (<a href="http://creativecommons.org/licenses/by/4.0/" rel="nofollow">http://creativecommons.org/licenses/by/4.0/</a>), copyright holder: the authors (2024).</p>
Context-Aware Activity Recognition in Logistics (CAARL) – A optical marker-based Motion Capture Dataset
<p><strong>CAARL </strong>is a freely accessible logistics-dataset for human activity recognition, which contains human movement and context information from two subjects. The context information includes the positions of objects such as two picking carts, a packaging table, different racks, a base and three entrances.</p> <p>In the ’Innovationlab Hybrid Services in Logistics’ at TU Dortmund University, two picking and one packing scenarios were recorded using an optical marker based motion capture system. Each subject and object is equipped with several markers. 140 minutes of human movements have been labelled and categorised into 8 activity classes and 19 binary coarse-semantic descriptions, also called attributes. The labelled human movements are synchronised with the context information. They have exactly the same sampling rate (same start and end).</p> <p>The oMoCap data is in csv format. Further formats (e.g. C3D) are available on request.</p> <p>CAARL is based on the set-up and scenarios of the LARa dataset, which contains only human movements. Information about LARa can be found in the dataset and the associated paper:</p> <ul> <li>Dataset: “Logistic Activity Recognition Challenge (LARa) – A Motion Capture and Inertial Measurement Dataset”, Zenodo 2020, DOI: <a href="https://doi.org/10.5281/zenodo.3862782">10.5281/zenodo.3862782</a></li> <li>Paper: “LARa: Creating a Dataset for Human Activity Recognition in Logistics Using Semantic Attributes”, Sensors 2020, DOI: <a href="https://doi.org/10.3390/s20154083">10.3390/s20154083</a></li> </ul> <p> </p> <p><strong>If you use the CAARL dataset for research, please cite the following paper: “Context-Aware Human Activity Recognition in Industrial Processes”, Sensors 2021, DOI: <a href="https://doi.org/10.3390/s22010134">10.3390/s22010134</a></strong></p>
Towards new demography proxies and regional chronologies: Radiocarbon dates from archaeological contexts located in the Czech Republic covering the period between 10,000 BC and AD 1250 (dataset)
<p>The dataset was created within the project “<em>Land use, social transformations and woodland in Central European Prehistory. Modelling approaches to human-environment interactions</em>” funded by the Czech Science Foundation (19-20970Y). This dataset represents the largest and the most comprehensive collection of archaeological radiocarbon dates from the Czech Republic to date. The dataset offers 1579 samples from 347 archaeological sites dating from Early Mesolithic (10 000 BC) to Medieval Period (AD 1250). Published in a simple spreadsheet format, the database offers researchers a quick tool for further analyses. It is important to highlight that dates we collected originated only from archaeological contexts, which means that we have excluded some radiocarbon dates produced through palaeoecological research without a direct relationship to past human activities, such as pollen records or samples from fossilized trees in river beds. The dataset is intended to be used for demographic modelling of population numbers during periods without written records, i.e. prehistory.</p>
Covid-19 et Intention d'utiliser les chèques psycho par les étudiants : la prise en compte du contexte dans la théorie du comportement planifié
<p>Cette base de données est issue d’une enquête quantitative par questionnaire (nombre d’observations = 460, période : mars 2021). Elle est construite sur la base de la théorie du comportement planifié. La variable finale que le modèle cherche à expliquer (la variable dépendante) est l’intention d’utiliser les chèques psycho, dispositif proposé par le gouvernement français en février 2021, par les étudiants. Ces chèques permettent aux étudiants de bénéficier de 3 consultations auprès d’un psychologue conventionné, pour un montant total de 96 €.</p> <p>Les objectifs (et les utilisations possibles) de cette BDD sont autant orientés vers les besoins des acteurs (notamment ceux en charge de la mise en œuvre du dispositif « chèque psycho » pour les étudiants ou de dispositifs similaires) que des chercheurs.</p> <p>L’objectif opérationnel est de mesurer (statistiques descriptives) et de comprendre (statistiques explicatives) les facteurs qui conduisent des étudiants à envisager d’utiliser ce dispositif ; et donc à pouvoir comprendre comment agir pour optimiser cette utilisation. L’extension récente (juin 2021) de ce dispositif aux enfants et adolescents de 3 à 17 ans (dispositif « PsyEnfantAdo ») induit un deuxième objectif opérationnel : la possibilité pour les acteurs en charge de ce nouveau dispositif de s’inspirer de la méthodologie présentée dans cette base pour piloter au mieux ce projet.</p> <p>L’objectif théorique réside a) dans la prise en compte de l’impact d’un contexte (le vécu des étudiants durant la pandémie de la Covid-19 (t leur antécédents au niveau psychologique dans la théorie du comportement planifié (TCP) et b) dans la confirmation de la place de l’identité personnelle en tant que mesure alternative de l’intention comportementale (et non en tant que variable explicative de cette intention). Les chercheurs pourront également utiliser cette base dans des méta-analyses sur la TCP, la prise en compte du contexte dans la compréhension de l’intention comportementale et les impacts de la Covid-19.</p>
Environmental context observed during the Tara Pacific Expedition 2016-2018, simplified version at site level
<p>This dataset provide a site level-compilation of previous datasets provided at the event level (see https://zenodo.org/record/6445609#.YlP8B5NByEA). In some cases, certain parameters were not available at specific sampling sites due to technical issues or sensor availability, however, various basin scale studies and statistical tests require a complete dataset for all sampled sites. During the Tara Pacific expedition, many parameters were concurrently measured in-situ, estimated from remote sensing and/or modeled. For instance, sea surface temperature was measured on the boat using the thermosalinograph included in the underway system, but also with satellite and estimated from a model. Each of these three modes of acquisition have their caveat and accuracy, however, within a certain confidence interval, missing in-situ data can be replaced by its remotely sensed or modeled equivalent. We provide here a simplified version at the sampling site level by replacing missing in-situ data by their closest and most accurate satellite or modeled equivalent. In each case, in-situ data was considered as the most accurate source of data, with a preference to HPLC pigments analysis followed by measurements done by the ACS, while satellite and modeled data were used only if in-situ data was not available. We evaluated the accuracy of ACS and of each satellite and modeled datasets by linear regressions with their in-situ counterparts. A bias of the modeled or satellite data was identified when the slope of the regression was different to 1 and/or an intercept was different to 0. The satellite and modeled data were forced to match the in-situ data by dividing by the slope and subtracting the intercept. This is the case for SST. When large bias persisted between matchups with observations, the corrected data was not used to replace missing in-situ data. This is the case for chl. The same approach was then applied to fill missing data with modeled values (MERCATOR-Copernicus).</p> <p>A correction for the bias in the following variable was applied for SST, SSS, PO4, and SiOH. As previously done, if large bias persisted between observations and corrected data, they were not used to replace missing in-situ data. This is the case for chl, NO3, and Fe.</p> <p>The [MTE] samples were sometimes sampled in the afternoon instead of the morning alongside all the other water samples, thus were located in between two sampling stations. These [MTE] samples could not be assigned to a sampling station following the criterion presented in the section 3, therefore, the missing values of the corresponding morning stations were interpolated linearly.</p> <p>The same approach was used for pH measurements, with a preference from measurements provided by total carbonate system quantifications, followed by direct pH measurements and then modeled values (MERCATOR-Copernicus).</p>
eDNAbyss samples provenance and environmental context - version 1
<p>This is a tabular version of the eDNAbyss sample provenance & environmental context metadata submitted to BioSamples (<a href="https://www.ebi.ac.uk/biosamples/samples?text=eDNAbyss">https://www.ebi.ac.uk/biosamples/samples?text=eDNAbyss</a>).</p>
Test Stimuli for Context Based Evaluation of the OPUS Audio Codec
<p>A dataset that includes the 360° video .mp4 files and Ambisonic audio .wav files used in the listening tests for "Context Based Evaluation of the OPUS Audio Codec for Spatial Audio Content in Virtual Reality".</p>
Context-based Entity Recommendation on Real-Life Knowledge Work in Context (RLKWiC dataset)
<h2><a href="../records/11059573">RLKWiC</a> Add-on: Benchmarking Dataset for Entity Recommendation</h2> <p>This benchmark, built on top of the Real-Life Knowledge Work in Context (<a href="../records/11059573">RLKWiC</a>) dataset, is designed to evaluate context-based entity recommendation by simulating a scenario where participants receive entities extracted from their activities across their defined contexts. </p> <p>In total, 1850 entity recommendations were generated across 56 contexts. After deduplication, these entities were presented to participants for explicit relevance assessment on a 3-point scale:</p> <ul> <li>0 [Irrelevant]: Signifying a lack of relevance between the recommended entity and the context.</li> <li>1 [Relevant] Denoting a connection between the entity and the context, although it may not fully represent it.</li> <li>2 [Representative]: The entity closely aligns with the context, indicating a high level of relevance where the context can be inferred to be about this entity.</li> </ul> <p>Participants could also suggest additional relevant entities. The resulting dataset comprises 1067 entities with explicit relevance scores, offering a resource for benchmarking entity recommendation in real-life knowledge work.</p> <h3><strong>Paper: </strong><a href="https://dl.acm.org/doi/10.1145/3640457.3688068" target="_blank" rel="noopener">Context-based Entity Recommendation for Knowledge Workers: Establishing a Benchmark on Real-life Data</a></h3>
Context-Aware 3D Object Anchoring for Mobile Robots Dataset
<p>This dataset accompanies the following publication:</p> <p>Günther, M.; Ruiz-Sarmiento, J. R.; Galindo, C.; González-Jiménez, J. & Hertzberg, J. <strong>Context-Aware 3D Object Anchoring for Mobile Robots.</strong> <em>Robot. Auton. Syst.</em>, 2018 (accepted)</p> <p>The dataset consists of 15 scenes inspected by a robot equipped with a RGB-D camera driving around a table and turning towards it from different locations. The table contained a number of objects in varying table settings. In total, the dataset contains 1387 seconds of observation and 144 unique objects from 9 categories:</p> <ul> <li>SugarPot</li> <li>MilkPot</li> <li>CoffeeJug</li> <li>MobilePhone</li> <li>Mug</li> <li>Dish</li> <li>Fork</li> <li>Knife</li> <li>Spoon</li> <li>TableSign</li> </ul> <p>Segmentation, tracking and local object recognition was run on the recorded sensor data, and its output (tracked objects and local recognition results) was added to the dataset. Since the objects were observed from multiple perspectives and tracking was lost while the robot was moving from one observation pose to another, the dataset contains more than one track ID for most objects (one for each subsequent observation of the object). Each track ID was manually labeled with the ground truth category of the object it represented. Additionally, all track IDs belonging to the same object were manually grouped together to allow evaluation of the anchoring process. Track IDs that did not correspond to any object on the table (but instead to objects on different tables, pieces of the table itself or other artifacts) were manually removed. In total, out of 432 track IDs, 410 (94.9 %) were associated with true objects, while 22 (5.1 %) were removed as artifacts.</p> <p><br> <strong>File contents</strong></p> <p>All data is provided as rosbags. The naming scheme is as follows:</p> <ul> <li>`*-sensordata.bag.bz2`: The raw sensor data from the robot and all transform data, including localization in a map.</li> <li>`*-perception.bag.bz2`: The object recognition results and ground truth information for the tracked objects.</li> <li>`scene??-pr2-*.bag.bz2`: 5 scenes that were recorded using the PR2 robot.</li> <li>`scene??-calvin-*.bag.bz2`: 10 scenes that were recorded using the Calvin robot.</li> </ul> <p>Both robots used an ASUS Xtion Pro Live as 3D camera.</p> <p>`race_vision_msgs.tar.bz2`: The custom messages used in the `-perception` rosbags, as a ROS Kinetic package.</p> <p><br> <strong>Videos</strong></p> <p>To get a first impression of the dataset, `scene10.mp4` and `scene19.mp4` show the corresponding scenes from the point of view of the robot's RGB camera.</p>
Chapter 3: Scale Separation Reliability: What Does it Mean in the Context of Comparative Judgement?
<p>This is the supplementary material for Chapter 3 of the dissertation "Beyond a Mere Rank Order: The Method, the Reliability and the Efficiency of Comparative Judgment" and the article "Scale separation reliability: What does it mean in the context of comparative judgment?" published in "Applied Psychological Measurement".</p>
RUSSE'2018: Human-Annotated Sense-Disambiguated Word Contexts for Russian
<p>This dataset contains human-annotated sense identifiers for 2562 contexts of 20 words used in the <a href="https://russe.nlpub.org/2018/wsi/">RUSSE'2018</a> shared task on Word Sense Induction and Disambiguation for the Russian language; part of the <em>bts-rnc</em> evaluation dataset. These sense identifiers are disambiguated as according to the sense inventory of the <a href="http://gramota.ru/slovari/info/bts/">Large Explanatory Dictionary of Russian</a>.</p> <p>The annotation is done on December 1, 2017, on the <a href="https://tolokanyandex.com/">Yandex.Toloka</a> crowdsourcing platform. In particular, 80 pre-annotated contexts are used for training the human annotators, 2562 contexts are annotated by humans such that each context was annotated by 9 different annotators. The annotation reliability is indicated by a high value of Krippendorff's α = 0.83. After the annotation, every context was additionally inspected (“curated”) by the organizers of the shared task.</p> <p>The following words are represented: <em>акция</em> (action / stock), <em>байка</em> (yarn / tale), <em>гвоздика</em> (carnation / nail), <em>гипербола</em> (hyperbole), <em>град</em> (avalanche), <em>гусеница</em> (grub), <em>домино</em> (domino), <em>кабачок</em> (marrow / pub), <em>капот</em> (hood), <em>карьер</em> (mine / career), <em>кок</em> (cook), <em>крона</em> (top / crown), <em>круп</em> (croup), <em>мандарин</em> (mandarine), <em>рок</em> (fate / rock), <em>слог</em> (syllable), <em>стопка</em> (glass, stack), <em>таз</em> (bowl), <em>такса</em> (rate / badger-dog), <em>шах</em> (shah / check).</p> <p>The following files are included in this dataset:</p> <ul> <li>Toloka assignments (training: <em>tasks-train.tsv</em>, annotation: <em>tasks-test.tsv</em>)</li> <li>Toloka output (non-aggregated: <em>assignments_01-12-2017.tsv.xz</em>, aggregated: <em>aggregated_results_pool_1036853__2017_12_01.tsv</em>)</li> <li>annotator agreement report (<em>agreement.txt</em>)</li> <li>curated report (<em>report-curated.tsv.xz</em> and a supplementary file <em>tasks-eval.tsv.xz</em>)</li> <li>the final aggregated dataset (<em>bts-rnc-crowd.tsv</em>)</li> </ul> <p>The <em>bts-rnc-crowd.tsv</em> file has the following format: <em>id</em>, <em>lemma</em>, <em>sense_id</em>, <em>left</em> hand side context, <em>word</em> form, <em>right</em> hand side context, list of <em>senses</em>. The encoding is UTF-8 and the line breaks are LF (UNIX).</p>
Integrated pedagogical methods effectiveness in Physics' preliminary undergraduate education within the context of large size lectures.
<p>Three files relating the first round of analysis testing active methods for large size lectures. The Presentation including the research design, methods, main results in synthesis is available here: https://www.researchgate.net/project/Getting-started-with-Physics-preliminary-undergraduated-strategies/update/5a44cac6b53d2f0bba475104</p> <p>2- Dataset on Students' Learning Outcomes. Dataset adopted in the first experimental round. The dataset includes data used for the first type of analysis (learning outcomes) carried out for the ICEM2017 Conference presentation "Integrating MOOCs in Physics preliminary undergraduate education: beyond large size lectures". The data includes the results of the initial, baseline Test, the final Test, and two other variables that could be used to analyse covariance: Sex and Type of Group (Large/Small).</p> <p>3- Dataset on Students' Opinion. Dataset adopted in the first experimental round. The dataset includes data used for the second type of analysis (students' opinion) carried out for the ICEM2017 Conference presentation "Integrating MOOCs in Physics preliminary undergraduate education: beyond large size lectures". The data includes the results of a final questionnaire gathering the students opinion on the four types of pedagogical factors affecting their experience within a large size lecture: MOOCs, Active Learning, Self-Assessment tools, Tutors’ guidance.</p> <p>4- Codes and analysis adopted in the first experimental round. The Document includes two analysis carried on for the ICEM2017 Conference presentation "Integrating MOOCs in Physics preliminary undergraduate education: beyond large size lectures". These are: Test (measuring students' knowledge on the subject taught) and Students' Opinion/satisfaction on the several pedagogical methods adopted along the experimental intervention.</p>
Context-Aware Dataset: STS - South Tyrol Suggests IoT Mobile App Data
<p><strong>STS dataset </strong>was collected by a context-aware recommender system mobile app named as<strong> <a href="https://play.google.com/store/apps/details?id=it.unibz.sts.android&hl=en">"South Tyrol Suggests"</a></strong>. The app provides <strong>context-aware recommendations</strong> for attractions, events, public services, restaurants, and much more based on the rating preferences and personality factors of users.</p> <p><strong>Contextual</strong> <strong>variables</strong> includes </p> <ul> <li><strong>distance:</strong> far away, near by</li> <li><strong>time available:</strong> half day, one day, more than one day</li> <li><strong>temperature:</strong> burning, hot, warm, cool, cold, freezing</li> <li><strong>crowdedness:</strong> crowded, not crowded, empty</li> <li><strong>knowledge of surroundings:</strong> new to area, returning visitor, citizen of the area</li> <li><strong>season:</strong> spring, summer, autumn, winter</li> <li><strong>budget:</strong> budget traveler, price for quality, high spender</li> <li><strong>daytime:</strong> morning, noon, afternoon, evening, night</li> <li><strong>weather:</strong> clear sky, sunny, cloudy, rainy, thunderstorm, snowing</li> <li><strong>companion:</strong> alone, with friends/colleagues, with family, with girlfriend/boyfriend, with children</li> <li><strong>mood:</strong> happy, sad, active, lazy weekday: weekday, weekend</li> <li><strong>travel goal:</strong> visiting friends, business, religion, health care, social event, education, scenic/landscape, hedonistic/fun, activity/sport</li> <li><strong>means of transport:</strong> no transportation means, a bicycle, a car, public transport</li> </ul> <p>More details can be found here:</p> <p><em>Braunhofer, Matthias, Mehdi Elahi, and Francesco Ricci. <a href="https://www.researchgate.net/profile/Mehdi_Elahi2/publication/283502363_Techniques_for_cold-starting_context-aware_mobile_recommender_systems_for_tourism/links/56ccaa7608ae059e37507cc0.pdf">"<strong>Techniques for cold-starting context-aware mobile recommender systems for tourism</strong>."</a> Intelligenza Artificiale 8, no. 2 (2014): 129-143.</em></p>
Consensus models to predict oral rat acute toxicity and validation on a dataset coming from the industrial context
<p>We report predictive models of acute oral systemic toxicity representing a follow-up of our previous work in the framework of the NICEATM project. It includes the update of original models through the addition of new data and an external validation of the models using a dataset relevant for the chemical industry context. A regression model for LD50 and classification model for toxicity classes according to the Global Harmonized System categories were prepared. ISIDA descriptors were used to encode molecular structures. Machine learning algorithms included Support Vector Machine (SVM), Random Forest (RF) and Naïve Bayesian. Selected individual models were combined in consensus.</p> <p>The different datasets were compared using the Generative Topographic Mapping approach. It appeared that the NICEATM datasets were lacking some relevant chemotypes for chemical industry. The new models trained on enlarged data sets have applicability domain (AD) sufficiently large to accommodate industrial compounds. The fraction of compounds inside the models’ AD increased from 58 % (NICEATM model) to 94 % (new model). Yet, the increase of training sets only slightly improved of the models’ prediction performance: RMSE values decreased from 0.56 to 0.47 and balanced accuracies increased from 0.69 to 0.71 for NICEATM and new models, respectively.</p>
Powering the Circular Future: Climate Change and Economic Perspectives on Second-Life Batteries in the Belgian Context - Supporting Information S2 and S3
<p>The data contains the databases used to calculate the climate change impacts of second-life batteries including full Life Cycle Inventory data published in the article entitled "Powering the Circular Future: Climate Change and Economic Perspectives on Second-Life Batteries in the Belgian Context".</p> <p>The second file S3 contains the economic data and the climate change impacts of the same article.</p> <p>In version 2.0 of S2, a sensitivity analysis and more detail is added in the results.</p> <p> </p>
Stimulating Wnt signaling reveals context-dependent genetic effects on gene regulation in primary human neural progenitors
<p>Summary statistics for chromatin accessibility and gene expression quantitative trait loci (ca/eQTLs) from Matoba, N., Le, B.D., Valone, J.M. <em>et al.</em> Stimulating Wnt signaling reveals context-dependent genetic effects on gene regulation in primary human neural progenitors. <em>Nat Neurosci</em> (2024). https://doi.org/10.1038/s41593-024-01773-6</p>
Visualization and perception of data gaps in the context of Citizen Science projects: Gradation of Reporting Activity
<p>Online experiment about the influence of different numbers of levels of representation of reporting activity (total number of reports for all birds in the given time span and region) on proportion of correct responses and subjective evaluation of the task (NASA-TLX). Effects of representation with three (3) levels and effects of representation with five (5) levels are investigated. Two groups of members of ornitho.de were tested: experts - persons with access to database (more than 10 reports per month in average) and novices - persons without access to database (less than 10 reports per month in average). Two different tasks were given. The evaluation of statements on a map and the selection of grid fields that met a given requirement.</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.