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.
2,015
datasets available to search
ShareScore release 0.9.0
Dataset results
2,015 results for “context”
Dataset: Context Therapeutics Inc. (CNTX) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Biomass production of experimental grassland communities in urban context within LandKlif project
<p><span>Mixtures of grassland communities (three different compositions) were sown in urban experimental areas to compare in biodiversity and functioning to standard urban lawns (56 plots, 2 x 4 m each) in Munich and Weihenstephan in 2020. Two samples (20 x 20 cm) per experimental plot were clipped in August 2021, sorted by functional type, oven-dried and weighed. For integrity of the database, all field experimental units of this study are associated to Plot ID 7835_1_U</span>, but the variable PlotID is deprecated.</p> <p>LandKlif is funded by the Bavarian State Ministry of Science and the Arts within the Bavarian Climate Research Network (bayklif). Within the five year funding period of bayklif, five interdisciplinary senior research associations and five junior research groups are be financed with a total sum of 18 million Euro. LandKliF, as one of the five interdisciplinary senior research associations, addresses the effects of climate change on biodiversity and ecosystem services in semi-natural, agricultural and urban landscapes.</p>
Lost in Translation? Not for Large Language Models: Automated Divergent Thinking Scoring Performance Translates to Non-English Contexts (Datasets)
<p>Datasets for: Zielińska, A., Organisciak, P., Dumas, D., & Karwowski, M. (2023). Lost in translation? Not for large language models: Automated divergent thinking scoring performance translates to non-English contexts. <em>Thinking Skills and Creativity, 50</em>, 101414. https://doi.org/10.1016/j.tsc.2023.101414</p>
Data for: Environment-dependent relationships between corticosterone and energy expenditure during reproduction: insights from seabirds in the context of climate change
<p>We studied the relationship between baseline levels of the steroid hormone corticosterone and daily energy expenditure (DEE) in the little auk (<em>Alle alle</em>), an Arctic sea bird that is experiencing mounting energetic challenges due to climate change. We specifically investigated the hypothesis that there might be environment-dependent relationships between baseline corticosterone, DEE, time activity budgets, diving behavior and fitness-related traits (chick provisioning rate, adult body condition). Furthermore, we also examined whether mercury (Hg) contamination might interfere with corticosterone production, and hence potentially the capacity to upregulate DEE. In addition, we performed a phylogenetically controlled analysis across breeding seabird species to assess the relationship between baseline corticosterone and DEE, which we estimated via <span>a model derived from a phylogenetically controlled meta-analysis, </span><span>available within a <span>web-based app (‘Seabird FMR Calculator’, </span></span><span><a href="https://ruthedunn.shinyapps.io/seabird_fmr_calculator/"><span>https://ruthedunn.shinyapps.io/seabird_fmr_calculator/</span></a></span><span>) (Dunn et al. 2018). These datasets contain information on corticosterone levels, DEE, TABs and Hg in little auks, and the data used in our phylogenetically controlled analysis. Please see the READ me file for details.</span></p>
Processed data and summary tables for 'Context TFs establish cooperative environments and mediate enhancer communication'
<p>The linked datasets contain processed data files and summary tables for 'Context transcription factors establish cooperative environments and mediate enhancer communication'. Raw sequenceing data for STARR-seq expeeriments can be found at GEO (Accession ID: GSE229646). For a detailed description of files please refer to the README.</p>
FIG. 6 in Animal remains in mortuary contexts in Southern Patagonia: a case study in lake Salitroso (Santa Cruz, Argentina)
FIG. 6. — Cases with faunal associations. Circle mark faunal remains: A, SAC 1-7, Individual 1 with guanaco (Lama guanicoe Müller, 1776) bone on his chest and guanaco and lesser rhea (Rhea pennata d'Orbigny, 1834) bones by his knees;B, SAC 1-8, Individual 1 with associated Diplodon chilensis (J.E. Gray, 1828) valves; C, SAC 1-1, Individual 5 with huemul (Hippocamelus bisulcus Molina, 1782) bones in his mouth.Photos credits: The authors.
FIG. 5 in Animal remains in mortuary contexts in Southern Patagonia: a case study in lake Salitroso (Santa Cruz, Argentina)
FIG. 5. — Correspondence analysis of taphonomic and processing mark variables in chenques and domestic open-air sites in northwest Santa Cruz. References: CHENQUES, this paper;CP2OA, ILB, Rindel 2009; GSLN, LPA1, LPA2, LS2, LS3C1, LS5, MS1M4, MS3S3, Bourlot 2009. Abbreviations:CP2OA, Cerro Pampa 2 Ojo de Agua; cut, cutmarks;GSLN, Grippa Sí Litto No; ILB, Istmo Lago Belgrano; LPA1, La Primera Argentina 1; LPA2, La Primera Argentina 2; LS2, La Siberia 2; LS3C1, La Siberia 3 concentración 1; LS5, La Siberia 5;MS1M3, Médanos Sur 1 sondeo 3;MS1M4, Médanos Sur 1 muestreo 4;MS3S3, Médanos Sur 3 sondeo 3;NTAXA, number of species; pe, percussion; W0-5, wheathering stages.
FIG. 3. — A in Animal remains in mortuary contexts in Southern Patagonia: a case study in lake Salitroso (Santa Cruz, Argentina)
FIG. 3. — A, Bone retoucher from Lake Salitroso burial; B, huemul (Hippocamelus bisulcus Molina, 1782) distal metapodials with percussion marks; C, choique (Rhea Brisson, 1760) distal tibiatarsal with percussion marks. Scale bars: 1 cm. Photos credits: The authors.
FIG. 1 in Animal remains in mortuary contexts in Southern Patagonia: a case study in lake Salitroso (Santa Cruz, Argentina)
FIG. 1. — Location of Lake Salitroso and all other archaeological burial sites mentioned in this article: 1, Aquihuecó; 2, Caepe Malal 1; 3, Rebolledo Arriba; 4, Quilachanquil; 5, Chimpay; 6, Puesto El Rodeo 17; 7, Heupel 1; 8, Puerto Ing. Ibáñez 11; 9, Juni Aike 6; 10, San Gregorio 11. Credit: The authors.
FIG. 2 in Animal remains in mortuary contexts in Southern Patagonia: a case study in lake Salitroso (Santa Cruz, Argentina)
FIG. 2. — Burial types of Lake Salitroso Basin:A, niches;B, burials under blocks; C, chenques. Photos credits: The authors.
FIG. 4 in Animal remains in mortuary contexts in Southern Patagonia: a case study in lake Salitroso (Santa Cruz, Argentina)
FIG. 4. — Evidence of processing per taxa per burial (% NISP [number of identified specimens] with human modifications): A, guanaco Lama guanicoe Müller, 1776; B, fox Lycalopex Burmeister, 1856; C, lesser rhea Rhea pennata d'Orbigny, 1837; D, huemul Hippocamelus bisulcus Molina, 1782; E, Magellan goose Chloephaga picta (Gmelin, 1789). Abbreviation: SAC, Sierra Colorada sites.
BRAIN Journal-Developing Distance Learning Environments in the Context of Cross-Border Cooperation-Figure 5. Weekly session stats for 2016
<p>On following figures, statistic usage is given as Monthly and Weekly statistic for individual users and sessions. The number of the individual users and of the sessions is far better for the 2015 period, especially for the extent of time until the end of June. This is reasonable because it was during the project and the site is frequently put forward in promotional conferences, in press, in direct contacts with schools, and companies. After that period the site was not promoted additionally, and the only pointer to the site is a number of links found on our institutional websites as one of the services we are offering to the students. Considering all that, the figures for 2015 and even 2016, seems to be quite satisfactory. (Figure 1, Figure 4, and Figure 5).</p>
BRAIN Journal-Developing Distance Learning Environments in the Context of Cross-Border Cooperation-Figure 3. Monthly user/session stats for 2016
<p>It is also interesting to observe that in Figure 3, where the usage for 2016 is presented, that in the non-promotional period, the site is mostly used in January (before January exam term), in April, May, and June (before the June exam session and during colloquial exams) or in July and August (before the September exam session).</p>
BRAIN Journal-Developing Distance Learning Environments in the Context of Cross-Border Cooperation-Figure 2. Monthly user/session stats for 2015
<p>On following figures, statistic usage is given as Monthly and Weekly statistic for individual users and sessions. The number of the individual users and of the sessions is far better for the 2015 period, especially for the extent of time until the end of June. This is reasonable because it was during the project and the site is frequently put forward in promotional conferences, in press, in direct contacts with schools, and companies. After that period the site was not promoted additionally, and the only pointer to the site is a number of links found on our institutional websites as one of the services we are offering to the students. Considering all that, the figures for 2015 and even 2016, seems to be quite satisfactory. (Figure 1, Figure 4, and Figure 5). </p>
BRAIN Journal-Developing Distance Learning Environments in the Context of Cross-Border Cooperation-Figure 1. Components of EduWebCast System
<p>The aim of this partnership would be to implement an infrastructure for live and on-demand video streaming of learning material for the targeted groups and, to this purpose, to establish a long and fruitful cooperation between teachers, pupils, and students on both sides of the border. The joint creation and administration of the webcast project is the ground stone of the partnership between the two universities and will result in more common projects based on the materials obtained through the project, contests between pupils and students, possible periodic educational exchanges. </p>
Beyond Throughput: a 4G LTE Dataset with Channel and Context Metrics
<p>The following provides a 4G trace dataset composed of client-side cellular key performance indicators (KPIs) collected from two major Irish mobile operators, across different mobility patterns (static, pedestrian, car, tram and train). The 4G trace dataset contains 135 traces, with an average duration of fifteen minutes per trace, with viewable throughput ranging from 0 to 173 Mbit/s at a granularity of one sample per second. Our traces are generated from a well-known non-rooted Android network monitoring application, G-NetTrack Pro. This tool enables capturing various channel related KPIs, context-related metrics, downlink and uplink throughput, and also cell-related information.</p> <p>To supplement our real-time 4G production network dataset, we also provide a synthetic dataset generated from a large-scale 4G ns-3 simulation that includes one hundred users randomly scattered across a seven-cell cluster. The purpose of this dataset is to provide additional information (such as competing metrics for users connected to the same cell), thus providing otherwise unavailable information about the eNodeB environment and scheduling principle, to end user. In addition to this dataset, we also provide the code and context information to allow other researchers to generate their own synthetic datasets.</p>
Dataset used in "Free context smartphone based application for motor activity levels recognition"
<p>This is the data set used in the paper "Free context smartphone based application for motor activity levels recognition", 2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI), Bologna, 2016, pp1-4.</p> <p>The data refer to three subjects (i.e. subject1, subject2 and subject3). For each subject a folder is created. The folder contains data used for training and for test in all the conditions addressed by the reference paper.</p> <p>Activities are labeled by the last character of the filename as follows: 1-2 resting; 3-6 walking; 7-8 running; 9-12 climbing stairs</p>
A Factor Analysis Model for Dimension Reduction of Outcome Factors in Neonatal Seizure Context-Figure 2. Identified risk factors hierarchy
<p>AED - antiepileptic drug, CP - cerebral palsy, GDD - global developmental delay, GA - gestational age, BW- birth weight, RS - repeated/recurrent seizure, MD - type/mode of delivery, AS1 - Apgar score at 1 minute, AS5 - Apgar score at 5 minute, AS10 - Apgar score at 10 minute, SO - seizure onset, ST_EPI - status epilepticus, UBS - ultrasound brain scan, MSU- maternal substance used, MIS – maternal inflammatory state, PRM- prolonged rupture of membranes, PNN – postnatal neuroimaging, PNS – postnatal seizure. The most frequently identified risk factors were the EEG findings (abnormal / severe electroencephalogram results), seizure characteristics (type, onset, duration, semiology), etiology, birth weight, Apgar score, cerebral ultrasound scan findings (abnormal) (Figure 2).</p>
A Factor Analysis Model for Dimension Reduction of Outcome Factors in Neonatal Seizure Context-A Factor Analysis Model for Dimension Reduction of Outcome Factors in Neonatal Seizure Context
<p>R retrospective, P prospective, CC case – control study, MC multicenter controlled trail, PB populational based, HB hospital based, C clinical, CT computed tomographic scan, MRI cerebral magnetic resonance imaging, CUS cranial ultrasonography / cerebral ultrasound, USG ultrasonography, EEG electroencephalogram (standard), CpH cord Ph, BpH blood Ph, HT therapeutic hypothermia It can be noticed that seizure diagnosis was based on clinical grounds and functional explorations naming neuroimaging and/or EEG procedures (conventional EEG, aEEG, vEEG, CUS, MRI). The minimum number of newborns considered in these studies was 55, while the maximum was 403 with a mean of 148 (SD=86.75, median=112, IQR: (98,175)) and a total of 2226 evaluated cases.</p>
#nowplaying-RS: A New Benchmark Dataset for Building Context-Aware Music Recommender Systems
<p>Music recommender systems can offer users personalized and contextualized recommendation and are therefore important for music information retrieval. An increasing number of datasets have been compiled to facilitate research on different topics, such as content-based, context-based or next-song recommendation. However, these topics are usually addressed separately using different datasets, due to the lack of a unified dataset that contains a large variety of feature types such as item features, user contexts, and timestamps. To address this issue, we propose a large-scale benchmark dataset called #nowplaying-RS, which contains 11.6 million music listening events (LEs) of 139K users and 346K tracks collected from Twitter. The dataset comes with a rich set of item content features and user context features, and the timestamps of the LEs. Moreover, some of the user context features imply the cultural origin of the users, and some others—like hashtags—give clues to the emotional state of a user underlying an LE. In this paper, we provide some statistics to give insight into the dataset, and some directions in which the dataset can be used for making music recommendation. We also provide standardized training and test sets for experimentation, and some baseline results obtained by using factorization machines.</p> <p>The dataset contains three files:</p> <ul> <li>user_track_hashtag_timestamp.csv contains basic information about each listening event. For each listening event, we provide an id, the user_id, track_id, hashtag, created_at </li> <li>context_content_features.csv: contains all context and content features. For each listening event, we provide the id of the event, user_id, track_id, artist_id, content features regarding the track mentioned in the event (instrumentalness, liveness, speechiness, danceability, valence, loudness, tempo, acousticness, energy, mode, key) and context features regarding the listening event (coordinates (as geoJSON), place (as geoJSON), geo (as geoJSON), tweet_language, created_at, user_lang, time_zone, entities contained in the tweet).</li> <li>sentiment_values.csv contains sentiment information for hashtags. It contains the hashtag itself and the sentiment values gathered via four different sentiment dictionaries: AFINN, Opinion Lexicon, Sentistrength Lexicon and vader. For each of these dictionaries we list the minimum, maximum, sum and average of all sentiments of the tokens of the hashtag (if available, else we list empty values). However, as most hashtags only consist of a single token, these values are equal in most cases. Please note that the lexica are rather diverse and therefore, are able to resolve very different terms against a score. Hence, the resulting csv is rather sparse. The file contains the following comma-separated values: <hashtag, vader_min, vader_max, vader_sum,vader_avg, afinn_min, afinn_max, afinn_sum, afinn_avg, ol_min, ol_max, ol_sum, ol_avg, ss_min, ss_max, ss_sum, ss_avg >, where we abbreviate all scores gathered over the Opinion Lexicon with the prefix 'ol'. Similarly, 'ss' stands for SentiStrength. </li> </ul> <p>Please also find the training and test-splits for the dataset in this repo. Also, prototypical implementations of a context-aware recommender system based on the dataset can be found at <a href="https://github.com/asmitapoddar/nowplaying-RS-Music-Reco-FM">https://github.com/asmitapoddar/nowplaying-RS-Music-Reco-FM</a>.</p> <p>If you make use of this dataset, please cite the following paper where we describe and experiment with the dataset:</p> <p>@inproceedings{smc18,<br> title = {#nowplaying-RS: A New Benchmark Dataset for Building Context-Aware Music Recommender Systems},<br> author = {Asmita Poddar and Eva Zangerle and Yi-Hsuan Yang},<br> url = {http://mac.citi.sinica.edu.tw/~yang/pub/poddar18smc.pdf},<br> year = {2018},<br> date = {2018-07-04},<br> booktitle = {Proceedings of the 15th Sound & Music Computing Conference},<br> address = {Limassol, Cyprus},<br> note = {code at https://github.com/asmitapoddar/nowplaying-RS-Music-Reco-FM},<br> tppubtype = {inproceedings}<br> }</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.