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.
5,526
datasets available to search
ShareScore release 0.7.1
Dataset results
5,526 results for “information”
BRAIN Journal-A New Challenge for Information Mining-Figure 2. A ScreenShot of L4All Portal
<p> Simple selection or complex selection operations, with boolean operators, are possible. Each widget shows the value of the attributes for the current state of the dataset with different visualization. The current set of objects is shown on a “canvas” (see Figure 2 - right side of the interface). Thanks to advanced Human-Computer Interaction mechanisms, the portal can support sophisticated exploration activities in the cycle . Based on L4All, a number of scientific investigations by different research groups took place: on the relation between different forms of group-work and inclusion, on digital storytelling and related benefits, etc. (Di Blas, Paolini, 2013; Falcinelli, 2012; Falcinelli, Laici, 2012). </p>
BRAIN Journal-A New Challenge for Information Mining-Figure 1. kinds of information needs (Wildemuth and Freund,2012)
<p>The taxonomy of tasks, related to the two different kinds of information needs is illustrated in Figure 1. </p> <p>In the precision-oriented information needs category, the task’s goal is to locate one resource and get information about its attributes or metadata while in the recall-oriented information needs category the task’s goal is to locate (and get information about) a set of resources. In this category we can distinguish goals that require accessing sets of resources just in groups, or in groups accompanied by count information for getting an overview of a set of resources, e.g. as in Faceted Dynamic Taxonomies (FDT). Furthermore, we may have goals that require more complex aggregated results like those provided by data warehouses. For instance, aggregations of arithmetic (min, max, average) and Boolean functions over the numeric attributes of the documents in the answers of free-text queries. Moreover, counts are computed and displayed over combinations (pairs, triples, quadruplets, etc.) of attributes (of grouping criteria in general). In comparison to OnLine Analytical Processing (OLAP) queries, in exploratory search the information demand in unknown a priori (in OLAP it is known and the schema is fixed) and the objective is not only to compute and see various aggregate values (e.g. sales per month and department), but also to support a flexible process for finding the desired individual resources (Tzitzikas et al., 2016).</p>
The BigGrams: the semi-supervised information extraction system from HTML: an improvement in the wrapper induction - dataset
<p><strong>Brief description</strong></p> <p>The zip file contains two folders. The <strong>"websites"</strong> folder includes crawled web pages from real websites, like a agatameble.pl (an e-shop website), filmweb.pl (a website about films), and ptaki.info (a website about birds). The <strong>"reference-seeds"</strong> folder contains three subfolders, i.e. agatameble.pl, filmweb.pl, and ptaki.info. Each subfolder contains reference-seeds.csv file. The file contains data, i.e. reference instances - carefully labelled ground-truth of corresponding values in each web page of given websites mentioned above.</p> <p><strong>Reference</strong></p> <p>I would appreciate it if you cite the following paper when using the dataset:</p> <p>Marcin Mirończuk The BigGrams: the semi-supervised information extraction system from HTML: an improvement in the wrapper induction, Knowledge and Information Systems, Volume 54, Issue 3, p. 711–776, 2018, (pdf Open Access – http://rdcu.be/u88F lub DOI http://dx.doi.org/10.1007/s10115-017-1097-2)</p>
Data_MathyChekafCowan_JOC2018_Simple and Complex Working Memory Tasks Allow Similar Benefits of Information Compression
<p>Original data files for the article Mathy, Fabien, Chekaf, Mustapha, & Cowan Nelson (2018). Simple and Complex Working Memory Tasks Allow Similar Benefits of Information Compression. Journal of Cognition.</p> <p>Abstract : Complex working memory span tasks were designed to engage multiple aspects of working memory and impose interleaved processing demands that limit the use of mnemonic strategies, such as chunking. Consequently, the average span is usually lower (4 ± 1 items) than in simple span tasks (7 ± 2 items). One possible reason for the higher span of simple span tasks is that participants can take advantage of the spare time to chunk multiple items together to form fewer independent units, approximating 4 ± 1 chunks. It follows that the respective spans of these two types of tasks could be equal (at around 4 ± 1) if stimulus lists exclusively used nonchunkable stimulus items. To manipulate the chunkability of the stimulus lists, our method involved a measure of their compressibility, i.e., the extent to which a pattern exists that can be detected and used as a basis of chunk formation. We predicted an interaction between the types of tasks and chunkability/compressibility, supporting a single higher span for the condition in which a simple span task was combined with chunkable items. The three other conditions were predicted to prevent chunking processes, either because the interleaved processing task did not allow any chunking process to occur or because the noncompressible material inherently limited the chunkability of information. The prediction that chunking is important solely in simple spans was not confirmed: Effects of information compression contributed to performance levels to a similar extent in both tasks according to a theoretically-based metric. This result suggests that i) complex span tasks might overestimate storage capacity in general, and ii) the difference between simple and complex span performance levels must rest in some mechanism other than prevention of a chunking strategy by the interleaved processing task in complex span tasks.</p>
The "social brain" is highly sensitive to the mere presence of social information: An automated meta-analysis and an independent study
<p><strong>Abstract</strong></p> <p>How the human brain process social information is an increasingly researched topic in psychology and neuroscience, advancing our understanding of basic human cognition and psychopathologies. Neuroimaging studies typically seek to isolate one specific aspect of social cognition when trying to map its neural substrates. It is unclear if brain activation elicited by different social cognitive processes and task instructions are also spontaneously elicited by general social information. In this study, we investigated whether these brain regions are evoked by the mere presence of social information using an automated meta-analysis and confirmatory data from an independent study of simple appraisal of social vs. non-social images. Results of 1,000 published fMRI studies containing the keyword of “social” were subject to an automated meta-analysis (neurosynth.org). To confirm that significant brain regions in the meta-analysis were driven by a social effect, these brain regions were used as regions of interest (ROIs) to extract and compare BOLD fMRI signals of social vs. non-social conditions in the independent study. The NeuroSynth results indicated that the dorsal and ventral medial prefrontal cortex, posterior cingulate cortex, bilateral amygdala, bilateral occipito-temporal junction, right fusiform gyrus, bilateral temporal pole, and right inferior frontal gyrus are commonly engaged in studies with a prominent social element. The social – non-social contrast in the independent study showed a strong resemblance of the NeuroSynth map. ROI analyses revealed that a social effect was credible in 8 out of the 11 NeuroSynth regions in the independent dataset. The findings support that the “social brain” is highly sensitive to the mere presence of social information. </p>
Supplementary Information for: Quantifying the potential for consumer-oriented policy to reduce domestic and foreign carbon emissions
<p>These files comprise the Supplementary Information for the paper "Quantifying the potential for consumer-oriented policy to reduce domestic and foreign carbon emissions" submitted to Climate Policy.</p>
Fig. 11 in Redescription of Tapinesthis inermis (Araneae, Oonopidae), with detailed information on its ultrastructure
Fig. 11. Tapinesthis inermis, SEM, A–H dorsal views, I lateral view. – A–D. Ƌ (PBI 33882). A. Leg I. B. Leg II. C. Leg III. D. Leg IV. – E–G. ♀ (PBI 33883). E. Leg I. F. Leg II. G. Leg III. – H–I. Ƌ (PBI 33564). H. Leg IV. I. Tarsal organ, leg IV.
Fig. 8 in Redescription of Tapinesthis inermis (Araneae, Oonopidae), with detailed information on its ultrastructure
Fig. 8. Tapinesthis inermis, SEM views of ♀ genitalia (PBI 33267). A. Overview of genital area, dorsal view. B. Same, close up. C. Same, view slightly anterior. D. Anterior receptaculum (ARe), close up, arrow indicates gland ducts (GD). E. Same, anterior view. F. Same, detail of gland ducts (arrows). G. Lateral protrusion (Pr), detail, dorsal view. – ARe: Anterior Receptaculum; AS: Anterior Sclerite; FA: Flattened Apodeme; GD: Gland Duct; Pr: lateral Protrusion; PRe: Posterior Receptaculum; T: transverse sclerite.
Fig. 7 in Redescription of Tapinesthis inermis (Araneae, Oonopidae), with detailed information on its ultrastructure
Fig. 7. Tapinesthis inermis, SEM views of Ƌ (PBI 33564). A. Palp, frontal view. B. Same, prolateral view. C. Tip of embolus, ventral view. D. Same, retrolateral view.
Fig. 5 in Redescription of Tapinesthis inermis (Araneae, Oonopidae), with detailed information on its ultrastructure
Fig. 5. Tapinesthis inermis, genitalia of ♀ (PBI 33267) A. Genital area, ventral view. B. Same, after digestion, ventral view. C. Same, dorsal view. D. Same, anterior view. – ARe: Anterior Receptaculum; AS: Anterior Sclerite; FA: Flattened Apodeme; Pr: lateral Protrusion; PRe: Posterior Receptaculum.
Fig. 4. Tapinesthis inermis. – A–C in Redescription of Tapinesthis inermis (Araneae, Oonopidae), with detailed information on its ultrastructure
Fig. 4. Tapinesthis inermis. – A–C. Ƌ (PBI 33564), palp. A. Retrolateral view. B. Same, frontal view. C. Same, prolateral view. – D. Ƌ (PBI 09021), palp, prolateral view, transmitted light. – E. Ƌ palp, lateral view after Saaristo & Marusik, 2009 (modiFed). – D: duct; Te: tendon; Tw: thin-walled duct; V: vesicle.
Fig. 3 in Redescription of Tapinesthis inermis (Araneae, Oonopidae), with detailed information on its ultrastructure
Fig. 3. Tapinesthis inermis, Ƌ from the Czech Republic (PBI 32902). A. Habitus, dorsal view. B. Same, lateral view. C. Same, lateral view. D. Prosoma, ventral view.
Fig. 9 in Redescription of Tapinesthis inermis (Araneae, Oonopidae), with detailed information on its ultrastructure
Fig. 9. Tapinesthis inermis, SEM views of spinneret area of Ƌ (PBI 33564) and ♀ (PBI 33267). A. Ƌ, colulus, ventral view. B. Same, anterior lateral spinnerets, ventral view. C. Same, posterior lateral spinnerets. D. Same, focus on posterior median spinnerets. E. ♀, posterior median spinnerets, ventral view. F. Same, anterior lateral spinnerets. G. Same, posterior lateral spinnerets. H. Same, lateral view. – ALS: anterior lateral spinnerets; PLS: posterior lateral spinnerets; PMS: posterior median spinnerets.
Fig. 2. Tapinesthis inermis. A in Redescription of Tapinesthis inermis (Araneae, Oonopidae), with detailed information on its ultrastructure
Fig. 2. Tapinesthis inermis. A. Ƌ (PBI 33564), habitus, dorsal view. B. Same, carapace. C. Same, sternum, ventral view. D. ♀ (PBI 33267), habitus, dorsal view. E. Ƌ (PBI 33564), habitus, lateral view. F. Same, ♀ (PBI 33267). G. ♀ (PBI 08123), carapace, anterior view. Scale bars = 0.5 mm.
Fig. 6. Tapinesthis inermis, SEM views. A in Redescription of Tapinesthis inermis (Araneae, Oonopidae), with detailed information on its ultrastructure
Fig. 6. Tapinesthis inermis, SEM views. A. Ƌ (PBI 08123), carapace, antero-lateral view. B. Same, close up on the distribution of the smooth platelets. C. Ƌ (PBI 33564), sternum, ventral view. D. Same, mouthparts. E. Same, close up of endite setae. F. Same, cheliceral fangs. G. Ƌ (PBI 08123), endites, anterior view with detail of serrula. H. Ƌ (PBI 33564), genital region, ventral view.
Fig. 12 in Redescription of Tapinesthis inermis (Araneae, Oonopidae), with detailed information on its ultrastructure
Fig. 12. Tapinesthis inermis Ƌ (PBI 33564), SEM views. A. Cymbium, frontal view. B. Same. C. Metatarsus II, dorsal view, black arrow showing pore and white arrow indicating eye-shaped structure. D. Metatarsus IV, black arrow as in C. E. Trichobothrium, dorsal view of bothrium on tibia IV. F. Dorsal view of bothrium on metatarsus.
Figs. 10 in Redescription of Tapinesthis inermis (Araneae, Oonopidae), with detailed information on its ultrastructure
Figs. 10. Tapinesthis inermis, SEM views of tarsal claws of Ƌ (PBI 33564). A. Leg I, anterior view. B. Same, lateral view. C. Leg II, lateral view. D. Same, anterior view. E. Leg III, lateral view. F. Leg IV, lateral view.
UnmixDB: A Dataset for DJ-Mix Information Retrieval
<p>A collection of automatically generated DJ mixes with ground truth, based on creative-commons-licensed freely available and redistributable electronic dance tracks.</p> <p>In order to evaluate the DJ mix analysis and reverse engineering methods, we created a dataset of excerpts of open licensed dance tracks and automatically generated mixes based on these.</p> <p>Each mix is based on a playlist that mixes 3 track excerpts beat-synchronously, such that the middle track is embedded in a realistic context of beat-aligned linear cross fading to the other tracks.<br> The first track's BPM is used as the seed tempo onto which the other tracks are adapted.</p> <p>Each playlist of 3 tracks is mixed 12 times with combinations of 4 variants of effects and 3 variants of time scaling using the treatments of the sox open source command-line program [http://sox.sourceforge.net].</p> <p>Each track excerpt contains about 20s of the beginning and 20s of the end of the source track. However, the exact choice is made taking into account the metric structure of the track. The cue-in region, where the fade-in will happen, is placed on the second beat marker starting a new measure, and lasts for 4 measures. The cue-out region ends with the 2nd to last measure marker. We assure at least 20s for the beginning and end parts. The cut points where they are spliced together is again placed on the start of a measure, such that no artefacts due to beat discontinuity are introduced.</p> <p>The UnmixDB dataset contains the ground truth for the source tracks and mixes in ASCII label format with tab-separated columns starttime, endtime, label.<br> For each mix, the start, end, and cue points of the constituent tracks are given, along with their BPM and speed factors.<br> We use the convention that the label starts with a number indicating which of the 3 source tracks the label refers to.</p> <p>The song excerpts are accompanied by their cue region and tempo information in .txt files in table format.</p> <p>Additionally, we provide the .beat.xml files containing the beat tracking results for the full tracks available from Sonnleitner et. al. 2016.</p> <p>Our DJ mix dataset is based on the curatorial work of Sonnleitner et. al. (ISMIR 2016), who collected Creative-Commons licensed source tracks of 10 free dance music mixes from Mixotic. We used their collected tracks to produce our track excerpts, but regenerated artificial mixes with perfectly accurate ground truth.</p> <p>The code used to create the dataset from the above is published at https://github.com/Ircam-RnD/unmixdb-creation, such that other researchers can create test data from other track collections or in other variants.</p> <p> </p>
Common20LS: A Lexical Simplification Dataset with Demographic Information
<p>Common20LS is a dataset for the task of Lexical Simplification that contains demographic information about the annotators. It consists on 20 Lexical Simplification problems annotated by 262 people. Each annotated instance is composed of a sentence, a target complex word or phrase, and a set of simplifications suggested by humans ranked by simplicity.</p>
Fig. 6 in Enlarging the monotypic Monocarpieae (Annonaceae, Malmeoideae): recognition of a second genus from Vietnam informed by morphology and molecular phylogenetics
Fig. 6. – Leoheo domatiophorus Chaowasku, D.T. Ngo & H.T. Le, showing habit with inflorescences and flowers. [HUAF collectors 2009-03-19-ND,CMUB] [Drawing: A. Damthongdee]
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.