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.
13,064
datasets available to search
ShareScore release 0.9.0
Dataset results
13,064 results for “predictions”
SemEval-2021 Task 1: Lexical Complexity Prediction
<p>We ran a shared task on Lexical Complexity Prediction via SemEval using this data. The data was released according to the following schedule:</p> <ul> <li> <p>Trial data available: July 31, 2020</p> </li> <li> <p>Training data available: September 4, 2020</p> </li> <li> <p>Test data available/Evaluation starts: January 11, 2021</p> </li> </ul> <p>See the task website for further information: <a href="https://sites.google.com/view/lcpsharedtask2021">https://sites.google.com/view/lcpsharedtask2021</a></p> <p>See the CodaLab site to view submissions: <a href="https://competitions.codalab.org/competitions/27420">https://competitions.codalab.org/competitions/27420</a></p> <p>If you find this dataset useful, please cite the task paper as: <em>Shardlow, M. Evans, R., Paetzold, G and Zampieri, M. SemEval-2021 Task 1: Lexical Complexity Prediction. In Proceedings of the 15th International Workshop on Semantic Evaluation. 2021.</em></p> <p>The script evaluate.py is the same evaluation script that was used as part of the shared task. You can run this by calling "python evaluate.py <results_path>/res/ <reference_path>/ref/" where res and ref are directories containing the system's results and the reference labels respectively.</p> <p>The trial, training and test data are available. The trial data comprises of 99 MWEs (29 bible, 33 biomed and 37 europarl) and 421 single word instances (143 bible, 135 biomed and 143 europarl).</p> <p>The training data comprises of 1,517 MWEs (505 bible, 514 biomed and 498 europarl) and 7,662 single word instances (2,574 bible, 2,576 biomed and 2,512 europarl).</p> <p>The test data comprises of 184 MWEs (66 bible, 53 biomed and 65 europarl) and 917 single word instances (283 bible, 289 biomed and 345 europarl). It is compressedd and encrypted using 7zip as above. The password will be released to those registered for the task. The order of the test data has been randomised to prevent systems from overfitting to the data ordering. All submissions should be made via CodaLab as above.</p> <p>The trial and training data is arranged by token and sorted by complexity (i.e., instances with the same token appear together in groups, and the groups are sorted by the complexity of the lowest scored item). Tokens that appear in one partition will not appear in another partition. We have deliberately included more than one instance of a token where possible to identify places where the context affects the complexity of a word. Consider, for example, the following two sentences from the trial data:</p> <ul> <li>We now have a proposal on the <em>table</em> which lays down strict emissions values for the next ten years and which simultaneously creates clarity and incentives for technological innovations.</li> <li>Mr President, in coordination with other groups, I would like to <em>table</em> an oral amendment concerning the draft bill in the Duma to ignore certain rulings of the European Court of Human Rights.</li> </ul> <p>When <em>table</em> is used as a noun in the first sentence (albeit in an abstract sense) it receives a score of 0.01 indicating it is very easily understood. However, when it is used as a verb in the second, it is scored at 0.23, indicating a more difficult word to comprehend.</p> <p>The data comes from three sources: biblical text, biomedical articles and proceedings of the European Parliament. These sources were selected as they contain a natural mixture of common language and difficult to understand expressions, whilst each containing vastly different domain-specific vocabulary.</p>
Data Sets and Results for "Improved data sets and evaluation methods for the automatic prediction of DNA-binding proteins"
<p>Data sets and results for "Improved data sets and evaluation methods for the automatic prediction of DNA-binding proteins"<br> <br> The file "dna_binding_protein_sequences.zip" has the training and testing sets from the paper:<br> <br> RLL - "random_<train/test>_full_1000.csv"<br> RSL - "random_<train/test>_40.csv"<br> RS&LL - "random_<train/test>_40_1000.csv"<br> RLL where included positive examples have verified DNA binding activity - "random_<train/test>_hq_1000.csv"<br> The 10 RS&LL data sets - "random_<train/test>_40_1000.csv" + "random_<train/test>_40_1000_cv_<0-8>.csv"</p> <p>The results files are named similarly. See "see_results.ipynb" in the <a href="https://doi.org/10.5281/zenodo.5153683">codebase</a> that supplement these data sets<br> The species data sets are derived from "uniprot_data_bac.tab" and "uniprot_data_not_bac.tab." See <a href="https://doi.org/10.5281/zenodo.5153683">code</a>. <br> <br> The ESM embeddings used by the XGBoost model are in "dna_binding_protein_esm.zip"</p>
WiBB: An integrated method for quantifying the relative importance of predictive variables
<p>This dataset contains simulated datasets, empirical data, and R scripts described in the paper: "Li, Q. and Kou, X. (2021) <em>WiBB</em>: An integrated method for quantifying the relative importance of predictive variables. Ecography (DOI: 10.1111/ecog.05651)".</p> <p>A fundamental goal of scientific research is to identify the underlying variables that govern crucial processes of a system. Here we proposed a new index, <em>WiBB</em>, which integrates the merits of several existing methods: a model-weighting method from information theory (<em>Wi</em>), a standardized regression coefficient method measured by <em>ß</em>* (<em>B</em>), and bootstrap resampling technique (<em>B</em>). We applied the <em>WiBB</em> in simulated datasets with known correlation structures, for both linear models (LM) and generalized linear models (GLM), to evaluate its performance. We also applied two other methods, relative sum of wight (<em>SWi</em>), and standardized beta (<em>ß</em>*), to evaluate their performance in comparison with the <em>WiBB</em> method on ranking predictor importances under various scenarios. We also applied it to an empirical dataset in a plant genus <em>Mimulus</em> to select bioclimatic predictors of species' presence across the landscape. Results in the simulated datasets showed that the <em>WiBB</em> method outperformed the <em>ß</em>* and <em>SWi</em> methods in scenarios with small and large sample sizes, respectively, and that the bootstrap resampling technique significantly improved the discriminant ability. When testing <em>WiBB</em> in the empirical dataset with GLM, it sensibly identified four important predictors with high credibility out of six candidates in modeling geographical distributions of 71 <em>Mimulus</em> species. This integrated index has great advantages in evaluating predictor importance and hence reducing the dimensionality of data, without losing interpretive power. The simplicity of calculation of the new metric over more sophisticated statistical procedures, makes it a handy method in the statistical toolbox.</p>
Fig. 3 in Life Time Egg Production in Females of the Cricket Teleogryllus commodus WALKER 1869 (Insecta: Orthoptera): Experimental Results and Theoretical Predictions
Fig. 3: Total fecundity of female Teleogryllus commodus WALKER for different environmental temperatures (20 °C, 23 °C, 25 °C, and 30 °C). Asterisks indicate significant differences between neighbouring points (p <0.01).
Fig. 5 in Life Time Egg Production in Females of the Cricket Teleogryllus commodus WALKER 1869 (Insecta: Orthoptera): Experimental Results and Theoretical Predictions
Fig. 5: Preliminary results obtained from the computer program CRICKTHERM. Dependence of fecundity on (A) temperature, (B) photoperiod, (C) protein content in food, (D) population density (individuals per square metre).
Fig. 4 in Life Time Egg Production in Females of the Cricket Teleogryllus commodus WALKER 1869 (Insecta: Orthoptera): Experimental Results and Theoretical Predictions
Fig. 4: Results concerning the investigation of ovary weights and their dependence on environmental temperature. A) Scheme showing the preparation procedure necessary for the dissection of the ovaries. B) Graph illustrating the dependence of ovary weight in grams on environmental temperature. Ovary weights from 5-day old females (pre-oviposition period) and 10-day old females (oviposition period) have been compared. Points and error bars denote mean values and standard deviations (asterisks mark significant differences between neighbouring points, p <0.01).
Fig. 2 in Life Time Egg Production in Females of the Cricket Teleogryllus commodus WALKER 1869 (Insecta: Orthoptera): Experimental Results and Theoretical Predictions
Fig. 2: Dependence of daily cricket fecundity on environmental temperature. (A) 20 °C, (B) 23 °C, (C) 25 °C, (D) 30 °C. Black lines represent mean values, whereas dashed lines mark the standard deviations. The thick grey lines show the respective out put produced by CRICKTHERM and should give an impression about the validity of the used model.
Fig. 1 in Life Time Egg Production in Females of the Cricket Teleogryllus commodus WALKER 1869 (Insecta: Orthoptera): Experimental Results and Theoretical Predictions
Fig. 1: Window design and function of the computer program CRICKTHERM calculating the fecundity of female crickets as a function of various environmental parameters. (A) Entrance window, (B) Input window including animal information and input parameters, (C) Output window presenting the fecundity-time-relationship as a line plot as well as specific output parameter, (D) Alternative output window presenting fecundity data as mean values and standard deviations.
Known and Predicted GPI-anchored proteins in Trypanosoma brucei
<p>This table is a list of proteins that are either known or predicted to be a GPI-anchored proteins in <em>Trypanosoma brucei</em>. </p>
Fig. 2 in Predictive distribution modelling for rufous-necked hornbill Aceros nipalensis (Hodgson, 1829) in the core area of the Western Forest Complex, Thailand
Fig. 2. Diagram showing the conceptual framework used to model the distribution of the rufous-necked hornbill in the Western Forest Complex (WEFCOM), Thailand.
Fig. 1 in Predictive distribution modelling for rufous-necked hornbill Aceros nipalensis (Hodgson, 1829) in the core area of the Western Forest Complex, Thailand
Fig. 1. Map of research locations showing the study areas where the point counts were conducted along14 line-transects, using a hand-held GPS to accurately measure the count locations or extent and position of evergreen forest where the RNH lives, in order to predict RNH distribution.
Fig. 3 in Predictive distribution modelling for rufous-necked hornbill Aceros nipalensis (Hodgson, 1829) in the core area of the Western Forest Complex, Thailand
Fig. 3. Presence-absence binary models for RNH distributions after introducing equal sensitivity-specific threshold values to the continuous MaxEnt model based on the smallest home range size of the male RNH #15, as derived from Tifong (2007), during: a, the breeding season; b, the non-breeding season; and c, showing the combined habitat classification.
Fig. 3 in Can weight/length relationship predict size at first maturity? A case study with two species of Characidae
Fig. 3. Relationship between size at first maturity (L) and 50 maximum reported size for small characids (grey spots, bold line; data from Table 1). The thin line represents L values 50 estimated from maximum size following the general function of Froese & Binohlan (2000). Black spots are original data from the present study (SCL values).
Fig. 2 in Can weight/length relationship predict size at first maturity? A case study with two species of Characidae
Fig. 2. Weight/length relationship for Cheirodon ibicuhiensis (a); proportional residuals resulting from Huxley´s adjustment (b); weight/length relationship and proportional residuals distribution according to the polyphasic growth model (c and d, respectively).
Fig. 1 in Can weight/length relationship predict size at first maturity? A case study with two species of Characidae
Fig. 1. Weight/length relationship for Astyanax jacuhiensis (a); proportional residuals resulting from Huxley´s adjustment (b); weight/length relationship and proportional residuals distribution according to the polyphasic growth model (c and d, respectively).
Fig. 2 in Simple relationships to predict attributes of fish assemblages in patches of submerged macrophytes
Fig. 2. Correlation coefficients (Pearson's R) obtained from relationships between fish assemblage attributes (a: density; b: species richness) and habitat variables (macrophyte biomass, BIOM; volume, VOL; proportional volume, %VOL). We show correlation results from all patches (All), patches dominated by E. densa and patches dominated by E. najas. All correlations were statistically significant (p <0.05).
Fig. 3 in Environmental factors predicting fish community structure in two neotropical rivers in Brazil
Fig. 3. Scatterplot of canonical correspondence analysis (CCA) for the fish communities of the Jogui and Iguatemi Rivers.
Fig. 2 in Environmental factors predicting fish community structure in two neotropical rivers in Brazil
Fig. 2. Similarity dendrogram of fish communities in Jogui River (above) and Iguatemi Rivers (below).
Fig. 4 in Environmental factors predicting fish community structure in two neotropical rivers in Brazil
Fig. 4. Altitudinal distributions of the main fish species in the Jogui (A) and Iguatemi (B) rivers. Black dots represent sampling sites. Horizontal black lines represent species distribution range.
Diarrhea etiology prediction validation dataset - Bangladesh and Mali
<p>Background: Diarrheal illness is a leading cause of antibiotic use for children in low- and middle-income countries. Determination of diarrhea etiology at the point-of-care without reliance on laboratory testing has the potential to reduce inappropriate antibiotic use.</p> <p>Methods: This prospective observational study aimed to develop and externally validate the accuracy of a mobile software application ("App") for the prediction of viral-only etiology of acute diarrhea in children 0-59 months in Bangladesh and Mali. The App used previously derived and internally validated models using combinations of "patient-intrinsic" information (age, blood in stool, vomiting, breastfeeding status, and mid-upper arm circumference), pre-test odds using location-specific historical prevalence and recent patients, climate, and viral seasonality. Diarrhea etiology was determined with TaqMan Array Card using episode-specific attributable fraction (AFe) >0.5.</p> <p>Results:<b> </b>Of 302 children with acute diarrhea enrolled, 199 had etiologies above the AFe threshold. Viral-only pathogens were detected in 22% of patients in Mali and 63% in Bangladesh. Rotavirus was the most common pathogen detected (16% Mali; 60% Bangladesh). The viral seasonality model had an AUC of 0.754 (0.665-0.843) for the sites combined, with calibration-in-the-large α=-0.393 (-0.455 – -0.331) and calibration slope β=1.287 (1.207 – 1.367). By site, the pre-test odds model performed best in Mali with an AUC of 0.783 (0.705 - 0.86); the viral seasonality model performed best in Bangladesh with AUC 0.710 (0.595 - 0.825).</p> <p>Conclusion: The app accurately identified children with high likelihood of viral-only diarrhea etiology. Further studies to evaluate the app's potential use in diagnostic and antimicrobial stewardship are underway.</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.