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13,064 results for “predictions”
Data from: Improving inferences and predictions of species environmental responses with occupancy data
<p>Occupancy models represent a useful tool to estimate species distribution throughout the landscape. Among them, MacKenzie et al.'s model (2002, MC), is frequently used to infer species environmental responses. However, the assumption that detection probability is homogeneous or fully explained by covariates may limit its performance. Species should be more easily observed at sites with a higher number of individuals. We simulated data following Royle and Nichols (2003) occupancy model (RN) that accounts for abundance-driven heterogeneous detection and two variants with overdispersion in the detection probability and local abundances. Then, we compared the performance of the MC model against that of RN.</p> <p> In addition to model misspecifications, insufficient information in data (i.e. infrequent detections) can limit our ability to detect existing effects with affordable sampling designs. To deal with this source of error, we extended RN approach to a community-level joint species model (RN-JSM), where species responses and detectability depended on their traits and phylogeny. Then, we tested RN-JSM performance in simulated and out-of-sample field data.</p> <p>High abundance-driven heterogeneity in detection (i.e. common and secretive species) limited the ability of the MC model to quantify covariate effects; especially, when the number of visits was low. Both models (MC and RN), often failed to detect existing effects when data were overdispersed. Moreover, the RN model consistently lacked sufficient power when analyzing data from uncommon species (even when simulations and model specifications perfectly matched). This problem was solved by our RN-JSM, which yielded more precise and accurate estimates of species environmental responses. Increased accuracy in rare species held when the RN-JSM was tested with real and out-of-sample datasets.</p> <p>In the light of our results, we propose: (i) for common and secretive species analyze occupancy data with the RN model and prioritize revisiting sites; (ii) for species that may have overdispersed detectability or local abundances (e.g. with correlated behaviors or occurring in clusters), apply RN extensions that account for this extra variation (e.g. Poisson-beta or zero-inflated models). Finally, (iii) for uncommon species (mean abundances < 1), whenever possible, gather data at the community level and apply joint-species modeling techniques.</p>
Archive of the analysis results of the microtremor data obtained from a seismic array with a radius of 0.58 m distributed to the participants of the blind prediction experiments for the ESG6 symposium
<p>This is a supplemental material of the paper "Array-size dependency of the upper limit wavelength normalized by array radius for the standard spatial autocorrelation method" by Ikuo Cho, published in Earth, Planets and Space. It consists of the analysis results of the microtremor data observed using a seismic array with a radius of 0.58 m, which were distributed to the participants of the blind prediction experiments in ESG6. It involves all analysis results and script files to draw Figure 1 of the paper. See the "Availability of data and materials" section of the paper to download the original observed data and analysis code. See the main text of the paper for the details of the analysis.</p>
Canadian fossil fuel production and greenhouse gas emissions compared to predictions following the 2.0°C scenario
<p>This spreadsheet shows the amounts of coal, oil and natural gas produced in Canada from 2010 to 2020 using governmental sources. McGlade and Ekins (2015) proposed quotas for the production of each type of fossil fuel in order to provide a 67% chance to limit warming to 2.0°C by 2100. The proportion of each quota that is already spent is calculated. Emissions targets from 21 scenarios originating from five effort-sharing studies are compared with Canadian 2020 emissions to evaluate the difference. Carbon budgets from 18 scenarios originating from seven studies are compared with Canadian cumulative emissions to evaluate the percentage of the budgets already emitted within the 2010-2050 period. Emissions from five database are used in the calculations.</p>
Supplementary data from 'Predicting the distribution of Australian frogs and their overlap with Batrachochytrium dendrobatidis under climate change'
<p><strong><span>Aim: </span></strong><span>Amphibians, with over 40% of assessed species listed as threatened, are disproportionately at risk in the global extinction crisis. Among the many factors implicated in the current and ongoing loss of amphibian biodiversity are climate change and the disease chytridiomycosis, caused by the fungus <em>Batrachochytrium dendrobatidis </em>(<em>Bd</em>). These two threats are of particular concern in Australia, where <em>Bd </em>has been implicated in the declines of at least 43 frog species, and climate change is emerging as an additional threat. Here, we explore how climate change is likely to affect the distributions of Australian frog species and <em>Bd </em>to the year 2100, as well as how the spatial and climatic niche overlap between <a>chytridiomycosis-declined </a></span><span>frogs and <em>Bd</em> could shift.</span></p> <p><strong><span>Location: </span></strong><span>Australia</span></p> <p><strong><span>Methods: </span></strong><span>We used species distribution modelling to infer the current and future distribution of 141 Australian frog species and <em>Bd</em>, under two emissions scenarios. We used metrics of niche similarity, including Schoener's D and the Niche Margin Index, to quantify predicted alterations to spatial interactions between <em>Bd</em> and frog species.</span></p> <p><strong><span>Results: </span></strong><span>Climate change is likely to have a variable impact on frog distributions in Australia, with some 23 and 47 species, primarily in southern Australia, predicted to lose at least 30% of their current distributions under low and high emissions scenarios, respectively. In contrast, 69 and 68 species, respectively, have potential to increase their distributions, primarily in northern Australia. While the distribution of <em>Bd </em>is predicted to decrease, the proportional spatial and niche overlap between <em>Bd </em>and susceptible frog species is predicted to remain little changed, and in some cases, to increase.</span></p> <p><strong><span>Main conclusions: </span></strong><span>Although effects will be variable across the continent, climate change is likely to be a threatening factor to a number of Australian frog species. Additionally, chytridiomycosis is likely to remain a significant threat to many frog species, as any reductions to the pathogen's distribution largely coincide with geographic range contractions of chytridiomycosis-susceptible species.</span></p>
GloPrSM: Global Prediction of Sand Modal Composition
<p>GloPrSM is a global prediction of sand modal composition that depicts relative abundances of quartz, feldspar, and lithic grain types (and related subcompositions) in modern river sand deposits.</p>
Data and analysis scripts of "Predictability awareness rather than mere predictability enhances the perceptual benefits for targets in auditory rhythms over targets following temporal cues"
<p>These are the data and the analysis scripts accompanying the publication </p> <p>"Predictability awareness rather than mere predictability enhances the perceptual benefits for targets in auditory rhythms over targets following temporal cues". </p> <p>Check the readMe for an instruction.</p>
Prediction of runoff characteristics in ungauged basins in Central Europe with machine learning – files
<p><em><strong>English</strong></em></p> <p>This are the shapefiles accompanying the paper: Klingler et al. (2022), Prediction of runoff characteristics in ungauged basins with machine learning, published in the journal Österreichische Wasser- und Abfallwirtschaft: <a href="https://doi.org/10.1007/s00506-022-00891-4">https://doi.org/10.1007/s00506-022-00891-4</a></p> <p>The basic idea was to train a machine learning model with observed runoff characteristics of the hydrological years 2003 - 2017 (LamaH_observations, 859 features) and 90 different catchment characteristics to be able to predict runoff characteristics in unobserved catchments (OWK_predictions, 9533 features).</p> <p>We provide two shapefiles to download:<br> <strong>1) LamaH_observations</strong>, which contains attributes for 6 different runoff characteristics calculated from observed runoff timeseries from the LamaH-CE dataset (https://doi.org/10.5194/essd-13-4529-2021).<br> <strong>2)</strong> <strong>OWK_predictions</strong>, which includes additionally to the predicted 6 runoff characteristics also attributes for uncertainty quantification.<br> All attributes of the shapefiles are described in the associated metadata (.qmd files).</p> <p><strong>Disclaimer:</strong> We have created the shapefiles with care and checked the outputs for plausibility. By downloading the data, you agree that we nor the provider of the used source datasets (e.g. observed runoff time series) cannot be liable for the data provided.</p> <p><strong>License:</strong> This work is licensed with CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/). This means that you may freely use and modify the data (even for commercial purposes). But you have to give appropriate credit (associated ÖWAV paper, version of dataset), indicate if and what changes were made and distribute your work under the same public license as the original.</p> <p><strong>Contact:</strong> If you find any errors in the dataset or have any further questions, feel free to send us an email: info@baseflow.ai</p> <p>-------------</p> <p><strong><em>Deutsch</em></strong></p> <p>Dies sind die beiden Shapefiles, welche dem folgenden Fachartikel zugehörig sind: Klingler et al. (2022), Vorhersage von hydrologischen Abflusskennwerten in unbeobachteten Einzugsgebieten mit Machine Learning, veröffentlicht im Journal Österreichische Wasser- und Abfallwirtschaft: <a href="https://doi.org/10.1007/s00506-022-00891-4">https://doi.org/10.1007/s00506-022-00891-4</a></p> <p>Der Ansatz hinter dieser Arbeit war ein Machine Learning Modell mit beobachteten Abflusskennwerten der hydrologischen Jahre 2003 - 2017 (LamaH_observations, 859 Features) und 90 verschiedenen Einzugsgebietseigenschaften zu trainieren, um anschließend diese Abflusskennwerte in unbeobachteten Einzugsgebieten vorherzusagen (OWK_predictions, 9533 Features).</p> <p>Wir bieten zwei Shapefiles zum Download an:<br> <strong>1)</strong> <strong>LamaH_observations</strong>, welche Attribute für 6 verschiedene Abflusskennwerten (MJHQ, MQ, MJNQ, MJNQ7, Q95, Q98) enthält, die aus beobachteten Abflusszeitreihen aus dem LamaH-CE-Datensatz berechnet wurden (https://doi.org/10.5194/essd-13-4529-2021).<br> <strong>2)</strong> <strong>OWK_predictions</strong>, welche zusätzlich zu den vorhergesagten 6 Abflusskennwerten auch Attribute zur Quantifizierung der Unsicherheit enthält.<br> Alle Attribute der Shapefiles sind in den zugehörigen Metadaten (.qmd-Dateien) beschrieben.</p> <p><strong>Haftungsausschluss:</strong> Wir haben die Shapefiles mit Sorgfalt erstellt und die Ergebnisse auf Plausibilität geprüft. Mit dem Herunterladen der Daten erklären Sie sich damit einverstanden, dass weder wir noch der Anbieter der verwendeten Quelldatensätze (zB. beobachtete Abflusszeitreihen) für die bereitgestellten Daten haften.</p> <p><strong>Lizenz:</strong> Diese Arbeit ist lizenziert mit CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/). Dies bedeutet, dass Sie die Daten frei verwenden und verändern dürfen (auch für kommerzielle Zwecke). Sie müssen jedoch eine entsprechende Quellenangabe machen (zugehöriger ÖWAV-Artikel, Version des Datensatzes), angeben ob und welche Änderungen vorgenommen wurden, und Ihre Arbeit unter der gleichen Lizenz wie das Original veröffentlichen.</p> <p><strong>Kontakt:</strong> Wenn Sie Fehler im Datensatz finden oder weitere Fragen haben, können Sie uns gerne eine E-Mail schicken: info@baseflow.ai</p>
Training data for submitted paper "Wildfire Danger Prediction and Understanding with Deep Learning"
<p>Training data for submitted paper "Wildfire Danger Prediction and Understanding with Deep Learning". To run the code in https://github.com/Orion-AI-Lab/wildfire_forecasting</p>
Predicting harvest impact and establishment success when translocating highly mobile and endangered species
<p>Harvesting individuals for translocations can negatively impact source populations, a critical challenge for species reduced to small populations. Consequently, translocation cohorts often remain small, reducing the establishment probability at the destination. Balancing the potential benefits and risks of such translocations is further complicated by philopatry and natural metapopulation dynamics if the target species is highly mobile. These challenges highlight the importance of translocation feasibility assessments, but such assessments often remain qualitative to date. The critically endangered Kuaka (Whenua Hou Diving Petrel; Pelecanoides whenuahouensis) is a philopatric, highly mobile seabird that could benefit from conservation translocations, but only one small population remains. Through expert elicitations with a user-friendly Shiny app, we developed a novel metapopulation extension to an integrated population model fitted to long-term data, allowing us to simultaneously project harvest impact on the source and establishment of destination populations under alternative translocation scenarios, while accounting for philopatry and metapopulation dynamics. Establishment of a destination population without excessive impact on the source was possible, but subject to uncertainty about philopatry and metapopulation dynamics. Accounting for juveniles returning to the source post-translocations reduced impact on the source, but also decreased establishment at the destination. Natural movements of adults and juveniles between source and destination populations were predicted to modulate effects of different harvest intensities. Synthesis and application: Using state-of-the art integrated population models and expert elicitations, we illustrate how translocation feasibility can be evaluated transparently and quantitatively, even when targeting endangered, philopatric, and highly mobile species. Our approach is a considerable improvement on current qualitative feasibility assessments. However, we also illustrate that, ultimately, the favoured translocation strategy depends on balancing biological and other fundamental objectives inherent to translocations. Therefore, the ideal strategy cannot be determined solely mathematically, and feasibility assessments should incorporate explicit value statements. Our methodology is applicable to any future translocation scenario.</p>
Data and Scripts from: Bayesian prediction of multivariate ecology from phenotypic data yields novel insights into the diets of extant and extinct taxa
<p>Morphology often reflects ecology, enabling the prediction of ecological roles for taxa that lack direct observations such as fossils. In comparative analyses, ecological traits, like diet, are often treated as categorical, which may aid prediction and simplify analyses but ignores the multivariate nature of ecological niches. Futhermore, methods for quantifying and predicting multivariate ecology remain rare. Here, we ranked the relative importance of 13 food items for a sample of 88 extant carnivoran mammals, and then used Bayesian multilevel modeling to assess whether those rankings could be predicted from dental morphology and body size. Traditional diet categories fail to capture the true multivariate nature of carnivoran diets, but Bayesian regression models derived from living taxa have good predictive accuracy for importance ranks. Using our models to predict the importance of individual food items, the multivariate dietary niche, and the nearest extant analogs for a set of data-deficient extant and extinct carnivoran species confirms long-standing ideas for some taxa, but yields new insights about the fundamental dietary niches of others. Our approach provides a promising alternative to traditional dietary classifications. Importantly, this approach need not be limited to diet, but serves as a general framework for predicting multivariate ecology from phenotypic traits.</p>
Dataset for learning to predict the suitability of experts for statements
<p>Dataset consisting of 1,700 (statement, expert) pairs for learning and evaluating whether a researcher can be cited as an expert for a statement.<br> A total of 170 statements were extracted from the dataset "De Argumentenfabriek" [1].<br> For each statement, two experts were manually selected from Google-Scholar and their data extracted.<br> In addition, for each of the 170 statements, 8 experts were randomly picked from the pool of 340 scientists and then manually assessed on a scale from 0 to 2 by two to three annotators whether that scientist can be cited as an expert for the statement.</p> <p>[1]: https://zenodo.org/record/4813727<br> </p>
SIGTYP 2022 Shared Task: Prediction of Cognate Reflexes
<p>This is the data and code underlying the SIGTYP 2022 Shared Task on Cognate Reflex Prediction.</p>
Potential distribution of invasive boxwood blight pathogen (Calonectria pseudonaviculata) as predicted by process-based and correlative models
<p>R project, R scripts, and data files for reproducing most of the analyses presented in a climatic suitability study for boxwood blight. The README. md file describes how to run the scripts and provides details on data inputs.</p> <p><strong>Abstract: </strong>Boxwood blight caused by <em>Cps</em> is an emerging disease that has had devastating impacts on <em>Buxus</em> spp. in the horticultural sector, landscapes, and native ecosystems. In this study, we produced a process-based climatic suitability model in the CLIMEX program and combined outputs of four different correlative modeling algorithms to generate an ensemble correlative model. All models were fit and validated using a presence record dataset comprised of <em>Cps</em> detections across its entire known invaded range. Evaluations of model performance provided validation of good model fit for all models. A consensus map of CLIMEX and ensemble correlative model predictions indicated that not-yet-invaded areas in eastern and southern Europe and in the southeastern, midwestern, and Pacific coast regions of North America are climatically suitable for <em>Cps</em> establishment. Most regions of the world where<em> Buxus</em> and its congeners are native are also at risk of establishment. These findings provide the first insights into <em>Cps</em> global invasion threat, suggesting that this invasive pathogen has the potential to significantly expand its range.</p>
Client data for churn prediction for an internet shipment reselling company
<p> Dataset to train and test a churn classifier model for a ecommerce company.</p>
Future seasonal changes in habitat for Arctic whales during predicted ocean warming
<p><span>Ocean warming is causing shifts in the distributions of marine species, but the location of suitable habitats in the future is unknown, especially in remote regions such as the Arctic. Using satellite tracking data from a 28-year long period, covering all three endemic Arctic cetaceans (227 individuals) in the Atlantic sector of the Arctic, together with climate models under two emission scenarios, species distributions were projected to assess responses of these whales to climate change by the end of the century. While contrasting responses were observed across species and seasons, long-term predictions suggest northward shifts (243 km in summer vs. 121 km in winter) in distribution to cope with climate change. Current summer habitats will decline (mean loss: </span><span>-</span><span>25%), while some expansion into new winter areas (mean gain: +3%) is likely. However, comparing gains vs. losses raises serious concerns about the ability of these polar species to deal with the disappearance of traditional colder habitats.</span></p>
Data for "Vegetation complexity and pool size predict species richness of forest birds"
<p>This xlsx file contains data needed to replicate analyses in our article on species richness of Australian passerine birds.</p> <p>AUTHOR of data files: Vladimir Remes<br> CONTACT: vlad.remes/at/gmail,com<br> AUTHORS of the MS: V. Remes, L. Harmacková, B. Matysiokova, L. Rubacova, E. Remesova<br> DATE CREATED: 8 June 2022</p> <p>The file was saved in MS Excel for Mac 16.42.</p> <p>The file is named "data_SR_AU_passerines.xlsx" and has two sheets:<br> "data": contains the data<br> "legend": contains explanations of data columns</p> <p>The related article is:</p> <p>Remeš V, Harmáčková L, Matysioková B, Rubáčová L and Remešová E (2022) Vegetation complexity and pool size predict species richness of forest birds. Front. Ecol. Evol. 10:964180. doi: 10.3389/fevo.2022.964180</p>
Data and code of "Post-trauma behavioral phenotype predicts the degree of vulnerability to fear relapse after extinction in male rats"
<p>This dataset contains behavioral and transcriptomic data, and the original code related to the following article:</p> <p>Post-trauma behavioral phenotype predicts the degree of vulnerability to fear relapse after extinction in male rats. Fanny Demars, Ralitsa Todorova, Gabriel Makdah, Antonin Forestier, Marie-Odile Krebs, Bill P Godsil, Thérèse M Jay, Sidney I Wiener, & Marco N Pompili (2022) Current Biology <em>32. https://doi.org/10.1016/j.cub.2022.05.050</em></p>
Predicting Respone: 3d printed biopolymer block dataset
<p>Dataset consisting of 87 edge shrinkage recording of 87 3d printed biopolymer components printed with inhouse receipe 10.5281/zenodo.5557218</p> <p> </p> <p>This dataset is reported in the paper</p> <p>Rossi G., Chiujdea R., Hochegger L., LHarchi A., Harding J., Nicholas P., Tamke M., Ramsgaard Thomsen M. (forthcomming 2022) Statistically modelling the curing of cellulose-based 3d printed components: Methods for material dataset composition, augmentation and encoding. In Design Modelling Symposium Berlin: Towards Radical Regeneration. 26-28 September 2022. University of the Arts Berlin, Germany</p>
SETH predictions for Swiss-Prot (downloaded 13/06/22) and the human proteome (downloaded 08/07/21)
<p>Per residue disorder predictions for proteins of Swiss-Prot (downloaded 13/06/22) and the human proteome (downloaded 08/07/21; (The UniProt et al., 2021)) generated with SETH (<a href="https://github.com/DagmarIlz/SETH">https://github.com/DagmarIlz/SETH</a>). </p> <p>For details on SETH see: Ilzhöfer D, Heinzinger M and Rost B (2022) SETH predicts nuances of residue disorder from protein embeddings. Front. Bioinform. 2:1019597. <a href="https://doi.org/10.3389/fbinf.2022.1019597">https://doi.org/10.3389/fbinf.2022.1019597</a>.</p> <p>For some proteins no ProtT5 (Elnaggar et al., 2021) embeddings could be generated due to their length. Therefore, these proteins are missing from the dataset. All in all, predictions for 567,458 out of 567,483 proteins are available for Swiss-Prot and 20,352 proteins are available for the human proteome.</p>
Data for RAPPPID: Towards Generalisable Protein Interaction Prediction with AWD-LSTM Twin Networks
<p>Data for RAPPPID, a method for the Regularised Automative Prediction of Protein-Protein Interactions using Deep Learning.</p> <p>These datasets are in a format that RAPPPID is ready to read.<br> <br> <strong>Comparatives Dataset</strong><br> These datasets were derived from the STRING v11 <em>H. sapiens</em> dataset, according to the C1, C2, and C3 procedures outlined by Park and Marcotte, 2012. Negative samples are sampled randomly from the space of proteins not known to interact. See <a href="https://doi.org/10.1101/2021.08.13.456309">Szymborski & Emad</a> for details.<br> <br> <strong>Repeatability Datasets</strong><br> The following datasets are all derived from STRING in the manner as the comparatives dataset, but three different random seeds are used for drawing proteins.<br> <br> <strong>References</strong><br> Park,Y. and Marcotte,E.M. (2012) Flaws in evaluation schemes for pair-input computational predictions. Nat Methods, 9, 1134–1136.</p> <p>Szklarczyk, D., Gable, A. L., Lyon, D., Junge, A., Wyder, S., Huerta-Cepas, J., Simonovic, M., Doncheva, N. T., Morris, J. H., Bork, P., Jensen, L. J., and Mering, C. (2019). String v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Research, 47(D1), D607–D613.<br> <br> Szymborski,J. and Emad,A. (2021) RAPPPID: Towards Generalisable Protein Interaction Prediction with AWD-LSTM Twin Networks. bioRxiv https://doi.org/10.1101/2021.08.13.456309</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.