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125 results for “open models”
Target enrichment of long open reading frames and ultraconserved elements to link microevolution and macroevolution in non-model organisms
<p>Despite the increasing accessibility of high-throughput sequencing, obtaining high-quality genomic data on non-model organisms without proximate well-assembled and annotated genomes remains challenging. Here we describe a workflow that takes advantage of distant genomic resources and ingroup transcriptomes to select and jointly enrich long open reading frames (ORFs) and ultraconserved elements (UCEs) from genomic samples for integrative studies of microevolutionary and macroevolutionary dynamics. This workflow is applied to samples of the African unionid bivalve tribe Coelaturini (Parreysiinae) at basin and continent-wide scales. Our results indicate that ORFs are efficiently captured without prior identification of intron-exon boundaries. The enrichment of UCEs was less successful but nevertheless produced substantial datasets. Exploratory continent-wide phylogenetic analyses with ORF supercontigs (> 515,000 parsimony informative sites) resulted in a fully resolved phylogeny, the backbone of which was also retrieved with UCEs (> 11,000 informative sites). Variant calling on ORFs and UCEs of Coelaturini from the Malawi Basin produced ~2,000 SNPs per population pair. Estimates of nucleotide diversity and population differentiation were similar for ORFs and UCEs. They were low compared to previous estimates in mollusks, but comparable to those in recently diversifying Malawi cichlids and other taxa at an early stage of speciation. Skimming off-target sequence data from the same enriched libraries of Coelaturini from the Malawi Basin, we reconstructed the maternally-inherited mitogenome, which displays the gene order inferred for the most recent common ancestor of Unionidae. Overall, our workflow and results provide exciting perspectives for integrative genomic studies of microevolutionary and macroevolutionary dynamics in non-model organisms.</p>
Open-population SCR model to estimate spatiotemporal variation in individual birth locations, detection rates, and survival
<p>This is an open population SCR model developed by R. Chandler and K. Engebretsen. The full model incorporates 4 spatial covariates in birth location density submodel, 3 location-specific, temporal covariates in the detection submodel, and 4 spatial covariates in the survival submodel. </p> <p>Formatted data is provided for the 2015 and 2016 fawning season in south Florida and the model can be fit using the script fitFawnModel.R.</p>
CCG: Beyond the Dams: Combatting Hydropower Over-reliance & Securing Pathways for a Low-carbon Future for Laos' Electricity Sector using OSeMOSYS (Open-Source Energy Modelling System)
<p>Seven clicSAND scenario files for <strong>Beyond the Dams: Combatting Hydropower Over-reliance & Securing Pathways for a Low-carbon Future for Laos' Electricity Sector using OSeMOSYS (Open-Source Energy Modelling System).</strong> </p> <p><strong>How to Visualise Results Online and Offline</strong> outline the steps required to re-run the scenarios on OSeMOSYS Cloud</p> <p><strong>Scenario Short Note</strong> outlines the steps to replicate the analysis and rebuild the scenarios</p> <p><strong>Annex - Input Data and Assumptions</strong> listing the data sources and assumptions in the scenarios</p>
Raw video and pose estimation data of top view open field mouse behavior recordings of acute and chronic stress models
<p>This repository contains raw data for 411 different open field recordings of mice. these include top view raw video .mp4 files (Videos.zip) and the corresponding .csv pose estimation data (data.zip) obtained with DeepLabCut. The data is from multiple different experiments. The METADATA.csv or METADATA.xlsx files contain all grouping variables and help linking the pose estimation files (located in multiple subfolders of /data) to the video files. Visit https://github.com/ETHZ-INS/BehaviorFlow to find out more about how this data has be used by us.</p>
EvoSL: A Large Open-Source Corpus of Changes in Simulink Models & Projects
<p>EvoSL is a corpus of 924 Simulink repositories useful to perform model evolutionary studies. <a href="https://zenodo.org/api/files/cbb76da1-1077-4020-8d7c-1c6f4f7b3d36/dataset_er_derived_full_schema.pdf?versionId=a9270932-acca-40d8-b5aa-88330525ade8">dataset_er_derived_full_schema</a> shows the full schema design of the EvoSL_v1 SQLite database. Refer to the paper for full details of the dataset.</p> <p>EvoSL.sqlite (Table: Model_element_changes) contains over 2+ million element-level change raw data extracted from 14k+ Simulink model snapshots. Refer to the tool (https://github.com/50417/EvoSL-Tool) to discard duplicates using its cleaning module.</p> <p>"EvoSL: A Large Open-Source Corpus of Changes in Simulink Models & Projects" is accepted in <a href="https://conf.researchr.org/track/models-2023/models-2023-technical-track">MODELS 2023</a> (CORE A, acceptance rate: 24.6%)</p> <p> </p>
Towards a general open dataset and model for late medieval Castilian text recognition (HTR/OCR). Datasets and scripts
<p>This repository contains the dataset of the article "Towards a general open dataset and models for late medieval Castilian writing (HTR/OCR)" submitted to the Journal of Data Mining and Digital Humanities (JDMDH). I refer to the paper (<a href="https://doi.org/10.5281/zenodo.7387376">https://doi.org/10.5281/zenodo.7387376</a>) for the description of the corpus and the models.</p><p><strong>The dataset is in version V2: it contains the allographetic AND graphematic transcriptions (files `*.normalized.xml`) and models.</strong></p><p><i>Caveat</i>: the allographetic transcriptions and models only are described in the data paper mentionned above. The graphematic transcriptions are produced using a Chocomuffin conversion table (see `corpus/conversion_table.csv`) to reduce each allograph to its corresponding grapheme. The abbreviations are not expanded.</p><p>Please cite the following paper if you use this dataset or the models:</p><p>@article{gille_levenson_2023_towards,<br> author = {Gille Levenson, Matthias},<br> date = {2023},<br> journaltitle = {Journal of Data Mining and Digital Humanities},<br> doi = {<a href="https://doi.org/10.46298/jdmdh.10416">10.46298/jdmdh.10416</a>},<br> editor = {Pinche, Ariane and Stokes, Peter},<br> issuetitle = {Special Issue: Historical documents and automatic text recognition},<br> title = {Towards a general open dataset and models for late medieval Castilian text recognition<br>(HTR/OCR)},</p><p>GILLE LEVENSON , Matthias, « Towards a general open dataset and models for late medieval Castilian<br>text recognition (HTR/OCR) », <i>Journal of Data Mining and Digital Humanities</i> (2023) : Special<br>Issue : Historical documents and automatic text recognition, eds. Ariane PINCHE and Peter<br>STOKES, DOI : <a href="https://doi.org/10.46298/jdmdh.10416">10.46298/jdmdh.10416</a>.</p><p>The image of the manuscript M (Esc_M) has not yet been uploaded, pending permission from the library that keeps the manuscript.</p><p>All images are kept in a directory named after the place where the manuscript is kept, and the sigla of the witness for the in-domain dataset.</p><p> </p><p>The global licence for the dataset (except for images) is CC-BY-NC-SA.</p><p>All manuscripts reproductions are published with the authorization of the libraries.</p><p><strong>©Biblioteca General Histórica de Salamanca</strong></p><p>Universidad de Salamanca (España), Biblioteca General Histórica, Ms. 2709 (L)</p><p>Universidad de Salamanca (España), Biblioteca General Histórica, Ms. 2097 (J)</p><p>Universidad de Salamanca (España), Biblioteca General Histórica, Ms. 2673</p><p>Universidad de Salamanca (España), Biblioteca General Histórica, Ms. 2011</p><p>Universidad de Salamanca (España), Biblioteca General Histórica, Ms. 2654</p><p>Universidad de Salamanca (España), Biblioteca General Histórica, Ms. 2086</p><p><strong>©Museo Lázaro Galdiano. Madrid</strong></p><p>Inv. 15304, Fundación Lázaro Galdiano (A)</p><p><strong>©Universidad de Valladolid</strong></p><p>Ms. 251, Biblioteca Santa Cruz (S)</p><p><strong>©Real Biblioteca del Escorial</strong></p><p>Ms. K.I.5, Biblioteca del Real Monasterio del Escorial (Q)</p><p>Ms. h.I.8, Biblioteca del Real Monasterio del Escorial (M): to be published</p><p>Ms. Z-I-12</p><p>Ms.Z-III-9</p><p>Ms. X-III-4</p><p>Ms. h-III-9</p><p>Ms. b-IV-15</p><p>Ms. b-II-11</p><p>Ms. a-II-17</p><p>Ms. T-III-5</p><p><strong>©Rosenbach Foundation</strong></p><p>Ms. 482/2 (U)</p><p><strong>© Gallica.bnf.fr</strong></p><p>Espagnol 12</p><p>Espagnol 36</p><p>Espagnol 218</p><p><strong>© Bodleian Library</strong></p><p>Ms. Span. d. 1</p><p>Ms. Span. d. 2/1</p><p><strong>© Biblioteca Real, Madrid</strong></p><p>Ms. II/215 (G)</p><p><strong>© Biblioteca Nacional de España</strong></p><p>Mss/4183</p><p>Inc/901 (Z)</p><p><strong>© Biblioteca Universitaria, Sevilla</strong></p><p>Ms. 332/131 (R)</p><p> </p><p>Edit: add result files</p>
Open-population SCR model to estimate spatiotemporal variation in individual birth locations, detection rates, and survival
Open the record for dataset details and reuse information.
Target enrichment of long open reading frames and ultraconserved elements to link microevolution and macroevolution in non-model organisms
Open the record for dataset details and reuse information.
Data from: Estimating abundance of an open population with an N-mixture model using auxiliary data on animal movements
Open the record for dataset details and reuse information.
Modeling to determine when to re-open HIV services during COVID-19 pandemic
Open the record for dataset details and reuse information.
Open source physiological data and physiological-based kinetic model code for the chicken (Gallus gallus domesticus)
<p>This excel file and mode code (DOI:10.5281/zenodo.3603114) provides:</p> <p>1. Physiological parameters and associated inter-individual variability (sample size, mean, coefficient of variation,) for chicken (<em>Gallus gallus domesticus</em>). These physiological parameters were estimated based on the results of extensive literature searches and specific experimental data described in Lautz et al., (2020).</p> <p>2. An R code for the generic chicken physiologically based model as well as the “soboljansen” code to carry out sensitivity analysis using sobol plots. The code for the generic model allows to run:</p> <p>a. A deterministic PBK model which represents only a single animal.</p> <p>b. A probabilistic PBK model to simulate individual differences in physiological parameters within a population. Sensitivity analyses can be performed to identify which parameters have the most impact on the model’s outputs. Predictions can be compared with experimental data. The model can be used to assess the influence of physiological parameters on the kinetics of chemicals. For PBK modelling purposes, species and chemical specific kinetics (e.g clearance, absorption rate, etc…) should be provided by the user.</p> <p>The full data collection and implementation of the models using case studies are described in (Lautz et al., 2020).</p> <p><strong>The dataset providing the physiological parameters is available in Excel.<br> The R code is presented as meta data to be implemented in R.</strong></p>
Euro-Calliope model and results for "Open Source Energiewende" multi-model analysis
<p>Contains the model version of Euro-Calliope applied in the multi-model analysis "Open Source Energiewende" and the aggregated results of six scenarios. See `./README.md` for more information.</p>
Integrating QSAR models predicting acute contact toxicity and mode of action profiling in honey bees (A. mellifera): Data curation using open source databases, performance testing and validation
<p>This excel file (DOI: <a href="https://doi.org/10.5281/zenodo.3755675">https://doi.org/10.5281/zenodo.3755675</a>) provides the collection of raw data used for developing the first integrative Quantitative Structure-Activity Relationship (QSAR) model using EFSA's OpenFoodTox, US-EPA ECOTOX and Pesticide Properties DataBase i) to predict acute contact toxicity (LD<sub>50</sub>) and ii) to profile the Mode of Action (MoA) of pesticides active substances in honey bees (<em>Apis mellifera</em>)<em>. </em>Chemical identifiers (e.g. SMILES, CAS n., InChI) and acute contact toxicity data (LD<sub>50</sub>) on honey bees were used to develop and validate i) a two-category QSAR model (toxic/non-toxic; n=411) (sensitivity =0.93), specificity =0.85), balanced accuracy =0.90), Matthews correlation coefficient MCC=0.78), and ii) a regression-based model (n=113) (R2=0.74; MAE=0.52). Similarly, current study proposes the first MoA profiling for 113 pesticides active substances and the first harmonised MoA classification scheme for acute contact toxicity in honey bees, including LD<sub>50s</sub> data points from three different databases such as EFSA's OpenFoodTox, US-EPA ECOTOX and Pesticide Properties DataBase. Such classification allows to further define MoAs and the target site of Plant Protection Products (PPPs) active substances, thus enabling regulators and scientists to refine chemical grouping and toxicity extrapolations for single chemicals and component-based mixture risk assessment of multiple chemicals.</p> <p>The full data collection and analysis of QSAR models, toxicity data (LD<sub>50</sub>) and Mode of Action (Moa) data are described in Carnesecchi et al., 2020 (DOI: doi.org/10.1016/j.scitotenv.2020.139243).</p> <p>This work was supported by the European Food Safety Authority (EFSA) [contract number: OC/EFSA/SCER/2018/01 and NP/EFSA/AFSCO/2016/02 (Edoardo Carnesecchi)].</p>
Data from: Accuracy of genomic selection models in a large population of open-pollinated families in white spruce
Genomic selection (GS) is of interest in breeding because of its potential for predicting the genetic value of individuals and increasing genetic gains per unit of time. To date, very few studies have reported empirical results of GS potential in the context of large population sizes and long breeding cycles such as for boreal trees. In this study, we assessed the effectiveness of marker-aided selection in an undomesticated white spruce (Picea glauca (Moench) Voss) population of large effective size using a GS approach. A discovery population of 1694 trees representative of 214 open-pollinated families from 43 natural populations was phenotyped for 12 wood and growth traits and genotyped for 6385 single-nucleotide polymorphisms (SNPs) mined in 2660 gene sequences. GS models were built to predict estimated breeding values using all the available SNPs or SNP subsets of the largest absolute effects, and they were validated using various cross-validation schemes. The accuracy of genomic estimated breeding values (GEBVs) varied from 0.327 to 0.435 when the training and the validation data sets shared half-sibs that were on average 90% of the accuracies achieved through traditionally estimated breeding values. The trend was also the same for validation across sites. As expected, the accuracy of GEBVs obtained after cross-validation with individuals of unknown relatedness was lower with about half of the accuracy achieved when half-sibs were present. We showed that with the marker densities used in the current study, predictions with low to moderate accuracy could be obtained within a large undomesticated population of related individuals, potentially resulting in larger gains per unit of time with GS than with the traditional approach.
Data from: An R package for analyzing survival using continuous-time open capture-recapture models
Capture–recapture software packages have proven to be very powerful tools for analysing factors affecting survival in wild populations. However, all such packages are limited to discrete-time protocols. Appropriate survival analysis tools are still lacking for data acquired from continuous-time protocols. We have developed a statistical method and propose an r package for analysing such data based on an extension of classical survival analysis models incorporating an inhomogeneous Poisson process for modelling capture histories. First, data were simulated from a continuous-time protocol. These data were used to (i) compare survival estimation biases of discrete- and continuous-time approaches and (ii) investigate the performance and accuracy of our r package for four types of covariates: factors varying between individuals (like sex), in time (like climatic factors), both in time and between individuals (like physical condition) and age (as a categorical factor). Secondly, the r package has been applied to a real data set for survival analysis of cats in the Kerguelen archipelago (regrouping 682 cats over 20 years) as an illustrative example. Results of the simulated data analysis show that the method performs better than its discrete-time counterpart for analysing data acquired from continuous-time protocols. It provides unbiased parameter estimates for all parameters except those that vary both in time and between individuals – which is not surprising, since in our case, these factors were not updated in continuous time (i.e. only upon capture). When applied to the Kerguelen cat data set, the results suggest that survival is lower in juveniles than in adults and subadults, varies between study sites and increases with physical condition, and this latter effect being more important in females than in males. Sex, season, temporal linear trend in survival and the NDVI vegetation index were also tested but were not found to be significant. However, confidence intervals were too large (due to a low recapture rate) for excluding such effects. Further analyses are still needed for rigorous covariate testing in this context. In conclusion, continuous-time approaches – such as that presented in this paper – should be preferred when data acquired from continuous-time protocols is analysed.
OPEN-KTH-3dMODELS: An Open Dataset of Building Models at KTH Campus Valhallavägen
<p>OPEN-KTH-3dMODELS: An open dataset of building models at KTH Campus Valhallavägen</p> <ul> <li>Open-KTH-3dModels is a subproject of the AD-EYE testbed for Automated Driving and Intelligent Transportation Systems.</li> <li>The dataset comprises of a series .blend files that have prominent buildings from KTH campus Valhallavägen. </li> <li>The dataset also contains PreScan compatible models that can be used wtih AD-EYE (<a href="https://www.adeye.se/open-kth-3dmodels">https://www.adeye.se/</a>)</li> </ul> <p>Visualisation video: https://www.youtube.com/watch?v=F6NfCiul3oE<br>Learn more at <a href="https://www.adeye.se/open-kth-3dmodels">https://www.adeye.se/open-kth-3dmodels</a> or contact <a href="mailto:adeye@md.kth.se">adeye@md.kth.se</a></p> <p> </p> <p>The AD-EYE testbed is based on the design presented in the work <strong>"<em>AD-EYE: A Co-Simulation Platform for Early Verification of Functional Safety Concepts"</em></strong></p> <p> </p> <p><strong>Original paper:</strong> <a href="https://doi.org/10.4271/2019-01-0126">https://doi.org/10.4271/2019-01-0126</a></p> <p><strong>Preprint available at: </strong><a href="https://arxiv.org/abs/1912.00448">https://arxiv.org/abs/1912.00448</a></p> <p><strong>Citation:</strong></p> <p>Naveen Mohan, Martin Törngren, "AD-EYE: A Co-Simulation Platform for Early Verification of Functional Safety Concepts", SAE Technical Paper 19AE-0203/2019-01-0126, <a href="https://doi.org/10.4271/2019-01-0126">https://doi.org/10.4271/2019-01-0126</a></p> <p> </p> <p><strong>Notes:</strong></p> <p>Modelling work primarily performed by Lester Jose, during his internship with AD-EYE.</p>
COS-1m: the first community-scale open space maps of 31 major cities in China created with the layered occlusion perception model
<p>Correspondence to:<br>Shihong Du<br>Peking University<br>Beijing 100871, China<br>E-mail: shdu@pku.edu.cn</p> <p>COS-1m (Community Open Spaces) offers detailed, high-resolution maps of community-level open spaces across 31 major cities in China. Developed using the latest very high-resolution satellite imagery and a novel Layered Occlusion Perception Model (LOPM), this dataset ensures precise representation with a spatial resolution of 1 meter. In total, the COS-1m dataset covers an area of 46727.46 square kilometers.</p> <p>The COS-1m dataset addresses the critical challenge of layered occlusions in remote sensing imagery by accurately modeling and reconstructing the intricate layered structures of COS. This approach allows for the detailed delineation of occluded ground elements, overcoming the limitations of traditional single-layer mapping methods. The dataset reveals that, on average, 60.51 km² of COS area per city is occluded, constituting 10.18% of the total COS area, which had previously been overlooked.</p> <p>Key features of the COS-1m dataset include:</p> <ul> <li><strong>Four Types of Public Open Space Elements</strong>: Green spaces (1), blue spaces (2), squares (4), and walkways (6).</li> <li><strong>Three Types of Coupled Open Spaces</strong>: waterfront woodlands (3), shaded squares (5), and shaded walkways (7).</li> </ul> <p>The dual-layer mapping technique used in COS-1m provides a comprehensive overview of urban open spaces, capturing the vertical overlaps of various elements such as trees, water bodies, squares, and walkways. This dataset is a significant advancement in COS monitoring technology, filling a critical gap in community-scale Sustainable Development Goal (SDG) assessments. It provides urban planners and policymakers with valuable insights to promote more effective and sustainable urban development.</p> <p>COS-1m achieved an overall accuracy of 86.39% and an average F1-score of 77.47% across the 31 cities, demonstrating its robustness and reliability as a tool for urban space analysis and planning.</p> <p>If you use this dataset, please cite: "Lei, Y., Zhang, X., Xiong, S., Tan, G., & Du, S. (2025). Developing Layered Occlusion Perception Model: Mapping community open spaces in 31 China cities. Remote Sensing of Environment, 316, 114498."</p> <p> </p>
Machine Learning Potential for Modelling H2 Adsorption/Diffusion in MOFs with Open Metal Sites
<p>Data for reproducibility of the paper "Machine Learning Potential for Modelling H2 Adsorption/Diffusion in MOFs with Open Metal Sites"</p> <p>Corresponding paper:</p> <p>ShanPing Liu, Romain Dupuis, Dong Fan, Salma Benzaria, Mickaele Bonneau, Prashant Bhatt, Mohamed Eddaoudi, and Guillaume Maurin. "Machine Learning Potential for Modelling H2 Adsorption/Diffusion in MOFs with Open Metal Sites." <em>Chemical Science</em> (2024). https://doi.org/10.1039/D3SC05612K</p>
Data underlying research paper "Developing an open data intermediation business model: insights from the case of Esri"
<p><strong>Data underlying research paper “Developing an open data intermediation business model: insights from the case of Esri” </strong></p> <p>by Ashraf Shaharudin, Bastiaan van Loenen, and Marijn Janssen from Delft University of Technology (TU Delft), the Netherlands.</p> <p>This folder contains data underlying the research paper “Developing an open data intermediation business model: insights from the case of Esri”. It consists of:</p> <p>1. De-identified interview transcripts</p> <p>2. Informed consent form template</p> <p><strong>Note about the de-identified interview transcripts:</strong></p> <p>The de-identified interview transcripts should be read in the context of the research on open data ecosystem and the role of Esri as open data intermediaries.</p> <p>The 27 interviews, involving 29 interviewees, were conducted between April 2023 and April 2024 based on the semi-structured approach. We shared the tentative interview questions with the interviewees in advance (for the majority, at least three working days prior). Since they are semi-structured interviews, the ultimate interview questions may differ from the tentative questions.</p> <p>We removed personally identifiable information from the transcripts. Some interviewees may risk being identifiable if their organization is known. Hence, we removed the organization and country information from all transcripts. </p> <p>With verbal communication, some sentences may be less incomprehensible in writing. Thus, we did minimal edits when transcribing to improve the comprehensibility where necessary, but the main objective was to keep the transcripts as close to verbatim as possible. </p> <p><strong>Note about the informed consent form template:</strong></p> <p>We sent the informed consent form to every interviewee in advance and requested that they return it to us before or during the interview. </p> <p>All interviewees whose interview transcripts are recorded in this document give permission for the anonymized transcript of their interview, with personally identifiable information redacted, to be shared in 4TU.ResearchData repository so it can be used for future research and learning.</p> <p><strong>Acknowledgement:</strong></p> <p>This research is part of the 'Towards a Sustainable Open Data ECOsystem' (ODECO) project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 955569. The opinions expressed in this document reflect only the author’s view and in no way reflect the European Commission’s opinions. The European Commission is not responsible for any use that may be made of the information it contains.</p>
Mapping the Landscape of Open Source Health Economic Models: A Systematic Database Review and Analysis
<p><span>Health economic models are crucial for health technology assessment (HTA) to evaluate the value of medical interventions. Open source models (OSMs), where source code and calculations are publicly accessible, enhance transparency, efficiency, credibility, and reproducibility. This study systematically reviews databases to map the landscape of available OSMs in health economics.</span></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.