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Relate-estimated coalescence rates, allele ages, and selection p-values for the 1000 Genomes Project
<p><strong>Overview</strong></p> <p>Coalescence rates, allele ages, and p-values for evidence of positive selection calculated for 2478 samples of the 1000 Genomes Project using Relate.</p> <p>We estimated the joint genealogy of all 1000 GP populations and then extracted the embedded genealogy for each population.<br> For the genealogy of each population, we jointly estimated the population size history and branch lengths. <br> Variants segregating in more than one population therefore have correlated but different allele ages in each population.</p> <p>Please refer to <a href="https://www.nature.com/articles/s41588-019-0484-x">Speidel et al. Nature Genetics (2019)</a> for more details or email leo.speidel@outlook.com for any queries.</p> <p><strong>Coalescence rates</strong></p> <p>The zipped directory coalescence_rates.zip contains coalescence rates for 26 populations in the 1000 Genomes Project data set.</p> <ul> <li>The .coal files show the haploid coalescence rates, please refer to the <a href="https://myersgroup.github.io/relate/modules.html#PopulationSizeScript_FileFormats">Relate documentation</a> for the file format.</li> <li>The popsize.RData file is an R data frame storing the diploid population sizes (0.5/coalescence rate) calculated using the .coal files. The columns of this data frame, named "pop_size", are <ul> <li>gens_ago: Time in generations at which epoch starts. (To get years from generations, we multiply by 28.)</li> <li>population_size: Diploid population size in this epoch.</li> <li>population: Name of population </li> <li>region: Name of region (AFR, AMR, EAS, EUR, SAS)</li> </ul> </li> </ul> <p><strong>Allele ages and selection p-values</strong></p> <p>The zipped directories allele_ages_*.zip contain R data frames for each 1000GP population storing allele ages and selection p-values.<br> Please note that only mutations that segregate in the population and map to a unique branch in the Relate-estimated marginal trees are included. Selection p-values are only provided for mutations of DAF > 2 that pass quality filters (see Speidel et al., 2019). </p> <p>To get an age estimate for a neutral mutation, use 0.5*(lower_age + upper_age). To get years from generations, we multiply by 28.</p> <p>The columns of these data frames, named "allele_ages", are</p> <ul> <li>CHR: chromosome index</li> <li>BP: base-pair position (GRCh37)</li> <li>ID: id of SNP</li> <li>lower_age: Age in generations of coalescence event at the lower end of the branch onto which the mutation maps</li> <li>upper_age: Age in generations of coalescence event at the upper end of the branch onto which the mutation maps</li> <li>ancestral/derived: Ancestral/derived allele</li> <li>upstream: Upstream (5') allele</li> <li>downstream: Downstream (3') allele</li> <li>DAF: Derived-allele frequency</li> <li>pvalue: log10 p-value for selection evidence</li> </ul>
Monitoring and evaluation of UKRI's Open Access Policy: Exploring the use of open data sources to inform baseline values - Dataset
<p>This dataset accompanies the report <em>"Monitoring and evaluation of UKRI's Open Access Policy: Exploring the use of open data sources to inform baseline values"</em>, which is available via Zenodo.<br><br>It provides record-level data of UKRI-funded and UK-affiliated research output (limited to journal articles with Crossref DOIs) published between 2012 and 2022 - including bibliographic metadata as well as data on open access availability, publisher, national and international collaborations, citations, views and downloads, altmetrics and subjects (fields). All variables are documented in the data dictionary included in this Zenodo record.</p> <p>The code used to generate the dataset from open data sources is available on GitHub. </p> <p>The following data sources were used:</p> <ul> <li> <p>Gateway to Research (records downloaded between 2023-11-05 and 2023-11-13)</p> </li> <li> <p>Crossref (Metadata Plus snaphot 2023-10-31, Crossref member route API 2024-01-23)</p> </li> <li> <p>OpenAlex (data snapshot 2023-10-18)</p> </li> <li> <p>Unpaywall (data snapshot 2023-11-27)</p> </li> <li> <p>IRUS UK (2024-04-03)</p> </li> <li> <p>Crossref Event Data (2023-04-01)</p> </li> </ul> <p><strong></strong><br><br>The project made use of Curtin Open Knowledge Initiative (COKI) infrastructure, which is documented on GitHub: <a href="https://github.com/The-Academic-Observatory">https://github.com/The-Academic-Observatory</a>. </p>
Comparative analysis of ADC values between high-cost (US331-000005-030PA) and low-cost (B07YZLCSRP) depth sensors
<p>The dataset provides a comparison between an expensive depth sensor, the "US331-000005-030PA", and a cheaper sensor, the "B07YZLCSRP". The dataset includes the ADC values from both sensors as well as the offset between them.</p> <p>Data were collected using an autonomous underwater profiler called s-Nautilus at the Real Club de Regatas de Cartagena. During this test, the s-Nautilus profiler was moved to various depths, and the time and 12-bit ADC values from both sensors were recorded.</p> <p>The recorded variables include:</p> <ul> <li><strong>timestamp UNIX (s):</strong> the timestamp indicating the date and time of each measurement.</li> <li><strong>hours (hh:mm:ss):</strong> time of recording of each measurement.</li> <li><strong>incr_time (s): </strong>cumulative time increment for each measurement.</li> <li><strong>ADC cheap sensor (unit of ADC of 12 bits): </strong>12-bit ADC values of depth sensor "B07YZLCSRP" at various depths of the s-Nautilus.</li> <li><strong>ADC expensive sensor (unit of ADC of 12 bits):</strong> 12-bit ADC values of depth sensor "US331-000005-030PA" at various depths of the s-Nautilus.</li> <li><strong>ADC difference (unit of ADC of 12 bits):</strong> difference in ADC values between the two sensors.</li> <li><strong>ADC + offset (unit of ADC of 12 bits):</strong> ADC values of depth sensor "B07YZLCSRP" adjusted by calculated offset.</li> <li><strong>average ADC differences (unit of ADC of 12 bits):</strong> average offset ADC for all measurements.</li> </ul>
Biocultural ecoutourism values per municipality in Colombia
<p>A dataset with eight columns with the output values for the analyses done in the Echeverri, et al. (2024) paper published in People and Nature called: "Toward Sustainable Biocultural Ecotourism: An Integrated Spatial Analysis of Cultural and Biodiversity Richness in Colombia"</p> <p>This data set has the following components:</p> <p>[1]Code_DANE: Unique code ID assigned to each municipality according to the National Statistics Bureau DANE to each of the 1,121 municipalities</p> <p>[2]Departamento: Name of the Departamento (equivalent to state or province) for each municipality</p> <p>[3]Region: Ecoregion where the municipality is located (either in Amazonia, Orinoquia, Caribbean, Pacific, Andean)</p> <p>[4]Biodiversity_index_scaled: Results of the biodiversity richness index calculated in this paper and scaled from 0 to 1, where 1 is the highest possible value, and 0 is the lowest value</p> <p>[5]Cultural_index_scaled: Results of the institutionalized cultural richness index calculated in this paper and scaled from 0 to 1, where 1 is the highest possible value, and 0 is the lowest value</p> <p>[6]Accesibility_index_scaled: Results of the accesibility index calculated in this paper and scaled from 0 to 1, where 1 is the highest possible value, and 0 is the lowest value</p> <p>[7]Demand_index_scaled: Results of the demand index calculated in this paper and scaled from 0 to 1, where 1 is the highest possible value, and 0 is the lowest value</p> <p> </p>
Data set accompanying the research article "Complete representation of action space and value in all striatal pathways"
<p>GCaMP6s calcium imaging data set recorded from freely behaving mice performing open field and 2-choice decision-making tasks using miniscopes. Mice were implanted in the right dorsomedial striatum and three types of output neurons were genetically targeted using transgenic Cre-lines. The data set comprises single-cell spatial filters and calcium activity traces extracted using CaImAn (https://github.com/flatironinstitute/CaImAn) as well as behavioral event logs and tracking coordinates. For more details please refer to the article "Complete representation of action space and value in all striatal pathways" published by the data sets' authors. Analysis code can be found at https://doi.org/10.5281/zenodo.5034618.</p>
Gravitational waves from a cosmological vacuum phase transition - scalar field value
<p>Movie based on Figure 2 of the paper <a href="https://doi.org/10.1103/PhysRevD.97.123513">Gravitational waves from vacuum first-order phase transitions: from the envelope to the lattice</a> [<a href="https://arxiv.org/abs/1802.05712">arXiv:1802.05712</a>]. This movie originally appeared as Supplemental Material associated with the paper.</p> <p><em>Caption based on original figure caption</em>: Slices through a simultaneous nucleation simulation with parameters <span class="math-tex">\(R_\mathrm{c} M = 7.15\)</span>, <span class="math-tex">\(N_\mathrm{b} = 64\)</span> and <span class="math-tex">\(R_* M = 56.32\)</span> showing the expansion, collision, and oscillatory phase of the scalar field. The scalar field value is shown in blue, and the gravitational wave energy density is shown in red. Note that the range of the colourbar for the gravitational wave energy density changes with time. During the oscillatory phase the gravitational wave energy density becomes very uniform and the “hotspots” are deviations on the sub percent level.</p> <p>The movie is also available on Vimeo, <a href="https://vimeo.com/255031420">here</a>.</p>
Rideshare Dataset without Missing Values
<p>This dataset contains various hourly time series representations of attributes related to Uber and Lyft rideshare services for various locations in New York between 26/11/2018 and 18/12/2018. </p> <p>For a given starting location, provider and service, the following types are represented: 'price_min', 'price_mean', 'price_max', 'distance_min', 'distance_mean', 'distance_max', 'surge_min', 'surge_mean', 'surge_max', 'api_calls', 'temp', 'rain', 'humidity', 'clouds' and 'wind'.</p> <p>The original dataset contains missing values and they have been simply replaced by zeros. </p>
Vehicle Trips Dataset without Missing Values
<p>This dataset contains 329 daily time series representing the number of trips and vehicles belonging to a set of for-hire vehicle (FHV) companies. The dataset was originally extracted from https://github.com/fivethirtyeight/uber-tlc-foil-response.</p> <p>The original dataset contains missing values and they have been replaced by carrying forward the corresponding last seen observations (LOCF method).</p>
Bitcoin Dataset without Missing Values
<p>This dataset contains the potential influencers of the bitcoin price. There are a total of 18 daily time series including hash rate, block size, mining difficulty etc. It also encompasses public opinion in the form of tweets and google searches mentioning the keyword bitcoin. The data is scraped from the interactive web-graphs available at https://bitinfocharts.com. </p> <p>The original dataset contains missing values and they have been replaced by carrying forward the corresponding last seen observations (LOCF method).</p>
Rideshare Dataset with Missing Values
<p>This dataset contains various hourly time series representations of attributes related to Uber and Lyft rideshare services for various locations in New York between 26/11/2018 and 18/12/2018. </p> <p>For a given starting location, provider and service, the following types are represented: 'price_min', 'price_mean', 'price_max', 'distance_min', 'distance_mean', 'distance_max', 'surge_min', 'surge_mean', 'surge_max', 'api_calls', 'temp', 'rain', 'humidity', 'clouds' and 'wind'.<br> </p>
Bitcoin Dataset with Missing Values
<p>This dataset contains the potential influencers of the bitcoin price. There are a total of 18 daily time series including hash rate, block size, mining difficulty etc. It also encompasses public opinion in the form of tweets and google searches mentioning the keyword bitcoin. The data is scraped from the interactive web-graphs available at https://bitinfocharts.com.</p>
Vehicle Trips Dataset with Missing Values
<p>This dataset contains 329 daily time series representing the number of trips and vehicles belonging to a set of for-hire vehicle (FHV) companies. The dataset was originally extracted from https://github.com/fivethirtyeight/uber-tlc-foil-response.</p>
Temperature Rain Dataset without Missing Values
<p>This dataset contains 32072 daily time series showing the temperature observations and rain forecasts, gathered by the Australian Bureau of Meteorology for 422 weather stations across Australia, between 02/05/2015 and 26/04/2017.</p> <p>The original dataset contains missing values and they have been simply replaced by zeros.<br> </p>
Temperature Rain Dataset with Missing Values
<p>This dataset contains 32072 daily time series showing the temperature observations and rain forecasts, gathered by the Australian Bureau of Meteorology for 422 weather stations across Australia, between 02/05/2015 and 26/04/2017.<br> </p>
IMPACT HTA, WP7 (Methodological tools using multi-criteria value methods for HTA decision-making), Task 2 (Multi-criteria evaluation framework), Results of the 2nd Web-Delphi process to HTA stakeholders, organized in a single panel
<p>IMPACT HTA, WP7 (Methodological tools using multi-criteria value methods for HTA decision-making), Task 2 (Multi-criteria evaluation framework), Results of the 2<sup>nd</sup> Web-Delphi process to HTA stakeholders, organized in a single panel (all stakeholder groups in a single panel, 2 rounds), about the views of stakeholders regarding “This aspect should be considered in the evaluation of new medicines on a common basis” (2019)</p> <p>For details on the Web-Delphi process, see: IMPACT HTA, Work Package 7 (Methodological tools using multi-criteria value methods for HTA decision-making), Task 2, Deliverable 7.2 (Multi-criteria evaluation framework), Advancing knowledge and MCDA tools to assist HTA agencies in evaluating medicines on a common basis (2021) Oliveira, M.D. (IST), Panos Kanavos (LSE), Bana e Costa, C. (IST)</p>
IMPACT HTA, WP7 (Methodological tools using multi-criteria value methods for HTA decision-making), Task 2 (Multi-criteria evaluation framework), Results of the 1st Web-Delphi process to HTA stakeholders, organized into 6 separate parallel panels
<p>IMPACT HTA, WP7 (Methodological tools using multi-criteria value methods for HTA decision-making), Task 2 (Multi-criteria evaluation framework), Results of the 1<sup>st</sup> Web-Delphi process to HTA stakeholders, organized into 6 separate parallel panels (one panel per stakeholder group, 2 rounds), about the views of stakeholders regarding “This aspect should be considered in the evaluation of new medicines on a common basis” (2019)</p> <p>For details on the Web-Delphi process, see: IMPACT HTA, Work Package 7 (Methodological tools using multi-criteria value methods for HTA decision-making), Task 2, Deliverable 7.2 (Multi-criteria evaluation framework), Advancing knowledge and MCDA tools to assist HTA agencies in evaluating medicines on a common basis (2021) Oliveira, M.D. (IST), Panos Kanavos (LSE), Bana e Costa, C. (IST)</p>
Values, distributions and approximations of the empirical liquidity cost function for various futures contracts.
<p>The figures presents the values, distributions and approximations of the empirical liquidity cost function for various futures contracts. The raw data was obtained from the LOB snapshots for the cash-settled futures contracts on the RTS index (RI), on Brent oil (BR) and FX-rate of US dollar versus Russian ruble (Si). The data corresponds to the period from 05 May 2020 to 26 Feb 2021. The tables summarize the results.</p>
Case study result data set for Energy Economics (submitted) article "On Wholesale Electricity Prices and Market Values in a Carbon-Neutral Energy System"
<p>The data set contains wholesale power price time series data for Germany and France focussing on price setting effects in a long term low carbon European energy system context (scenario year 2050) generated with the model SCOPE SD of Fraunhofer Institute for Energy Economics and Energy System Technology IEE. The single time series are focussing on the price setting effects of different flexible technologies including both traditional and new market participants due to cross-sectoral integration.</p> <p>Unit: Euro/Megawatthour</p> <p><strong>Abbreviations:</strong></p> <ul> <li>BEV - Battery Electric Vehicles</li> <li>GER - Germany</li> <li>FRA - France</li> <li>OCGT - Open Cycle Gas Turbine</li> <li>PHEV - Plug-In Hybrid Vehicles</li> <li>RES - Renewable energy sources (here: wind and solar power)</li> <li>th. - thermal</li> </ul>
Extended Wikipedia Web Traffic Daily Dataset (without Missing Values)
<p>This dataset contains 145063 time series representing the number of hits or web traffic for a set of Wikipedia pages from 2015-07-01 to 2022-06-30. This is an extended version of the dataset that was used in the Kaggle Wikipedia Web Traffic forecasting competition. For consistency, the same Wikipedia pages that were used in the competition have been used in this dataset as well. The colons (:) in article names have been replaced by dashes (-) to make the .tsf file readable using our <a href="https://github.com/rakshitha123/TSForecasting/tree/master/utils">data loaders</a>.</p> <p>The original dataset contains missing values. They have been simply replaced by zeros.</p> <p>The data were downloaded from the <a href="https://wikimedia.org/api/rest_v1/#/Pageviews%20data/get_metrics_pageviews_per_article__project___access___agent___article___granularity___start___end_">Wikimedia REST API</a>. According to the conditions of the API, this dataset is licensed under <a href="https://creativecommons.org/licenses/by-sa/3.0/">CC-BY-SA 3.0</a> and <a href="https://www.gnu.org/licenses/fdl-1.3.html">GFDL</a> licenses.</p>
Extended Wikipedia Web Traffic Daily Dataset (with Missing Values)
<p>This dataset contains 145063 time series representing the number of hits or web traffic for a set of Wikipedia pages from 2015-07-01 to 2022-06-30. This is an extended version of the dataset that was used in the Kaggle Wikipedia Web Traffic forecasting competition. For consistency, the same Wikipedia pages that were used in the competition have been used in this dataset as well. The colons (:) in article names have been replaced by dashes (-) to make the .tsf file readable using our <a href="https://github.com/rakshitha123/TSForecasting/tree/master/utils">data loaders</a>.</p> <p><br> The data were downloaded from the <a href="https://wikimedia.org/api/rest_v1/#/Pageviews%20data/get_metrics_pageviews_per_article__project___access___agent___article___granularity___start___end_">Wikimedia REST API</a>. According to the conditions of the API, this dataset is licensed under <a href="https://creativecommons.org/licenses/by-sa/3.0/">CC-BY-SA 3.0</a> and <a href="https://www.gnu.org/licenses/fdl-1.3.html">GFDL</a> licenses.</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.