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MERLIN H2020 Project: Video of Faces of MERLIN: Dr Cristina Berges
<p>Video of Faces of MERLIN: Dr Cristina Berges, 2019</p>
MERLIN H2020 Project: Video of Faces of MERLIN; Dr. Wojciech Józefowicz (medVC)
<p>Project: Video of Faces of MERLIN; Dr. Wojciech Józefowicz (medVC), 2019</p>
RESCCUE (RESilience to cope with Climate Change in Urban arEas) EU Project - WP1 Data
<p>These files contain the data generated for the Work Package 1 of the RESCCUE project. RESCCUE project was devised to analyse future urban impacts due to climate change so as to improve resilience of three target cities: Barcelona, Bristol and Lisbon. To achieve that, future climate projections and changes in extreme events were obtained at a local scale for the Work Package 1. Several past studies were analysed to identify all the climate variables and extreme events that could affect urban areas, e.g. heavy rainfall, heat waves and storm surge. All available meteorological observations in the considered areas were collected and filtered through several tests (general consistency, outliers and inhomogeneities) in order to handle datasets long enough and of good quality. As a way to obtain the best input possible, every valid station was extended in time by downscaling process with the ERA-Interim reanalysis.</p> <p>Future climate projections were obtained for ten different global climate models considering two of the main Representative Concentration Pathways (RCP4.5 and RCP8.5) established in the last IPCC report. These models were downscaled through a sophisticated statistical methods (analogous stratification and transfer functions among others) to project local climate according to the identified climate drivers: temperature, precipitation, wind, relative humidity, sea level pressure, potential evapotranspiration, snowfall, wave height and sea level; and for both climate and decadal timescales. Already downscaled models were first validated for the method and afterwards verified, obtaining small errors and good coherent simulations.</p> <p>Extreme events of the main climate drivers were obtained and analysed for both historical and future scenarios through the combination of several statistical methods as well as through the analysis of several teleconnectionpatterns. Derived events such as heat waves, drought, snowstorms, storm surges, wave height among others were afterwards inferred for climate, decadal and seasonal scale.</p> <p><strong>FILES</strong></p> <p>The data generated have been grouped into three different files, one for each studied area: the hydrological basin of the rivers Ter and Llobregat (the area that influences Barcelona), the geographical area between England and South Wales (the area that influences Bristol) and the Lisbon area.</p> <p>Each of the files contains a self-explanatory file detailing the structure of the information contained and the way in which it is provided.</p> <p><strong>ABOUT THE RESCCUE PROJECT</strong></p> <p>The RESCCUE project, Resilience to cope with Climate Change in Urban Areas, –a multisectorial approach focusing on water– aims to provide practical and innovative models and tools to end-users facing climate change challenges to build more resilient cities.</p> <p>The project provides tools to assess urban resilience from a multisectorial approach, for current and future climate scenarios and including multiple hazards. This holistic approach to urban resilience will enable city managers and urban systems operators to decide the optimal investments to cope with future situations.</p> <p>For more information, please visit <a href="http://www.resccue.eu/">www.resccue.eu</a></p>
Biogeochemical model projections years 1982-2007 in North East Atlantic
<p>Marine enviromental data from Biogeochemical models in paper "Can we project changes in fish abundance and distribution in response to climate?" at Global Change Biology journal</p>
Distributed Science Infrastructure Projects
<p>This data set lists 43 projects and additional information in the field of distributed<br> science infrastructure. A basic list of project names and URLs was initially collected at the <em>Scientific Publishing on the Blockchain</em> unconference in 2018 in the hackpad on <a href="https://hackmd.io/QTaG8S3LQAeBfnCT-EFFfQ">https://hackmd.io/QTaG8S3LQAeBfnCT-EFFfQ</a> . We visited the websites during September and October 2019 and manually extracted links to source code, whitepapers and prototypes. Additionally, we added information like project descriptions, license information and social media accounts. Two more projects related to distributed computation were added in February 2020, during the review process of a data report on this data set.</p> <p>The classification of the projects is based on manual labelling by us. "active in 2019" refers to any obvious non-automatic activity on the website, in a source code repository or social media accounts. Due to the manual information extraction, we expect mistakes or missing data in all fields. This data collection should be seen as preliminary and will likely be replaced by a community driven effort in the future (watch this space for a future reference).</p> <p>Encoding: UTF8<br> <a>ipfs:QmRprZUfc7C8AnyvydrTr9WR4RRCRBQacM22XGSLwugGWh</a> </p>
RCP8.5-ECEARTH-RACMO-LARSIM_ME Climate Flow Projection Data for German Waterways
<p>The datasets provided here were produced as part of the IMPREX project for work package 4, task 4 „<em>Improving prediction on the climate scale</em>“ and work package 9, task 3 “<em>Case studies</em>”. Analysis of the datasets are published in Deliverable 4.4 „<em>Estimation of hazards based on improved representation of highly vulnerable water resources of strategic importance on the climate scale</em>“ (Falloon et al 2019). The aim was to study the impact of internal climate model variability and bias correction method on the climate change signal of relevant flow indicators for the German waterways Rhine, Elbe and Danube.</p> <p>To assess the impact of internal variability of the global climate model on future changes of flow, precipitation, temperature and global radiation of the 16-member ensemble generated with the RCM KNMI-RACMO2 driven by the GCM EC-EARTH 2.3 provided by WP3 of IMPREX were used. EC-EARTH was run 16 times from 1850 to 2100, each member starting from a slightly different initial state, under forcing of historical emissions until 2005 and the RCP8.5 greenhouse gas concentration pathway from 2006 onwards. Each of the EC-EARTH members was subsequently dynamically downscaled using KNMI-RACMO2 on a 0.11° (~12 km) resolved domain (Aalbers et al. 2018).</p> <p>To correct the systematic model biases of climate models different bias correction methods were applied: (1) no bias correction, (2) linear scaling (Lenderink et al. 2007) and (3) quantile-quantile mapping (Piani et al. 2010). Bias correction relationships were derived for five-day periods (for each variable and location, in total 73 bias correction relationships were derived) including 13 days before and after the considered five-day period (total window size was 31 days) from the observations and values of the regional climate simulations. The period used to estimate the bias correction relationships was 1971-2000.</p> <p>The hydrological model applied is called LARSIM-ME (ME – MittelEuropa = Central Europe) and is based in the model software LARSIM (Large Area Runoff SImulation Model) originally developed by Ludwig & Bremicker (2006). LARSIM-ME covers the catchments of the rivers Rhine, Elbe, Weser/Ems, Odra and Upper Danube. The total catchment size simulated by the model is approximately 800,000 km². The spatial resolution is 5 km x 5 km and the computational time-step is daily. As observed meteorological forcings, precipitation, air temperature and global radiation from the HYRAS data set (Rauthe et al. 2013) available for the 5 km x 5 km model grid and the period 1951-2015 were used. The hydrological model was calibrated using the automatic calibration scheme Shuffled Complex Evolution SCE-UA algorithm (Duan et al. 1994). For more details about the model see Meißner et al. (2017).</p> <p>The meteorological variables air temperature, precipitation and global radiation produced by the KNMI RACMO-EC-EARTH 16 member ensemble (period 1951-2100) were interpolated to a 25 km x 25 km grid and afterwards bias corrected with respect to the observation data (HYRAS) used for calibration of the hydrological model LARSIM. From this 25 km x 25 km grid the bias corrected variables were downscaled to the 5 km x 5 km model grid of LARSIM using monthly background climatology fields on the 5 km x 5 km target grid of the HYRAS dataset. The bias-corrected and downscaled data was then used as meteorological forcing of LARSIM to calculate flow projections for the rivers Rhine, Elbe and Upper Danube (up to the German/Austrian border).</p> <p><strong>Dataset Q_OBS_DE.nc:</strong></p> <p>Mean daily observed flow of the gauges Kaub, Koeln, Ruhrort / Rhine, Pfelling, Hofkirchen / Danube, Desden, Magdeburg Strombruecke, Neu-Darchau / Elbe for the period 1951–2017 stored as variable <em><strong>q_obs(time=24472, stations=8)</strong></em>.</p> <p>Data originate from the database of gauge measurements of the Federal Waterways and Shipping Administration (WSV). These data were quality checked and published by the gauge-operating WSV offices. Nevertheless, data errors and inconsistencies cannot be ruled out completely, so that neither the WSV nor the BfG do accept any liability for the correctness and completeness of the data. Data source: "German Federal Waterways and Shipping Administration (WSV)", provided by the German Federal Institute of Hydrology (BfG)</p> <pre><code>float q_obs(time=24472, stations=8); :units = "m3/s"; :_FillValue = -9999.0f; // float :long_name = "observed streamflow"; :coordinates = "lat lon";</code></pre> <p><strong>Dataset Q_HYRAS_LME.nc:</strong></p> <p>Mean daily simulated flow of the hydrological model LARSIM-ME forced by observed meteorology from the HYRAS dataset stored as variable <em><strong>q_sim (time=23741, stations=8)</strong></em>. Period 1951-2015, Gauges Kaub, Koeln, Ruhrort / Rhine, Pfelling, Hofkirchen / Danube, Desden, Magdeburg Strombruecke, Neu-Darchau / Elbe.</p> <pre><code>float q_sim(time=23741, stations=8); :units = "m3/s"; :_FillValue = -9999.0f; // float :long_name = "simulated streamflow"; :coordinates = "lat lon";</code></pre> <p><strong>Q_RCP85_ECEARTH_RACMO_[bc]_LME.nc:</strong></p> <p>Mean daily projected flow of the hydrological model LARSIM-ME forced by 16 realizations of RCP8.5-ECEARTH-RACMO stored as variable <em><strong>q_sim(time=54787, realization=16, stations=8)</strong></em>, first dimension time, second dimension realization and third dimension stations. Bias correction of meteorological forcings [bc]: NOBC: no bias correction, LS: linear scaling, QQMAP Quantile-Quantile Mapping. Period 1951-2100, Gauges Kaub, Koeln, Ruhrort / Rhine, Pfelling, Hofkirchen / Danube, Desden, Magdeburg Strombruecke, Neu-Darchau / Elbe.</p> <pre><code>float q_sim(time=54787, realization=16, stations=8); :units = "m3/s"; :_FillValue = -9999.0f; // float :long_name = "projected streamflow"; :coordinates = "lat lon";</code></pre> <p><strong>Literature</strong></p> <p>Aalbers, E. E., G. Lenderink, E. van Meijgaard & B. J. J. M. van den Hurk (2018): Local-scale changes in mean and heavy precipitation in Western Europe, climate change or internal variability? Climate Dynamics 50(11), 4745-4766</p> <p>Duan, Q., S. Sorooshian & V. K. Gupta (1994): Optimal use of the SCE-UA global optimization method for calibrating watershed models. Journal of Hydrology 158(3–4), 265-284</p> <p>Falloon, P., K. Williams, J. Andreu, A. Solera, S. Suárez-Almiñana, B. Klein, D. Meissner, J. Hunink, J. Eekhout & J. de Vente (2019): Estimation of hazards based on improved representation of highly vulnerable water resources of strategic importance on the climate scale. Deliverable 4.4, IMPREX - Improving Predictions of Hydrological Extremes - Grant Agreement Number 641811, <a href="https://imprex.eu/system/files/generated/files/resource/imprex-deliverablereport-d4-4-final-1.pdf">https://imprex.eu/system/files/generated/files/resource/imprex-deliverablereport-d4-4-final-1.pdf</a></p> <p>Lenderink, G., A. Buishand & W. van Deursen (2007): Estimates of future discharges of the river Rhine using two scenario methodologies: direct versus delta approach. Hydrology and Earth System Sciences 11(3), 1143-1159</p> <p>Ludwig, K. & M. Bremicker (2006): The Water Balance Model LARSIM –Design, Content and Applications. 22. C. Leibundgut, S. Demuth and J. Lange (Eds), Freiburger Schriften zur Hydrologie, Institut für Hydrologie, Universität Freiburg im Breisgau, Freiburg, 141 pp.</p> <p>Meißner, D., B. Klein & M. Ionita (2017): Development of a monthly to seasonal forecast framework tailored to inland waterway transport in central Europe. Hydrol. Earth Syst. Sci. 21(12), 6401</p> <p>Piani, C., J. O. Haerter & E. Coppola (2010): Statistical bias correction for daily precipitation in regional climate models over Europe. Theoretical and Applied Climatology 99(1-2), 187-192</p> <p>Rauthe, M., H. Steiner, U. Riediger, A. Mazurkiewicz & A. Gratzki (2013): A Central European precipitation climatology - Part I: Generation and validation of a high-resolution gridded daily data set (HYRAS). Meteorologische Zeitschrift 22(3), 235-256</p>
Supplementary material for "Measurement of volume change and mass transfer during serpentinisation: insights from the Oman Drilling Project" (JGR Solid Earth)
<p>The three supplementary files include one figure displaying the results of X-ray microtomography (File S1), two compilations of figures dealing with the volume change measurement (Files S2 and S3) and a table with the measured compositions in major and trace elements (Table S1).</p> <p>File S1 (Malvoisin_ds01.pdf). Result of X-ray microtomography. Olivine and Fe-brucite have a similar attenuation. The grains segmented by selecting this attenuation are displayed with a circle with a size proportional to the radius of a sphere of equivalent volume. The color of each circle depends on <em>e</em>, the aspect ratio (ratio of the length of the minimum and maximum axes of an ellipsoid fitted to each grain). The white arrow indicates the location of the main central vein. Grains near the main central vein have a lower <em>e</em> attributed to the platy habitus of Fe-brucite. The other grains are considered as olivine and used for the calculation of the extent of reaction.</p> <p>File S2 (Malvoisin_ds02.pdf). Optical photomicrographs used to calculate volume change during reaction (see main text for details about the calculation). Olivine grains (white) are surrounded by the serpentine + brucite mixture (light green to yellowish). The magnetite/clinopyroxene platelets are mapped in blue in olivine and in red in the serpentine + brucite mixture. The results of the model fitting the best the average orientation of the platelets in olivine are shown in green. The title of each photomicrograph provides the minimum and maximum estimates for volume change calculated with this model (see details about the calculation in the main text), and the size of the bottom right scale bar.</p> <p>File S3 (Malvoisin_ds03.pdf). Histograms of magnetite/clinopyroxene platelet orientation (in degree). Each histogram corresponds to a photomicrograph of File S1. The order of the histograms and the photomicrographs is the same. The orientations in the olivine grains, the serpentine + brucite mixture and for the model fitting the best the average orientation in olivine are displayed in blue, red and green, respectively. The title of the histograms provides the standard deviation of the platelet orientation in the olivine grains (STD<sub>oli</sub>), the serpentine + brucite mixture (STD<sub>ori</sub>) and calculated with the model fitting the best the orientation in olivine (STD<sub>cal</sub>).</p> <p>Table S1 (Malvoisin_ds01.xlsx). Composition in major and trace elements measured with LA-ICPMS along five profiles perpendicular to the main vein. The composition as a function of the distance to the main vein is provided. The name of the tabs indicates if the profile was acquired in spot or in continuous mode (the two modes are described in the main text).</p>
DRAFT experiment data of Fed4FIRE+ project
<p>The draft_experiment_data.zip contains four .json files with data generated during the DRAFT experiment which is a part of the Fed4FIRE+ Project - <a href="https://www.digiotouch.com/digiotouch-2nd-open-call-grant">https://www.digiotouch.com/digiotouch-2nd-open-call-grant</a></p> <p>1. bytebuffer - it contains data in JSON format that has been used as a part of a simulated DDoS attack to Cloud based<br> Web Services.</p> <p>2. denm - it contains Decentralized Environmental Notification Message (DENM) providing information related to a road <br> hazard or an abnormal traffic condition, including its type and position in JSON format. For more info, check the ETSI <br> draft - https://www.etsi.org/deliver/etsi_en/302600_302699/30263703/01.02.01_30/en_30263703v010201v.pdf</p> <p>3. senml_temp - it contains representation of an IoT temperature sensor metadata in JSON format. The format conforms with<br> Sensor Measurement Lists (SenML) standard from IETF - https://tools.ietf.org/pdf/rfc8428.pdf.</p> <p>4. subscription - it also contains data in JSON format used to perform a DDoS attack on the Cloud based Web Services.</p>
Project O2 - A Cooperative Bypassing Algorithm for Connected and Autonomous Vehicles in Mixed Traffic
<p>The dataset includes Python code and VISSIM file for the research paper "A Cooperative Bypassing Algorithm for Connected and Autonomous Vehicles in Mixed Traffic".</p> <p> </p> <p> </p>
Data from KdUINO. Loch Leven. H2020 MONOCLE project
<p>These data correspond to time series of light measurements taken at different depths.<br> The data was taken with low-cost sensors (light-frequency converters) within the framework of the H2020 MONOCLE project.<br> Data were obtained at Loch Leven, Scotland, during the first validation campaign for this project.<br> More information on the project at the link: https://monocle-h2020.eu/</p>
enCompass-datasets: Anonymized datasets of enCompass project
<p>Anonymized datasets of enCompass project.</p> <p>The enCOMPASS project collected data related to energy consumption in households, in schools and in public buildings, in three pilots located in Germany, Greece and in Switzerland.<br> <br> This repository contains enCompass datasets of behavioral data, households electrical consumption, sensor measurements data (indoor temperature, luminance and humidity) and anonymized user related data.</p> <p>Data in the repository are organized as follow:</p> <p>A metadata file enCOMPASS_METADATA.txt.<br> behavioral_datasets directory containing behavioral datasets.<br> household_consumption_and_sensor_data directory containing consumption and sensor datasets and a metadata file named enCOMPASS_consumption_sensor_METADATA.txt</p>
California Streamflow Projection Dataset
<p>This dataset consists of streamflow projection data for 20 basins in California. The files with 'RCP8.5' are for future projection under RCP8.5 forcing and 'hist' represents the projection with historical forcing. </p>
The SONOZOTZ project: assembling an echolocation calls library for bats in a megadiverse country
<p><span><span><span><span><span><span><span><span><span><span><span>Bat acoustic libraries are important tools that assemble echolocation calls to allow the comparison and discrimination to confirm species identifications. The Sonozotz project represents the first nation-wide library of bat echolocation calls for a megadiverse country. It was assembled following a standardized recording protocol that aimed to cover different recording habitats, recording techniques, and call variation inherent to individuals. The Sonozotz project included 69 species of echolocating bats, a high species richness that represents 50% of bat species found in the country. We include recommendations on how the database can be used and how the sampling methods can be potentially replicated in countries with similar environmental and geographic conditions. To our knowledge, this represents the most exhaustive effort to date to document and compile the diversity of bat echolocation calls for a megadiverse country. This database will be useful to address a range of ecological questions including the effects of anthropogenic activities on bat communities through the analysis of bat sound.</span></span></span></span></span></span></span></span></span></span></span></p>
edexter/MAR: Initial release of project
<p>Data and code pertaining "Modeling the trophic impacts of invasive zooplankton in a highly invaded river"</p>
MicroCT Data of Mice for Osteoarthritis Biomarkers Project
<p>MicroCT data of mice for Quantitative Image-Based Osteoarthritis Bio-markers project. This dataset contains original data (ORIG- in filename of zipped folders containing DICOM files), manual segmentations (MAN- in filename), and automatic segmentations (AUTO- in filename) used for benchmarking of automated 3D bone characterization tools created by Kitware, Inc.</p> <p>Original data from: Lau AG (2014) Joint bleeding in factor VIII deficient mice causes an acute loss of trabecular bone and calcification of joint soft tissues which is prevented with aggressive factor replacement. <em>Haemophilia</em> 20(5):716-722. PMCID: <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/pmc4396845/">PMC4396845</a>.</p> <p>Changes in version 1.0.1: Includes individual files for manual and automatic segmentations and corresponding original data in zipped folders. Version 1.0.0 contained a single zipped file with the entire dataset (MicroCT data mice osteoarthritis biomarkers.zip). See https://doi.org/10.5281/zenodo.3890144.</p>
Maps of reference evapotranspiration for the irrigation project in Brazil
<p>The maximum daily evapotranspiration data set for a project (ETproject) for Brazil. It has a spatial resolution of <strong>30 seconds (~ 1 km²)</strong>. The data set grid is in <strong>GeoTIFF format</strong> and corresponds perfectly to WorldClim. It uses the <strong>geographic coordinate</strong> reference system, with <strong>WGS84 projection (EPSG: 4326)</strong>.</p> <p>The objective study is to estimate and provide evapotranspiration values of monthly reference and the maximum of twelve months, for dimensioning irrigation systems throughout the Brazilian territory. With the meteorological data of two hundred and fifty-nine conventional INMET stations, the daily reference evapotranspiration (ETo) for 15 years was calculated. For each weather station, the data was grouped by month and the ETo for the irrigation project (ETproject) was determined to meet the eighty percent probability of occurrence, following the recommendations of FAO24. In parallel, monthly images of 15 years of ETo were acquired for Brazil, and the climatic variables of WorldClim. Using the ET values of the stations design, it was modeled for the rest of Brazil, using machine learning algorithms and the covariates. After modeling, the following performances were achieved: mean square error of 0.306 mm / d, mean bias error of -0.004 mm / d, mean absolute error of 0.227 mm / d, determination coefficient of 0.938 and efficiency of Nash-Sutcliffe 0.937. ETo values for irrigation projects were similar to several others reported in the literature when compared at a given point. With this research it was possible to determine the monthly and annual ETo for irrigation projects throughout the Brazilian territory.</p> <p>The article has been submitted for publication.</p>
Data set for "Projection-specific activity of layer 2/3 neurons imaged in mouse primary somatosensory barrel cortex during a whisker detection task"
<p>Data set for: Vavladeli A, Daigle T, Zeng H, Crochet S, Petersen CCH (2020) Projection-specific activity of layer 2/3 neurons imaged in mouse primary somatosensory barrel cortex during a whisker detection task. FUNCTION 1: zqaa008. doi: 10.1093/function/zqaa008</p> <p>There are 2 files in this upload:</p> <p>1. The file named "2020_Vavladeli_FUNCTION.pdf" is the Open Access pdf file of the manuscript published in FUNCTION.</p> <p>2. The file named "Vavladeli_data_code.zip" (~2 GB) is a zipped version of a folder named "Vavladeli_data_code" (~2 GB), which contains the data analysed in the study along with the Matlab code used to generate the published figures. When unzipped, the folder contains 8 Matlab '.m' files with analysis code and two '.mat' data files. In order to run the analysis of the data set, you need to execute the '.m' file with the corresponding figure name.</p>
Data & Open Science analysis of citizen science projects related with pollution
<p>These two spreadsheets contain the analysis done from a Data & Open Science perspective of citizen science projects related with pollution</p>
Dataset for the article "Dalton Project: A Python platform for molecular- and electronic-structure simulations of complex systems"
<p>This dataset contains additional material related to the article "Dalton Project: A Python platform for molecular- and electronic-structure simulations of complex systems". The article is available at <a href="https://doi.org/10.1063/1.5144298">https://doi.org/10.1063/1.5144298</a> (open access).<br> <br> Note that the current version of the dataset is not complete. The complete dataset will be uploaded as soon as possible.</p>
Amphibian survey of riparian buffer zones at SAFE Project, Borneo
<b>Description: </b><p>Results from amphibian surveys carried out along riparian transect sites at SAFE project, Borneo</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/152"><b>MRes Tropical Forest Ecology Field Course</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3973551">here</a></p><p><b>Files: </b>This consists of 1 file: frogs_data_rob.xlsx</p><p><b>frogs_data_rob.xlsx</b></p><p>This file contains dataset metadata and 3 data tables:</p><ol><li><p><b>Stream Overview</b> (described in worksheet StreamMeasuresParent)</p><p>Description: Survey overview details</p><p>Number of fields: 16</p><p>Number of data rows: 12</p><p>Fields: </p><ul><li><b>ec5_uuid</b>: EpiCollect 5 code (Field type: id)</li><li><b>created_at</b>: Date and time of measurement (Field type: date)</li><li><b>created_by</b>: Researcher contact email (Field type: id)</li><li><b>title</b>: Survey location and visit (Field type: id)</li><li><b>2_Stream_ID</b>: Stream idenity code (Field type: location)</li><li><b>RiparianWidth</b>: Riparian buffer width (Field type: numeric)</li><li><b>lat_3_GPS_Location</b>: Latitude (Field type: numeric)</li><li><b>long_3_GPS_Location</b>: Longitude (Field type: numeric)</li><li><b>accuracy_3_GPS_Location</b>: Accuracy of GPS location data (Field type: numeric)</li><li><b>4_Visit</b>: Visit number (Field type: id)</li><li><b>5_Photo_of_stream_la</b>: File ID of photo of river (Field type: id)</li><li><b>7_Start_Time</b>: Time sampling started (Field type: time)</li><li><b>8_Date_ddMMYYYY</b>: Calendar date of sampling (Field type: date)</li><li><b>9_Comments_weather_e</b>: Weather conditions (Field type: comments)</li><li><b>11_Surveyors</b>: Identity of surveyors (Field type: id)</li><li><b>12_RAs</b>: Identity of research assistants (Field type: id)</li></ul></li><li><p><b>Stream characteristics</b> (described in worksheet StreamMeasures)</p><p>Description: Stream characteristics (dimensions, canopy cover and substrate)</p><p>Number of fields: 19</p><p>Number of data rows: 105</p><p>Fields: </p><ul><li><b>ec5_parent_uuid</b>: EpiCollect 5 code (Field type: id)</li><li><b>created_at</b>: Date and time of measurement (Field type: datetime)</li><li><b>created_by</b>: Researcher contact email (Field type: id)</li><li><b>StreamID</b>: Stream idenity code (Field type: location)</li><li><b>15_Marker_point</b>: Identity of marker point (Field type: id)</li><li><b>SiteCode</b>: Location code of site (Field type: location)</li><li><b>Width</b>: Width of stream bed (Field type: numeric)</li><li><b>17_Upstream_Canopy</b>: Percentage of canopy cover upstream from point (Field type: numeric)</li><li><b>18_Downstream_Canopy</b>: Percentage of canopy cover downstream from point (Field type: numeric)</li><li><b>19_Left_Canopy</b>: Percentage of canopy cover to left of point (Field type: numeric)</li><li><b>20_Right_Canopy</b>: Percentage of canopy cover to right of point (Field type: numeric)</li><li><b>20_Large_boulder__co</b>: Percentage coverage by large boulders (Field type: numeric)</li><li><b>21_Medium_rock__cove</b>: Percentage coverage by medium rocks (Field type: numeric)</li><li><b>22_Small_rock__cover</b>: Percentage coverage by small rocks (Field type: numeric)</li><li><b>23_Gravel__cover</b>: Percentage coverage by gravel (Field type: numeric)</li><li><b>24_Sand__cover</b>: Percentage coverage by sand (Field type: numeric)</li><li><b>25_MudGround__cover</b>: Percentage coverage by mud (Field type: numeric)</li><li><b>26_Substrate_cover_o</b>: Percentage coverage by other substrate type (Field type: categorical)</li><li><b>27_Water_flow_at_poi</b>: Water flow at point (Field type: categorical)</li></ul></li><li><p><b>Frog species </b> (described in worksheet FrogData)</p><p>Description: Recorded frog species and associated body measurements</p><p>Number of fields: 19</p><p>Number of data rows: 785</p><p>Fields: </p><ul><li><b>Stream_ID</b>: Stream identification code (Field type: location)</li><li><b>Date</b>: Date (Field type: date)</li><li><b>23_Closest_Marker</b>: Distance along transect at which frog was recorded (Field type: numeric)</li><li><b>SiteCode</b>: Location code of recorded frog (Field type: location)</li><li><b>24_Horizontal_Distan</b>: Approximate distance of recorded frog from stream bed (Field type: numeric)</li><li><b>25_Vertical_Distance</b>: Approximate distance of recorded frog above stream bed (Field type: numeric)</li><li><b>27_Substrate</b>: Substrate on which frog was recorded (Field type: categorical)</li><li><b>28_Substrate_if_othe</b>: Secondary substrate on which frog was recorded (Field type: categorical)</li><li><b>30_Dorsal</b>: EpiCollect 5 code for photograph (dorsal) of recorded frog species (Field type: id)</li><li><b>31_Ventral</b>: EpiCollect 5 code for photograph (ventral) of recorded frog species (Field type: id)</li><li><b>32_Left_side</b>: EpiCollect 5 code for photograph (left-side) of recorded frog species (Field type: id)</li><li><b>33_Right_side</b>: EpiCollect 5 code for photograph (right-side) of recorded frog species (Field type: id)</li><li><b>Species</b>: Species or Genus level identity (Field type: taxa)</li><li><b>36_Mass_of_frog_in_bag</b>: Combined mass of frog and bag (Field type: numeric)</li><li><b>38_Mass_of_empty_bag</b>: Mass of bag alone (Field type: numeric)</li><li><b>Mass_of_frog</b>: Mass of frog (Field type: numeric trait)</li><li><b>40_SnoutVent_Length</b>: Snout-vent length of frog (Field type: numeric trait)</li><li><b>Comment</b>: Comments on frog identity (Field type: comments)</li><li><b>Sex</b>: Sex of frog (Field type: categorical trait)</li></ul></li></ol><p><b>Date range: </b>2019-02-04 to 2019-02-13</p><p><b>Latitudinal extent: </b>4.6498 to 4.7273</p><p><b>Longitudinal extent: </b>117.5413 to 117.6414</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div> -  Animalia <br> -  -  Chordata <br> -  -  -  Amphibia <br> -  -  -  -  Anura <br> -  -  -  -  -  [Other] <br> -  -  -  -  -  Bufonidae <br> -  -  -  -  -  -  <i>Ansonia</i> <br> -  -  -  -  -  -  -  <i>Ansonia spinulifer</i> <br> -  -  -  -  -  -  <i>Phrynoidis</i> <br> -  -  -  -  -  -  -  <i>Phrynoidis juxtaspera</i> <br> -  -  -  -  -  Dicroglossidae <br> -  -  -  -  -  -  <i>Limnonectes</i> <br> -  -  -  -  -  -  -  <i>Limnonectes finchi</i> <br> -  -  -  -  -  -  -  <i>Limnonectes kuhlii</i> <br> -  -  -  -  -  -  -  <i>Limnonectes leporinus</i> <br> -  -  -  -  -  -  -  <i>Limnonectes palavanensis</i> <br> -  -  -  -  -  -  <i>Ingerana</i> <br> -  -  -  -  -  -  -  <i>Ingerana baluensis</i> (as homotypic_synonym: <i>Occidozyga baluensis</i>)<br> -  -  -  -  -  -  <i>Fejervarya</i> <br> -  -  -  -  -  -  -  <i>Fejervarya limnocharis</i> <br> -  -  -  -  -  -  <i>Occidozyga</i> <br> -  -  -  -  -  -  -  <i>Occidozyga laevis</i> <br> -  -  -  -  -  Ranidae <br> -  -  -  -  -  -  <i>Chalcorana</i> <br> -  -  -  -  -  -  -  <i>Chalcorana megalonesa</i> <br> -  -  -  -  -  -  -  <i>Chalcorana raniceps</i> <br> -  -  -  -  -  -  <i>Meristogenys</i> <br> -  -  -  -  -  -  -  <i>Meristogenys orphnocnemis</i> <br> -  -  -  -  -  -  <i>Hylarana</i> <br> -  -  -  -  -  -  -  <i>Hylarana picturata</i> (as synonym: <i>Pulchrana picturata</i>)<br> -  -  -  -  -  -  <i>Staurois</i> <br> -  -  -  -  -  -  -  <i>Staurois guttatus</i> <br> -  -  -  -  -  -  -  <i>Staurois latopalmatus</i> <br> -  -  -  -  -  Megophryidae <br> -  -  -  -  -  -  <i>Leptolalax</i> <br> -  -  -  -  -  -  -  <i>Leptolalax fritinniens</i> <br> -  -  -  -  -  -  -  <i>Leptolalax gracilis</i> <br> -  -  -  -  -  -  <i>Leptobrachium</i> <br> -  -  -  -  -  -  -  <i>Leptobrachium abbotti</i> <br> -  -  -  -  -  Rhacophoridae <br> -  -  -  -  -  -  <i>Rhacophorus</i> <br> -  -  -  -  -  -  -  <i>Rhacophorus pardalis</i> <br> -  -  -  -  -  -  <i>Polypedates</i> <br> -  -  -  -  -  -  -  <i>Polypedates macrotis</i> <br></div><p></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.