Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
44
datasets available to search
ShareScore release 0.9.0
Dataset results
44 results for “2016-2018”
Solute dynamics in the hyporheic zone of a headwater stream in Watershed 1 at the Andrews Experimental Forest, 2016-2018
This project examined the interactions between stream water and subsurface sediment to quantify how these interactions influenced organic C respiration and dissolved inorganic C (DIC) production in the hyporheic zone of a high-gradient headwater mountain stream draining a forested catchment at the H. J. Andrews Experimental Forest, Oregon, USA. The study used six 2-m long hyporheic mesocosms which were packed with streambed sediment in the spring of 2016. The mesocosms are located at the Watershed 1 (WS1) stream gage and stream water from WS1 has been pumped through the mesocosms continuously since they were first packed through the end of (and beyond) this study in autumn of 2018. The mesocosms were designed around 1-m long 20-cm diameter aluminum pipe segments with sample ports located each meter along the flowpath through each mesocosm – thus sampling at the inlet, at 1 m, and at the outlet which represents the full 2-m long flow path. Sampling was conducted on seven dates between Oct 23 2016 and Aug 27 2018. On two of these dates, only background samples were collected. On the remaining 5 dates, sampling was designed around continuous-injection tracer experiments using both a conservative tracer (salt) and a reactive tracer (various dissolved organic substrates). For background sampling events, samples were generally only collected once. The tracer experiments involved 4 discreet sampling times: 1. pre-injection (under background conditions); 2. early plateau; 3. late plateau, and 4. post-injection (and in one injection experiment, a 5th sample at late-post-injection time). For each round of samples, the mesocosm water temperature, pH, EC, and DO were measured with sensors in a small flow-through cell. Then water samples were collected for laboratory analysis for both DOC and DIC. The median travel time of water through each pipe segment of the 2-m mesocosms was also calculated from the conservative tracer break-through curves.
Lake Mendota, Wisconsin, USA, Phytobenthos Abundance and Community Composition 2016-2018
We sampled the phytobenthos (epibenthic periphyton) of Lake Mendota from 2016-2018 to track impacts of invasive zebra mussels (Dreissena polymorpha) which were discovered in Lake Mendota in 2015 and grew exponentially to densities greater than 10,000 m-2 in shallow, rocky habitat by 2018. We sampled along three transects inherited from Karatayev et al. (2013) at five different depths (1, 3, 5, 8, and 10 m) twice a summer (June and August) from 2016-2018. A pared-down version of this routine sampling continued from 2019 onward but is not included here. This dataset complements zebra mussel and zoobenthos data collected according to the same routine sampling structure, for which data is also archived with EDI.
Lake Mendota, Wisconsin, USA, Zebra Mussel Density and Biomass 2016-2018
We sampled adult zebra mussels (Dreissena polymorpha) in the benthos of Lake Mendota from 2016-2018 to track the growth of the population following its initial detection in fall 2015. We sampled along three transects inherited from Karatayev et al. (2013) at five different depths (1, 3, 5, 8, and 10 m) twice a summer (June and August) from 2016-2018. Because suitable zebra mussel substrate was limited at these sites, we also selected five 1 m depth, rocky sites (optimal zebra mussel sites) to track density and biomass where colonization was most intense. A pared-down version of this routine sampling continued from 2019 onward but is not included here. This dataset complements zoobenthos and phytobenthos data collected according to the same routine sampling structure, as well as larval zebra mussel (veliger) sampling for which data is also archived with EDI. Biomass data are modeled from lengths of up to 100 individuals that were measured in each sample. Those lengths were fed into Lake Mendota-specific length-to-weight power law equations parameterized by body size measurements (length, width, live weight, wet weight, dry weight, shell weight, shell-free weight, and ash-free dry weight) of 99 mussels collected at different sites across Lake Mendota in 2018.
Lake Mendota, Wisconsin, USA, (Non-Dreissenid) Benthic Macroinvertebrate Abundance, Biomass, and Community Composition 2016-2018
We sampled the zoobenthos (macroinvertebrates of the benthos) of Lake Mendota from 2016-2018 to track impacts of invasive zebra mussels (Dreissena polymorpha) which were discovered in Lake Mendota in 2015 and grew exponentially to densities greater than 10,000 m-2 in shallow, rocky habitat by 2018. The data presented here exclude all zebra mussels, which are archived in a separate datset. We sampled along three transects inherited from Karatayev et al. (2013) at five different depths (1, 3, 5, 8, and 10 m) twice a summer (June and August) from 2016-2018. These data also contain some samples opportunistically taken from deeper depths along these transects that do not follow the routine sampling structure. A pared-down version of this routine sampling continued from 2019 onward but is not included here. This dataset complements zebra mussel and phytobenthos data collected according to the same routine sampling structure, for which data is also archived with EDI.
Chlorophyll a concentration, particulate organique carbon, and particle mean size index [gamma; 0.2 - 20 µm] measured using an hyperspectral spectrophotometer [ACS, Wetlabs] during the Tara Pacific Expedition 2016-2018
<p>The Tara Pacific expedition (2016-2018) sampled coral ecosystems around 32 islands in the Pacific Ocean, and sampled the surface of oceanic waters at 249 locations, resulting in the collection of nearly 58,000 samples (Gorsky et al. 2019, Planes et al. 2019, Flores et al. 2020). The expedition was designed to systematically study corals, fish, plankton, and seawater, and included the collection of samples for advanced biogeochemical, molecular, and imaging analysis. Here we provide the continuous dataset originating from the hyperspectral and multispectral spectrophotometers [ACS] instruments acquiring continuously during the full course of the campaign. Surface seawater was pumped continuously through a hull inlet located 1.5 m under the waterline using a membrane pump (10 LPM; Shurflo), circulated through a vortex debubbler, a flow meter, and distributed to a number of flow-through instruments. An [ACS] spectrophotometer (WETLabs) measured hyper-spectral (4 nm resolution) attenuation and absorption in the visible and near infrared except between Panama and Tahiti where an AC-9 multispectral spectrophotometer (WETLabs) was used instead. The flow was automatically directed through a 0.2 µm filter for 10 minutes every hour before being circulated through the spectrophotometer to eliminate the impact of biofouling and instrument drift and estimate particulate absorption [ap] and attenuation [cp] (Slade et al. 2010). Chlorophyll a content was estimated from particulate absorption line height at 676 nm (Boss et al. 2001). The particulate organic carbon concentration [poc] was estimated using an empirical relation (Gardner et al. 2006) between measured [poc] and measured [cp]. An indicator for size distribution of particles between 0.2 and ~20 µm [gamma] was calculated from [cp] (Boss et al 2001). The data was processed with custom software for underway optical data (InLineAnalysis software available on GitHub). The detailed information regarding the data processing is given in the processing report attached with the data and in Lombard et al. (In prep.). These results are preliminary: no matchup with in-situ chlorophyll from HPLC or [poc] measurements were performed.</p>
EUENGAGE Dataset 2016-2018
<p>The EUENGAGE Dataset collects data from the EUENGAGE Project, which ran from 2015 to 2018. For more information on the EUENGAGE Project, please visit the dedicated website: www.euengage.eu</p> <p>It includes data from:</p> <ol> <li> <p>The EUENGAGE Mass and Businesspeople panel surveys, which were conducted in ten Member States of the European Union: Czech Republic, France, Germany, Greece, Italy, Netherlands, Poland, Portugal, Spain, and United Kingdom in 2016 and 2017.</p> </li> <li> <p>The EUENGAGE elite surveys, which were conducted in the same ten Member States in 2016 and 2017.</p> </li> <li> <p>The Pre- and Post- Deliberation surveys, which were conducted in October 2016, respectively before and after the online deliberation named “E-Voice”.</p> </li> </ol> <p>The EUENGAGE Dataset also includes Chapel Hill Expert Survey (CHES) data for party positioning. Values for each variable of CHES datasets have been matched to survey respondents’ voting intentions, party identifications (mass and B2B), or party of election (politicians).<br> For instance, for a respondent expressing the intention to vote for party XY (variable QG2_T1) in wave 1 (T1), there will be a set of CHES variables from CHES 2014 with values corresponding to the positioning of party XY on all the issues included in CHES 2014. Likewise, party preferences expressed in wave 2 (T4) will correspond to the values of party positioning measured by CHES 2017.</p> <p>When using CHES data, please cite the following:</p> <p>Polk, Jonathan, Jan Rovny, Ryan Bakker, Erica Edwards, Liesbet Hooghe, Seth Jolly, Jelle Koedam, Filip Kostelka, Gary Marks, Gijs Schumacher, Marco Steenbergen, Milada Vachudova and Marko Zilovic. 2017. "Explaining the salience of anti-elitism and reducing political corruption for political parties in Europe with the 2014 Chapel Hill Expert Survey data," Research & Politics (January-March): 1-9.</p> <p>CHES codebooks are available here: https://www.chesdata.eu/2014-chapel-hill-expert-survey (CHES 2014) https://www.chesdata.eu/1999-2014-chapel-hill-expert-survey-ches-trend-file-1</p>
Water chemistry and aquatic vegetation data from Les Cheneaux Islands, Northern Lake Huron, Michigan, USA, 2016-2018
Remote sensing approaches that could identify species of submerged aquatic vegetation (SAV) and measure their extent in lake littoral zones would greatly enhance their study and management, especially if they can provide faster or more accurate results than traditional field methods. Remote sensing with multispectral sensors can provide this capability, but SAV identification with this technology must address the challenges of light extinction in aquatic environments where chlorophyll, dissolved organic carbon, and suspended minerals can affect water clarity and the strength of the sensed light signal. Here, we present environmental data collected to support a study using an unmanned aerial system (UAS)-enabled methodology to identify the extent of the invasive SAV species Myriophyllum spicatum (Eurasian watermilfoil, or EWM) in the Les Cheneaux Islands area of northwestern Lake Huron, Michigan, USA. Data collected includes water chemistry (nitrogen, phosphorus, carbon, suspended solids, chlorophyll a), light profiles, and submerged aquatic vegetation characteristics including cover, species dominance using aquatic vegetation survey methods (AVAS), and biomass.
Repeated synoptic watershed chemistry from three watersheds near Toolik Field Station, Alaska, summer 2016-2018
Data file describing repeated sampling of chemistry of distributed river water from the Kuparuk River, Oksrukuyik Creek, and Trevor Creek watersheds near Toolik Field Station, North Slope of Alaska. Data file includes sampling date, season, sampling point, subcatchment area, and resulting concentrations for a suite of solutes.
Producción científica UNdeC 2016-2018
<p>Datos primarios del relevamiento realizado, entre marzo/2016 a febrero/2018, de todos los productos científicos obtenidos (con filiación institucional UNdeC) de Redalyc, Scielo, Google Scholar, Scopus, PoP (software Publish or Perish) y la base de datos de la Secretaría de Ciencia y Tecnología (SeCyT) de la UNdeC</p>
Wet season standardised maximum land surface temperature of the Greater Paramaribo Region 2016-2018
<p>This map shows the maximum land surface temperature for the wet season of the Greater Paramaribo Region, Suriname, 2016-2018. The satellite images stem from the Landsat 8 OLI/TIRS (Operational Landsat Imager/Thermal Infrared Sensor) satellite and were obtained from the United States Geological Survey (USGS). A detailed description is provided in the metadata document.</p><p><i>Tom Remijn, Lisa Best, Rudi van Kanten, Nina Schwarz , Louise Willemen, 2020, Wet season standardised maximum land surface temperature of the Greater Paramaribo Region 2016-2018, product of 'Naar een groen en leefbaarder Paramaribo' by Tropenbos Suriname and University of Twente-ITC.</i> DOI: 10.5281/zenodo.7696837,<i> licensed under the </i><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/"><i>Creative Commons License CC BY-NC-SA 4.0</i></a><i>.</i></p>
Eulerian and Lagrangian diagnostics of the dynamical properties of the water masses sampled during the Tara Pacific Expedition 2016-2018
<p>In order to provide a description of the dynamical properties of the water masses sampled, different Eulerian and Lagrangian diagnostics were calculated. </p> <p>For each of the 246 stations sampled, we proceeded as follows.</p> <p>We identified the water mass sampled at the given station. This was considered as a stadium shape with the two semi-circles centered on the starting and ending points of the transect, respectively. The radius of the stadium semi-circles was considered 0.1°, which is in accordance with previous studies25,29,30. The stadium was filled with virtual particles separated by 0.01°.</p> <p>For each virtual particle inside the stadium shape, we calculated an Eulerian or Lagrangian diagnostic (described above). The Eulerian diagnostics were extracted directly from the velocity field of the day of sampling. Concerning the Lagrangian diagnostics, these were obtained by advecting the virtual particle backward in time for an amount of time 𝞽 from the day of sampling day_S. For the Lagrangian betweenness, the advection was performed between day_S+𝞽/2 and day_S-𝞽/2, so that the advective time window was centered on the sampling day (details in25).</p> <p>For the Lagrangian diagnostics, we used the following advective times 𝞽: 5, 10, 15, 20, 30, and 60 days. The only exception is the retention time, which, by construction, was calculated only with the largest advective time, namely 𝞽=60 days.</p> <p>Once that, a given diagnostic (Eulerian or Lagrangian) was calculated for all the virtual particles filling the stadium shape, we calculated the mean value, and the 25, 50, and 75 percentiles. The percentiles were calculated in order to quantify the spatial variation of the diagnostic inside the stadium shape. Therefore, we associated each station with four values (mean, 25, 50, and 75 percentiles) of a given diagnostic.</p> <p> Furthermore, two different velocity fields were used, which are described as follows. </p> <p>Both the velocity fields were downloaded from E.U. Copernicus Marine Environment Monitoring Service (CMEMS, http://marine.copernicus.eu/). The first velocity field used was MULTIOBS_GLO_PHY_REP_015_004 [GlobEkmanDt]. This was produced by combining the altimetry derived geostrophic velocities and modeled Ekman surface currents. It had a spatial resolution of 0.25° and a temporal resolution of one day. The second velocity field was GLOBAL_REANALYSIS_PHY_001_030 [GloryS12]. It was obtained by a NEMO model assimilating altimetry and other observations. It had a spatial resolution of 1/12° and a temporal resolution of 1 day.</p> <p>The following Eulerian diagnostics were calculated:</p> <ul> <li> <p>Absolute velocity ([Uabs], m s-1): sqrt(u2+v2), where u and v are the zonal and meridional components of the horizontal velocity field used (described below)</p> </li> <li> <p>Kinetic energy ([Ekin], m2 .s-2): 0.5*(u2+v2)</p> </li> <li> <p>Divergence ([EulerDiverg], d-1): du/dx + dv/dy</p> </li> <li> <p>Vorticity ([Vorticity], d-1): dv/dx - du/dy</p> </li> <li> <p>Okubo-Weiss ([OW], d-2): s2-vorticity2, where s2 is (du/dx-dv/dy)2 + (dv/dx+du/dy)2. If negative, it indicates that the station sampled was inside an eddy.</p> </li> </ul> <p>The following Lagrangian diagnostics were calculated:</p> <ul> <li> <p>Finite-Time Lyapunov Exponents ([Ftle], d-1): it indicates the rate of horizontal stirring, and it is a means to quantify the intensity of turbulence in a given region. FTLE are commonly used to identify Lagrangian Coherent Structures, i.e. barriers to transport. In this case, a strong FTLE value indicates a region separating water masses which were far away backward in time.</p> </li> <li> <p>Lagrangian betweenness ([betw], adimensional): this diagnostic draws inspiration from Lagrangian Flow Network Theory26. It can identify regions which act as bottlenecks for the circulation, in that they receive waters coming from different origins, and that are then spread over several different destinations. These can represent possible hotspots driving biodiversity25.</p> </li> <li> <p>Lagrangian Divergence ([LagrDiverg], d-1). This diagnostic was calculated by integrating the Eulerian divergence along the backward trajectories. If positive, it indicates a water mass that, during the previous days, was subjected to a strong divergence, thus to a possible upwelling. If negative, it indicates a strong convergence, thus possible downwelling.</p> </li> <li> <p>Retention Time ([RetentionTime], d). This diagnostic indicates how many days a water mass has spent inside an eddy in the previous period. If the water mass is outside an eddy, then its retention time is set to zero.</p> </li> </ul>
Environmental context observed during the Tara Pacific Expedition 2016-2018, simplified version at site level
<p>This dataset provide a site level-compilation of previous datasets provided at the event level (see https://zenodo.org/record/6445609#.YlP8B5NByEA). In some cases, certain parameters were not available at specific sampling sites due to technical issues or sensor availability, however, various basin scale studies and statistical tests require a complete dataset for all sampled sites. During the Tara Pacific expedition, many parameters were concurrently measured in-situ, estimated from remote sensing and/or modeled. For instance, sea surface temperature was measured on the boat using the thermosalinograph included in the underway system, but also with satellite and estimated from a model. Each of these three modes of acquisition have their caveat and accuracy, however, within a certain confidence interval, missing in-situ data can be replaced by its remotely sensed or modeled equivalent. We provide here a simplified version at the sampling site level by replacing missing in-situ data by their closest and most accurate satellite or modeled equivalent. In each case, in-situ data was considered as the most accurate source of data, with a preference to HPLC pigments analysis followed by measurements done by the ACS, while satellite and modeled data were used only if in-situ data was not available. We evaluated the accuracy of ACS and of each satellite and modeled datasets by linear regressions with their in-situ counterparts. A bias of the modeled or satellite data was identified when the slope of the regression was different to 1 and/or an intercept was different to 0. The satellite and modeled data were forced to match the in-situ data by dividing by the slope and subtracting the intercept. This is the case for SST. When large bias persisted between matchups with observations, the corrected data was not used to replace missing in-situ data. This is the case for chl. The same approach was then applied to fill missing data with modeled values (MERCATOR-Copernicus).</p> <p>A correction for the bias in the following variable was applied for SST, SSS, PO4, and SiOH. As previously done, if large bias persisted between observations and corrected data, they were not used to replace missing in-situ data. This is the case for chl, NO3, and Fe.</p> <p>The [MTE] samples were sometimes sampled in the afternoon instead of the morning alongside all the other water samples, thus were located in between two sampling stations. These [MTE] samples could not be assigned to a sampling station following the criterion presented in the section 3, therefore, the missing values of the corresponding morning stations were interpolated linearly.</p> <p>The same approach was used for pH measurements, with a preference from measurements provided by total carbonate system quantifications, followed by direct pH measurements and then modeled values (MERCATOR-Copernicus).</p>
Surveys of coastal foredune topography and vegetation abundance, U.S. North Carolina Outer Banks, 2016-2018
These datasets include paired measurements of beach and foredune topographic profiles and dune vegetation surveys along the North Carolina Outer Banks, collected from October 2016 to June 2018. In October 2016, we conducted topographic and vegetation surveys at 55 transect locations along the southern Outer Banks (Shackleford Banks, South Core Banks, and North Core Banks) that were approximately 2 km apart. We then surveyed an additional 34 transect locations along the northern Outer Banks (Ocracoke Island, Hatteras Island, Pea Island, Bodie Island, and False Cape) that ranged from approximately 2-20 km apart in June 2017. In October 2017 and June 2018, we repeated these surveys at the same transect locations. At each cross-shore transect, we measured elevation and position using an RTK GPS and plant species abundance and dune grass density (within 0.25 m2 quadrats) every 5 m from the foredune toe to the foredune heel. We measured the areal percent cover of all plant species found within each quadrat and counted the live and dead shoot density of four native Atlantic Coast dune building grasses (Uniola paniculata, Ammophila breviligulata, Panicum amarum, and Spartina patens).
Greenhouse gas data of permafrost-affected reservoirs on the Qinghai-Tibet Plateau, 2016-2018
This dataset is a collation of 3-year direct measurement values of CO2, CH4 and N2O concentrations and fluxes of permafrost-affected reservoirs on the Qinghai-Tibet Plateau, along with information on location and chemical conditions of the study sites. Given the rarity and high value of this data set, it will be very valuable for the next update to global reservoir GHG flux estimates.
Aspen Fungal Canker Inventory at Previoulsy Established Monitoring and Inventory Plots Across Interior Alaska, Sampled 2016-2018
This dataset contains characterization of 88 study sites and inventory data for individual trees at these study sites recorded between 2016-2018. Canker was inventoried within previously established networks of long-term monitoring and inventory plots scattered across interior Alaska, and within additional sites selected specifically for this study. These included sites within the Bonanza Creek (BNZ) Experimental Forest (BCEF), and sites within the BNZ Regional Site Network (RSN), both maintained by the BNZ Long-Term Ecological Research program (BNZ LTER). We also surveyed plots within the Cooperative Alaska Forest Inventory (CAFI) network, a network of 203 sites representing a wide range of mature boreal forest stands scattered across interior and south-central Alaska including the Kenai Peninsula.
Drought and herbivory effects on woody plant seedling establishment and grass competition in the Jornada Basin, 2016-2018
This dataset contains observations of seedling establishment and grass competition under precipitation manipulations, and herbivory and granivory exclosure treatments, in the Chihuahuan desert of southern New Mexico, USA. The experiment took place at the Jornada Basin LTER site. We used a rainfall manipulation system and various herbivore exclosures in a factorial design, to test hypotheses about how precipitation (PPT), competition between grasses and shrub seedlings, and predation affect the germination and first-year survival of Mesquite (Prosopis glandulosa), a shrub that has encroached in Southern Great Plains and Chihuahuan Desert grasslands. Data collected in these files include seedling counts in each treatment over observation years 2016 to 2018. This data supports the related publication in Ecological Applications (Weber-Grullon et al. in press). The dataset is complete.
Environmental data at the sampling event level collected with Inline instruments, almanach, models and satellites during the Tara Pacific Expedition 2016-2018
<p>The Tara Pacific expedition (2016-2018) sampled coral ecosystems around 32 islands in the Pacific Ocean, and sampled the surface of oceanic waters at 249 locations, resulting in the collection of nearly 58,000 samples. The expedition was designed to systematically study corals, fish, plankton, and seawater, and included the collection of samples for advanced biogeochemical, molecular, and imaging analysis. Here we provide at the sampling event level, the environmental data originating from all instruments acquiring continuously during the full course of the campaign. This dataset is augmented with the addition of variables originating from almanach (local sun/moon set/rise, local zenith), from operational models obtained from Copernicus Marine Services, but also <strong>f</strong>rom satellite imagery (MODIS-AQUA satellite - Level 3 mapped product, 8 day average, 4km resolution) at <a href="https://oceandata.sci.gsfc.nasa.gov">https://oceandata.sci.gsfc.nasa.gov</a>. The zone corresponding to the station position and date was recovered either by taking a two pixel buffer around the given location (total zone being a 5 by 5 pixels square of 20 km side) and in order to propose an alternative measure in the inevitable case where clouds were present an alternative 12 pixels buffer was taken (total zone being a 25 by 25 pixels square of 100 km side). All data were provided as mean, standard deviation (sd) together with 0.05, 0.25, 0.5, 0.75 and 0.95 quartiles</p>
Camera trap fauna survey in Talissieu (France) 2016-2018
<p>Mammals and birds presence identified from a camera trap survey in a mixed forest and fields environment in the Lavours marsh (France). This dataset gathers observations from April 2016 to November 2018 (with some missing days) from a single camera trap (2 successive models) placed in different locations in Talissieu (Ain, France).</p> <p>See <a href="https://doi.org/10.5281/zenodo.2533381">doi:10.5281/zenodo.2533381</a> for more information.</p> <p>location : 5.7211 45.8625 (WGS84)</p> <p>fields :</p> <pre>id_obs <int> identifier date_heure <dttm> UTC date time of observation (%Y-%m-%dT%H:%M:%SZ) cd_nom_taxref <int> TAXREF taxon identifier see https://inpn.mnhn.fr/programme/referentiel-taxonomique-taxref taxref_cd_ref <int> TAXREF valid taxon identifier taxon <chr> taxon name (binomial) espece <chr> taxon name (vernacular, french) nom_complet <chr> taxon name (binomial with author) nom_complet_html <chr> taxon name (binomial italicized, with author) classe <chr> class ordre <chr> order famille <chr> family rang <chr> rank (ES : species, GN : genus, FM : family, OR : order, CL : class) effectif <int> number of individuals sexe <chr> sex type <chr> V: visual direction <chr> direction of travel (mostly blank) temperature <int> ambiant temperature (°C) duree_estimee_min <int> duration in frame (min) rem_obs <chr> observation note id_session <int> session identifier debut_session <dttm> session date time start UTC (%Y-%m-%dT%H:%M:%SZ) fin_session <dttm> session date time end UTC (%Y-%m-%dT%H:%M:%SZ) duree_session_h <int> session duration (h) rem_session <chr> session note id_localisation <int> camera trap location identifier lieu <chr> camera trap location name x_wgs84 <dbl> camera trap longitude (decimal degrees WGS84) y_wgs84 <dbl> camera trap latitude (decimal degrees WGS84) alti <int> camera trap altitude (m, NGF) azimuth <int> camera trap azimuth (°) habitat <chr> habitat type environnement <chr> local environment type cible <chr> target camera <chr> camera model id_etude <int> study identifier nom_etude <chr> study name date <date> observation date (%Y-%m-%d) heure <dbl> observation hour (%I, UTC)</pre> <p> </p>
Saber Pro 2016-2018 B. Ed. in Philosophy with Institutional Characteristics and Program Composition
<p>Edited data from the Colombian Institute for Assessment of Education (Instituto Colombiano para la Evaluación de la Educación, Icfes) database (ftp://ftp.icfes.gov.co/) for the Saber Pro results of the B.Ed. in Philosophy for the years 2016-2018. Additional data from the institutions and the programs, specially program constituents and curricula.</p>
Bird observations on the tidal flats of the Virginia Coast Reserve, 2016-2018
This dataset contains data on observations of birds on tidal flats along the Virginia portion of the Delmarva Peninsula, along the Atlantic side as part of the dissertation research of Alice Besterman. The first data table (ShorebirdswithZerosV1a.csv) has summary information on bird activities broken down by patch type and relation to tide. Shorebirds were surveyed on 9 intertidal mudflats from late April through end of May 2016. Surveys conducted within 3-5 hours of low tide. Scans last 15 minutes or less. Data table 2 (2018_patchscale_long.csv) contains data from surveys of shorebirds on 36 intertidal mudflats. Surveys conducted in May 2018 at low tide. Tidal flats were visited a single time, and scanned a single time. Scans lasted 15 minutes or less. Only target species were counted and microhabitat-use was recorded. A "wide" version of the data with columns of species/cover is also provided as a .zip file. Data table 3 (ContinSampFinala.csv) contains data collected during focal behavioral observations of shorebirds on intertidal mudflats. Data were collected from 9 sites in 2016 between late April and the end of May during low tide. Observations lasted up to 3 minutes or less if observer lost sight of the bird or bird left the site. Data table 4 (allbirds2a.csv) contains surveys of shorebirds using mudflats in May 2018. Sites were visited a single time during low tide. Tidal flats were scanned a single time, and scans lasted 15 minutes or less. This data table differs from Data table 2 in that all shorebird species of interest were counted (not just target species for behavioral analyses), and microhabitat-use data were not included. Data table 5 (InstantSampFinala.csv) contains data collected during focal behavioral observations of shorebirds on intertidal mudflats. Data were collected from 9 sites in 2016 between late April and the end of May during low tide. Observations lasted up to 3 minutes or less if observer lost sight of the bird or bird left the site.
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.