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Madison community science field campaign to assess abundance and distribution of invasive jumping worms.
Asian pheretimoid earthworms of the genera Amynthas and Metaphire (jumping worms) are leading a new wave of co-invasion into Northeastern and Midwestern states, with potential consequences for native organisms and ecosystem processes. However, little is known about their distribution, abundance, and habitat preferences in urban landscapes – areas which likely influence range expansion via human-driven spread. We led a participatory field campaign to assess jumping worm distribution and abundance in Madison, Wisconsin in September of 2017. By compressing 250 person-hours of sampling effort into a single day, we quantified presence and abundance of three jumping worm species across different land-cover types (forest, grassland, open space, residential lawns and gardens), finding that urban green spaces differed in invasibility. We show that community science can be powerful for researching invasive species while engaging the public in conservation. This approach was particularly effective here, where broad spatial sampling was required within a short temporal window.
HOSENG trial – HOme-based oral SElf-testiNG for absent and refusing individuals during a door-to-door HIV testing campaign: a cluster randomised clinical trial in rural Lesotho
<p>These are pseudo-anonymised data from the HOSENG randomized trial: " HOSENG trial – HOme-based oral SElf-testiNG for absent and refusing individuals during a door-to-door HIV testing campaign: a cluster randomised clinical trial in rural Lesotho". The data dictionary explains the data available in the dataset. Between July 2018 and December 2018, 10516 eligible individuals from 106 consenting villages were enrolled from two districts of Lesotho, and followed up for a maximum of 120 days. Main manuscript reference, DOI: <a href="https://doi.org/10.1016/s2352-3018(20)30233-2">10.1016/S2352-3018(20)30233-2. </a>The protocol was published, DOI:10.1186/s13063-019-3469-2.</p>
Metadata for the urbisphere-Paris campaign during 2022-2024: fieldwork maintenance log [L1]
<p>Machine-readable, formatted and redacted electronic fieldwork logs from the urbisphere-Paris observation campaign conducted between 2022-09-05 and 2024-07-22 in Paris, France. Provided in text format with comma separated columns (.csv) and in Microsoft Excel (.xlsx) format.</p> <p>The fieldwork logs are created from raw google form data submitted by campaign managers, scientists, technicinas and students. The formatting process is detailed in https://github.com/Urban-Meteorology-Reading/urbisphere-paris-fieldwork-log-format. The GitHub output has then been manually edited and adjusted.</p> <p>Contains maintenance information for the following observational sites operated as part of the urbisphere Paris campaign 2022 - 2024:</p> <table> <tbody> <tr> <td>PAARBO</td> <td>Paris – Arboretum de Vallée-aux-Loups </td> </tr> <tr> <td>PAAUNA</td> <td>Paris – Aunay-sous-Auneau</td> </tr> <tr> <td>PABOBI</td> <td>Paris – Bobigny</td> </tr> <tr> <td>PABONN</td> <td>Paris – Bonniel</td> </tr> <tr> <td>PABPAC</td> <td>Paris – Balloon Parc Andre Citroën</td> </tr> <tr> <td>PACHAM</td> <td>Paris – Chamant</td> </tr> <tr> <td>PACHAN</td> <td>Paris – Changis-sur-Marne </td> </tr> <tr> <td>PACHEM</td> <td>Paris – Chemin Vert Bobigny</td> </tr> <tr> <td>PACOMP</td> <td>Paris – Compiègne</td> </tr> <tr> <td>PACOUR</td> <td>Paris – Courdimanche-sur-Essonne</td> </tr> <tr> <td>PACRET</td> <td>Paris – Créteil</td> </tr> <tr> <td>PADENF</td> <td>Paris – Denfert Rocherau </td> </tr> <tr> <td>PADROU</td> <td>Paris – Droue Sur Drouette</td> </tr> <tr> <td>PAHOTE</td> <td>Paris – Hôtel de Ville</td> </tr> <tr> <td>PAJUSS</td> <td>Paris – Jussieu </td> </tr> <tr> <td>PALUPD</td> <td>Paris – Université Paris Diderot (LISA Platform)</td> </tr> <tr> <td>PAMEUD</td> <td>Paris – Meudon</td> </tr> <tr> <td>PANANG</td> <td>Paris – Nangis</td> </tr> <tr> <td>PANATI</td> <td>Paris – Rue Nationale</td> </tr> <tr> <td>PAPRUN</td> <td>Paris – Prunay-le-Temple</td> </tr> <tr> <td>PAROIS</td> <td>Paris – Roissy</td> </tr> <tr> <td>PAROMA</td> <td>Paris – Romainville</td> </tr> <tr> <td>PASIRT</td> <td>Paris – SIRTA Observatory Palaiseau</td> </tr> <tr> <td>PASTFE</td> <td>Paris – Saint Félix</td> </tr> <tr> <td>PAWYDT</td> <td>Paris – Wy-dit-Joli-Village</td> </tr> </tbody> </table> <p> </p>
Hydrochemical Data from a Tropical Andean Glacierized Catchment: δ18O, electrical conductivity, maximum fluorescence intensity, and dissolved organic carbon concentrations from short-term sampling campaigns, Ecuador (2022 and 2024)
Fluorescent dissolved organic matter (FDOM) quality, dissolved organic carbon (DOC) concentration, electrical conductivity (EC), and stable water isotopes (δ¹⁸O and δ2H) were determined in water, snow, and ice samples from a tropical glacierized catchment in the Ecuadorian Andes. The sampling locations were selected to capture the major hydrologic inputs to the main stream channel (glacial melt, tributaries, wetlands, and groundwater springs) and constrain the in-stream spatiotemporal variation in DOM quality and other hydrochemical characteristics. Two sets of high-resolution time series were collected on Oct 13, 2022 and Jun 14, 2024. Time series samples were collected at various upper catchment locations and the outlet simultaneously. DOM quality was characterized via fluorescence spectroscopy and processed using parallel factor analysis (PARAFAC). The DOM quality data are expressed as %FMax values obtained through a 4-component PARAFAC model, where %Fmax 1– 4 are interpreted as terrestrial humic-like, tyrosine-like, tryptophan-like, and microbial humic-like fluorescent components, respectively. DOC concentrations were quantified using high-temperature catalytic combustion, stable water isotopes were analyzed using laser-based spectroscopy, and EC was measured in situ with handheld multiparameter water quality probes.
Numerical weather simulation using COSMOiso in June 2019 during L-WAIVE field campaign: selected model output and post-processed data.
<p>This dataset consists of extracts from a simulation with the isotope-enabled regional numerical weather prediction model COSMOiso, which covers the timespan of the Lacustrine-Water vApor Isotope inVentory Experiment (L-WAIVE) field campaign taking place in June 2019 in the Annecy valley in the French Alps (Chazette et al. 2021).The simulation has a horizontal resolution of 0.1° (~10km) and 40 vertical levels.</p><p>This COSMOiso simulation is used in Thurnherr et al. (submitted) to compare stable water isotope measurements from various platforms. Here, we provide selected model outputs and post-processed data used in this comparison study. The post-processed data contain:</p><ol><li>COSMOiso output files for time steps 20190612_12, 20190613_12, 20190615_13, 20190616_13, 20190617_12, 20190622_12.</li><li>Pressure weighted total and subcolumn averages for time steps 20190612_12, 20190613_12, 20190615_13, 20190616_13, 20190617_12, 20190622_12.</li><li>Vertical cross section of selected variables at Annecy, the location of the L-WAIVE field campaign, for the simulation time window.</li><li>Interpolated time series of subcolumn and total column averages at Annecy, the location of the L-WAIVE field campaign, for the simulation time window.</li><li>Interpolated variables along the flight tracks from the L-WAIVE campaign (see Sodemann and Seidl, 2023).</li></ol><p>See also README files for more details on the provided data.</p><p>To access further model output and post-processed data, please contact the dataset authors.</p>
Snow depth, snow water equivalent, ice thickness in Fuglebekken and Revdalen catchments collected in the SnowPilot campaign in Spring 2022
<p>File SnowPilot_snowdepth_along_the_GPR_profile_2022 contains snow depth measurements taken along the GPR profile performed during the SIOS SnowPilot campaign in Spring 2022. File SnowPilot_snowdepth_swe_2022 contains depth, snow water equivalent and basal ice thickness. Snowpits were dug on GPR profile crossings in the Fuglebekken and Revdalen catchments in the Hornsund fiord, Spitsbergen catchment. Snow density was measured with an IG PAS snow tube, and snow depth and basal ice (ice forming on the ground surface) thickness were measured with an avalanche probe. Point locations measured. with handheld GPR reciever.</p>
Wild for Orchids Citizen Science Campaign Records 2020 - 2023
<p>This dataset represents the geographic distribution of wild orchids in the Maltese Islands, as recorded by the Wild For Orchids Citizen Science Initiative between January 2020 and December 2023. It includes data obtained through citizen science contributions and has undergone rigorous two-stage quality control for species identification and GPS accuracy. Species identification was carried out according to Mifsud (2018). The location data of each records is provided as a shapefile format projected in ETRS89-extended / LAEA Europe (EPSG:3035), and exact GPS location were transformed in 1 km square grid according to the <span>European Forum for Geography and Statistics (EFGS).<br></span></p> <p><span>Wild for Orchids is a Citizen Science Initiative designed and managed by Green House Malta.</span></p>
VHR orthomosaic imageries produced by dedicated drone flight campaigns over Lithuania (2021-2022) - Vilnius -AOI 1, Sub-region 1
<p>In the context of the EU-funded project DIONE (No. 870378), targeted drone flight campaigns were conducted in determined Areas of Interest (AOIs) in the Lithuanian territory as a complementary source of information to the spaceborne Earth Observation (EO) open-accessed data. Such an activity attempted to overcome the limited capabilities of EO coarse resolution data to identify small-scale features alongside the agricultural fields, such as the non-productive Ecological Focus Areas (EFAs) and overall to enhance the quality and accuracy (1m or less) of the estimated Land Cover/Land Use elements.</p> <p>The dataset is in fact a collection of drone orthomosaic images delivered in GeoTIFF data format, which were acquired from the drone flight missions over the first subregion of the first AOI located near the capital of Lithuania, (Vilnius). For each area, four flight campaigns were scheduled and conducted on the following dates: </p> <ul> <li><strong>1st flight:</strong> 24-27/5/2021</li> <li><strong>2nd flight:</strong> 26-29/7/2021</li> <li><strong>3rd flight:</strong> 13-16/9/2021</li> <li><strong>4th flight:</strong> 25-28/8/2022</li> </ul>
VHR orthomosaic imageries produced by dedicated drone flight campaigns over Lithuania (2021-2022) - Vilnius -AOI 2
<p>In the context of the EU-funded project DIONE (No. 870378), targeted drone flight campaigns were conducted in determined Areas of Interest (AOIs) in the Lithuanian territory as a complementary source of information to the spaceborne Earth Observation (EO) open-accessed data. Such an activity attempted to overcome the limited capabilities of EO coarse resolution data to identify small-scale features alongside the agricultural fields, such as the non-productive Ecological Focus Areas (EFAs) and overall to enhance the quality and accuracy (1m or less) of the estimated Land Cover/Land Use elements.</p> <p>The dataset is in fact a collection of drone orthomosaic images delivered in GeoTIFF data format, which were acquired from the drone flight missions over the second AOI located near the capital of Lithuania, (Vilnius). For each area, four flight campaigns were scheduled and conducted on the following dates: </p> <ul> <li><strong>1st flight:</strong> 24-27/5/2021</li> <li><strong>2nd flight:</strong> 26-29/7/2021</li> <li><strong>3rd flight:</strong> 13-16/9/2021</li> <li><strong>4th flight:</strong> 25-28/8/2022</li> </ul>
VHR orthomosaic imageries produced by dedicated drone flight campaigns over Cyprus (2021-2022) - Choirokoitia region
<p>In the context of the EU-funded project DIONE (No. 870378), targeted drone flight campaigns were conducted in determined Areas of Interest (AOIs) in the Cypriotic territory as a complementary source of information to the spaceborne Earth Observation (EO) open-accessed data. Such an activity attempted to overcome the limited capabilities of EO coarse resolution data to identify small-scale features alongside the agricultural fields, such as the non-productive Ecological Focus Areas (EFAs) and overall to enhance the quality and accuracy (1m or less) of the estimated Land Cover/Land Use elements.</p> <p>The dataset is in fact a collection of drone orthomosaic images delivered in GeoTIFF data format, which were acquired from the drone flight missions over one of the two determined locations in Cyprus (e.g. Choirokoitia). For each area, four flight campaigns were scheduled and conducted on the following dates: </p> <ul> <li><strong>1st flight:</strong> 29/3/2021- 1/4/2021</li> <li><strong>2nd flight:</strong> 30/8/2021-2/9/2021</li> <li><strong>3rd flight:</strong> 29/11/2021-03/12/2021</li> <li><strong>4th flight:</strong> 17-20/5/2022</li> </ul>
VHR orthomosaic imageries produced by dedicated drone flight campaigns over Cyprus (2021-2022) - Akaki region
<p>In the context of the EU-funded project DIONE (No. 870378), targeted drone flight campaigns were conducted in determined Areas of Interest (AOIs) in the Cypriotic territory as a complementary source of information to the spaceborne Earth Observation (EO) open-accessed data. Such an activity attempted to overcome the limited capabilities of EO coarse resolution data to identify small-scale features alongside the agricultural fields, such as the non-productive Ecological Focus Areas (EFAs) and overall to enhance the quality and accuracy (1m or less) of the estimated Land Cover/Land Use elements.</p> <p>The dataset is in fact a collection of drone orthomosaic images delivered in GeoTIFF data format, which were acquired from the drone flight missions over one of the two determined locations in Cyprus (e.g. Akaki). For each area, four flight campaigns were scheduled and conducted on the following dates: </p> <ul> <li><strong>1st flight:</strong> 29/3/2021- 1/4/2021</li> <li><strong>2nd flight:</strong> 30/8/2021-2/9/2021</li> <li><strong>3rd flight:</strong> 29/11/2021-03/12/2021</li> <li><strong>4th flight:</strong> 17-20/5/2022</li> </ul>
VHR orthomosaic imageries produced by dedicated drone flight campaigns over Lithuania (2021-2022) - Vilnius -AOI 1, Sub-region 2
<p>In the context of the EU-funded project DIONE (No. 870378), targeted drone flight campaigns were conducted in determined Areas of Interest (AOIs) in the Lithuanian territory as a complementary source of information to the spaceborne Earth Observation (EO) open-accessed data. Such an activity attempted to overcome the limited capabilities of EO coarse resolution data to identify small-scale features alongside the agricultural fields, such as the non-productive Ecological Focus Areas (EFAs) and overall to enhance the quality and accuracy (1m or less) of the estimated Land Cover/Land Use elements.</p> <p>The dataset is in fact a collection of drone orthomosaic images delivered in GeoTIFF data format, which were acquired from the drone flight missions over the second subregion of the first AOI located near the capital of Lithuania, (Vilnius). For each area, four flight campaigns were scheduled and conducted on the following dates: </p> <ul> <li><strong>1st flight:</strong> 24-27/5/2021</li> <li><strong>2nd flight:</strong> 26-29/7/2021</li> <li><strong>3rd flight:</strong> 13-16/9/2021</li> <li><strong>4th flight:</strong> 25-28/8/2022</li> </ul>
Drone-based photogrammetric survey raw data from ESA PANGAEA-X 2017 planetary analogue campaign - Data collected on 2017-11-19
<p>Drone-based photogrammetric survey data from ESA PANGAEA-X 2017 planetary analogue campaign. Data were collected in the framework of the ESA PANGAEA-X testing campaign held in November 2017: We acknowledge ESA for organising the campaign and providing scientific and logistic assistance on site. The authors would like also to thank the Geopark of Lanzarote, the touristic center of Cueva de Los Verdes, the Cabildo of Lanzarote, the National Park of Timanfaya and the IGEO-CSIC-UCM for providing the necessary permits. Data collected on 2017-11-19 during an aerial survey with a DJI Phantom 4 - data from AGPA experiments (AGPA-D) see http://www.agpa-project.eu</p>
275 Candidates and 149 Validated Planets Orbiting Bright Stars in K2 Campaigns 0-10
<p>This dataset contains transit model posterior distributions and validation analyses for the 275 exoplanet candidates (in 233 systems) analyzed in Mayo et al. (2018), titled "275 Candidates and 149 Validated Planets Orbiting Bright Stars in K2 Campaigns 0-10".</p> <p>The dataset takes the form of 233 compressed directories each corresponding to an exoplanet system and titled after its EPIC ID. Within a given directory there are two numpy pickles named EPICXXXXXXXXX_chains.npy and EPICXXXXXXXXX_lnlikes.npy (where XXXXXXXXX is the 9 digit EPIC number) as well as n subdirectories, where n is the number of planet candidates in the system.</p> <p>The EPICXXXXXXXXX_chains.npy pickle is a representative sample of the posterior distribution of the transit model for a given exoplanet system. The pickle is a numpy array of size (j,k,l), where j is the number of walkers in the Markov chain Monte Carlo ensemble simulation that sampled the posterior distribution (note: we chose to fix j = 2*l), k is the number of walker steps reported in this dataset (the full posteriors were thinned down to between 750 and 10,000 steps), and l is the number of parameters in the transit model for the exoplanet system. The EPICXXXXXXXXX_lnlikes.npy pickle contains the associated ln(likelihood) values for each walker step in the previously described pickle. This pickle is a numpy array of size (j,k) where j and k are defined as above.</p> <p>The number of parameters will always be of the form 4 + 5*n, where n is again the number of planets in the systems. The first four parameters in the pickle are a baseline offset parameter for the normalized flux, a noise parameter to take the place of flux error bars, and two quadratic limb darkening parameters q<sub>1</sub> and q<sub>2</sub> based on Kipping et al. (2013). The next five parameters (and each subsequent set of five parameters in multi-candidate systems) refer to the reference epoch (a mid-transit time in BJD - 2454833), the period (in days), log<sub>10</sub>(R<sub>p</sub>/R<sub>*</sub>), the transit duration (T<sub>IV</sub>-T<sub>I</sub> in days), and the impact parameter. It should be noted that there is no consistent ordering of the planets in the posterior samples (for example, in a three planet system parameters 5-9 may refer to planet b, planet c, or planet d). Therefore, planetary periods should be used as reference to identify candidates. All parameters and the nature of the transit model are described in detail in Mayo et al. (2018).</p> <p>Each subdirectory contains the input and output of the validation analysis conducted via the VESPA validation package (Morton 2012, 2015). For additional details please refer to the relevant citations or the <a href="https://github.com/timothydmorton/VESPA">VESPA github repository</a>. Each subdirectory is named after the appropriate candidate listed in Mayo et al. (2018; specifically Tables 5 and 7).</p>
A multi-resolution, multi-epoch low Radio Frequency Survey of the Kepler K2 Mission Campaign 1 Field
<p>Data abstract:</p> <p>Contained within are the MWA images used as input data for this study. The production and analysis of these images are described in the linked paper. The final catalogues and light curves are available from VizieR (http://vizier.cfa.harvard.edu/viz-bin/VizieR?-source=J/AJ/152/82).</p> <p>Paper abstract:</p> <p>We present the first dedicated radio continuum survey of a Kepler K2 mission field, Field 1, covering the North Galactic Cap. The survey is wide field, contemporaneous, multi-epoch, and multi-resolution in nature and was conducted at low radio frequencies between 140 and 200 MHz. The multi-epoch and ultra wide field (but relatively low resolution) part of the survey was provided by 15 nights of observation using the Murchison Widefield Array (MWA) over a period of approximately a month, contemporaneous with K2 observations of the field. The multi-resolution aspect of the survey was provided by the low resolution (4‧) MWA imaging, complemented by non-contemporaneous but much higher resolution (20″) observations using the Giant Metrewave Radio Telescope (GMRT). The survey is, therefore, sensitive to the details of radio structures across a wide range of angular scales. Consistent with other recent low radio frequency surveys, no significant radio transients or variables were detected in the survey. The resulting source catalogs consist of 1085 and 1468 detections in the two MWA observation bands (centered at 154 and 185 MHz, respectively) and 7445 detections in the GMRT observation band (centered at 148 MHz), over 314 square degrees. The survey is presented as a significant resource for multi-wavelength investigations of the more than 21,000 target objects in the K2 field. We briefly examine our survey data against K2 target lists for dwarf star types (stellar types M and L) that have been known to produce radio flares.</p>
Campaign content analysis on X for the Castilla y León and Andalusia 2022 regional elections
<p>This dataset refers to the replication data of the article titled: "Could you teach new tricks to old dogs? An analysis of online communication of political leaders in Spanish regional elections", to be published in Frontiers in Political Sciece, after reviewing process.</p>
#Élysée2017fr: The 2017 French Presidential Campaign on Twitter
<p># README</p> <p>This archive contains the #Élysée2017fr dataset.</p> <p>(Initially published at https://web.archive.org/web/20200530171644if_/https://dataverse.mpi-sws.org/dataverse/icwsm18 on June 24, 2018. This dataverse being defunct now, we repost on Zenodo)</p> <p><br> ## Content</p> <p>### keywords.csv<br> The keywords used to collect the initial dataset, each presented with the start and stop dates of use (date format: YYYY-MM-DD).</p> <p><br> ### profiles_annotations.csv<br> The manual profiles annotations. The file contains the following columns:</p> <p>#### FROM_USER_ID<br> The profile's id used by Twitter</p> <p>#### PROFILE_NATURE<br> "**individual**" if the profile is managed by a single person, else "**non individual**".<br> The "**non individual**" label is itself divided in 3 subcategories:<br> - "**political**" for profiles of political parties or associations, and profiles representing groupes of militants.<br> - "**media**" for profiles of media outlets.<br> - "**other**" for profiles not included in the previous categories.</p> <p>#### PARTY<br> The profile's political affiliation(s), indicated as the shortcut for the political party:<br> - "**fi**": France Insoumise (far-left)<br> - "**ps**": Parti Socialiste (left)<br> - "**em**": En Marche ! (center)<br> - "**lr**": Les Républicains (right)<br> - "**fn**": Front National (far-right)<br> - **null**: no political affiliation</p> <p>When a profile has 2 affiliations, they are separated by a slash (ex: "ps/fi"). </p> <p>#### MEDIA_PROFESSIONAL<br> *For individual profiles only.*<br> Indicates if the profile's owner self-identify as a media professional (journalist, editorialist, ...)</p> <p>#### SEX<br> *For individual profiles only.*<br> Indicates the sex of the profile's owner:<br> - "**m**": male<br> - "**f**": female<br> - **null**: undetermined or other</p> <p><br> ### posts_ids_*<br> Files containing the tweets and retweets ids, divided according to the political affiliation of their authors for more flexibility.<br> - **posts_ids_fi.csv**: Tweet ids for profiles affiliated to France Insoumise (far-left)<br> - **posts_ids_ps.csv**: Tweet ids for profiles affiliated to Parti Socialiste (left)<br> - **posts_ids_em.csv**: Tweet ids for profiles affiliated to En Marche ! (center)<br> - **posts_ids_lr.csv**: Tweet ids for profiles affiliated to Les Républicains (right)<br> - **posts_ids_fn.csv**: Tweet ids for profiles affiliated to Front National (far-right)<br> - **posts_ids_multi_affiliations.csv**: Tweet ids for profiles affiliated to more than one party<br> - **posts_ids_indetermined.csv**: Tweet ids for profiles not affiliated to any party</p> <p>Each file contains one tweet id per line.</p> <p><br> ### networks_*<br> Files containing the mention and retweet networks, in NCOL and GraphML format.</p> <p>The NCOL files contains the directed weighted edges between profiles, one per line, in the following format:<br> profile1_twitter_id profile2_twitter_id edge_weight</p> <p>The GraphML files contains the directed weighted edges between profiles, as well as all the profiles annotations presented in *profiles_annotations.csv*. They can be opened using a graph visualisation software like Gephi.</p> <p> </p> <p>## How to get tweets from ids<br> You can use various tools to help you get tweets from their ids, we suggest the following:<br> - DMI-TCAT: https://github.com/digitalmethodsinitiative/dmi-tcat<br> - Twarc: https://github.com/DocNow/twarc</p> <p> </p> <p>## How to cite this work<br> Fraisier Ophélie, Cabanac Guillaume, Pitarch Yoann, Besançon Romaric, Boughanem Mohand. 2018. #Élysée2017fr: the French Presidential Election on Twitter. In International Conference on Weblogs and Social Media. https://aaai.org/ocs/index.php/ICWSM/ICWSM18/paper/view/17821 (https://hal.archives-ouvertes.fr/hal-02319715)</p>
MCR LTER: Coral Reef: Turbinaria CHN from a spatially explicit sampling campaign in lagoons of Moorea, French Polynesia
Nutrients are important for ecosystem structure and community dynamics. To quantify time-integrated patterns of nutrient regimes in tropical lagoon ecosystems, concentrations of nitrogen, hydrogen, and carbon were measured from tissues of the macroalga Turbinaria ornata collected in lagoons around Moorea, French Polynesia in 2016, 2017, and annually starting in 2019. These sampling periods initially corresponded with distinct seasonal shifts in rainfall and wave forcing and later focused on the rainy season. Results showed that N enrichment was highest nearshore in fringing reef habitats, as well as in bays and at reef passes.
Surface temperature mapped from thermal infrared survey from UAV campaign at Niwot Ridge, 2017.
Data collected as part of unmanned aerial vehicle (UAV)/drone campaign during Summer 2017. Investigating snow depth variability and spatiotemporal variations and controls on vegetation productivity within the Niwot Ridge LTER Saddle Catchment. Surface temperature of Niwot Ridge saddle was mapped from thermal infrared survey on June 21, July 11, 18, 25, and August 14, 2017.
Uncalibrated RGB orthomosaic imagery from UAV campaign at Niwot Ridge, 2017.
Uncalibrated RGB data were collected as part of unmanned aerial vehicle (UAV)/drone campaign during Summer 2017. The purpose of the project was to investigate snow depth variability and spatiotemporal variations and controls on vegetation productivity within the Niwot Ridge LTER Saddle Catchment.
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.