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Cumulated dam impact in France and the Iberian Peninsula (SUDOANG project)
<h2><strong>1. SUDOANG PROJECT</strong></h2><p>The SUDOANG project has provided common tools and assessment methods to managers to support the eel conservation in the SUDOE zone (Southern France, Spain and Portugal). One of the goals of the project was to develop an eel abundance and distribution <a href="https://zenodo.org/record/7546419">atlas</a> in the three countries, based on the results of the implementation of Eel Density Analysis (<a href="https://sudoang.eu/wp-content/uploads/2022/02/E411_Briand_et_al_2022_EDA_report_opt-1.pdf">EDA</a>). This model extrapolates eel abundance from a range of river segments sampled by electrofishing, to the whole river and lake network, by considering how eel abundance, size and sex vary according to different parameters related to eel habitat. To do this, we have created a dataset of "cumulated dam impact" which compiles different ways of calculating cumulated height from the sea.</p><h2><strong>2. SUDOANG DATABASE</strong></h2><p>The dataset on cumulated impact was first derived from information on obstacles collected by the SUDOANG project. Obstacles data for the three countries were imported in the SUDOANG database (<a href="https://sudoang.eu/wp-content/uploads/2020/11/E221_data_collection_storage-1.html#3_data_import_on_physical_obstacles_in_spain_and_portugal">deliverable 2.2.1</a>), whose structure is inherited from the DataBase for EEl (DBEEL), developed during a European research project (POSE - Pilot projects to estimate potential and actual escapement of silver eel, Walker et al., 2011). This database is designed to contain all data relative to eel biology and anthropogenic pressures applying to eel. During the course of SUDOANG, this database was used and ameliorated. </p><p>In France the obstacles were compiled from three pre-existing different databases:</p><ul><li>the Referential of flow obstacles (<a href="https://professionnels.ofb.fr/fr/node/367">ROE</a>) ,</li><li>the Information of Ecological Continuity (<a href="https://professionnels.ofb.fr/en/node/731">ICE</a>) and</li><li>the Flow Obstruction Database (Base de Données des Obstacles à l'Ecoulement, (BDOE).</li></ul><p>The data we have integrated into the SUDOANG 1.0.4. database came from a database dump of the 12th September 2019. The inventory includes bridges that have a significant impact on river continuity.</p><p>In Spain, data came from:</p><ul><li>the MITECO Ministry</li><li>the Basque Water Agency (URA) - Basque Country</li><li>the Catalan Water Agency (ACA) - Catalonia</li><li>the University of Girona - Catalonia</li><li>the University of Córdoba - Andalusia</li><li>Xunta de Galicia, Consellería de Medio Ambiente, Territorio e Vivenda - Galicia</li><li>the <a href="https://amber.international">AMBER </a>project</li></ul><p>In Portugal the data came from:</p><ul><li>the Portuguese Water Agency (APA)</li><li>MARE-ULisboa (University of Lisbon)</li><li>CIIMAR, the University of Porto</li><li>the <a href="https://amber.international">AMBER</a> project.</li></ul><p>In the case of the transboundary river Minho, the data came from:</p><ul><li>CIIMAR, the University of Porto (Portuguese area) (<a href="https://www.dgrm.mm.gov.pt/documents/20143/0/PGE+TIRM+Vers%C3%A3o+Portuguesa+Revis%C3%A3o+Novembro+2011.pdf/3c9d8b50-e5cc-2ed8-5714-90a115d4a6a5">report</a>)</li><li>EHEC, the University of Santiago de Compostela (Spanish area) (<a href="https://www.dgrm.mm.gov.pt/documents/20143/0/PGE+TIRM+Vers%C3%A3o+Portuguesa+Revis%C3%A3o+Novembro+2011.pdf/3c9d8b50-e5cc-2ed8-5714-90a115d4a6a5">report</a>)</li></ul><h2><strong>3. DATA DESCRIPTION</strong></h2><h3><strong>3.1. Data collected on artificial obstacles</strong></h3><p>Artificial obstacles were classified into 10 types according to the Adaptive Management of Barriers in European Rivers (<a href="https://amber.international">AMBER</a>) project. Some additional types (e.g., penstock pipes) were added to identify other obstacles in national databases that did not fit the AMBER classification (see the list below). Sometimes dams from different branches are connected, creating a dam-network. In those cases, we have only kept the dam(s) in the main course and use a hierarchical classification of the dams to only consider the cumulated height from the sea to the reference dam. We included only obstacles that are presently standing, <i>i.e.,</i> not planned, under construction, or destroyed. Dikes, longitudinal control structures and grates were excluded.</p><p><i>Obstacle classification according to the data collected and the AMBER project:</i></p><ul><li>BR - Bridge: A structure that is built over a river to allow people or vehicles to cross</li><li>CU - Culvert: A tunnel or pipe carrying a stream or open drain under a road or railway</li><li>DI - Dike: An embankment used to hold back water</li><li>DA - Dam: Structure that blocks the river and extends across the river bed to the flood plain</li><li>FO - Ford: A shallow crossing-place in a river</li><li>PP - Penstock: pipe Group of pipes that transport pressurised water from a reservoir (dam) to the turbines installed in a hydro-electric power plant</li><li>RR - Rock ramp: A weir made of rocks</li><li>WE - Weir: Structure across a river that does not extend to the flood plain</li><li>OT - Other: Structure that is not covered by previous definitions</li><li>UN - Unknown: Unknown</li></ul><p>We have projected obstacles on the SUDOANG river network at the nearest point within 300 m. To avoid projecting large obstacles in the wrong location in the southwestern France, SUDOANG experts have reviewed and corrected this information. We have also used an algorithm that extracts the best obstacle height data from the three existing databases in France. In the Iberian Peninsula, data providers validated and corrected obstacle location and height using a Shiny application developed by the project, in which they could directly correct the height of obstacles.</p><p>The variables in the <strong>obstacles </strong>table (csv delimiter ",") are:</p><ul><li><i>op_id</i>: Identifier of the observation place name</li><li><i>op_gis_layername</i>: Original data source</li><li><i>op_placename</i>: Name of the dam</li><li><i>op_op_id</i>: If the dam is linked within a complex (e.g. when there are multiple channels for the same river) the name of the parent dam</li><li><i>id_original</i>: Original id of the dam (in the raw table)</li><li><i>country</i>: Country code ('SP', 'ES' or 'FR')</li><li><i>dp_name</i>: Name of the data provider</li><li><i>obstruction_type_code</i>: Type of obstruction (see table obstruction type code)</li><li><i>obstruction_type_name</i>: Name of the dam</li><li><i>po_obstruction_height</i>: Difference of level of water between the downstream and the upstream part of the dam</li><li><i>po_presence_eel_pass</i>: Presence of a pass suitable for eel (see paper)</li><li><i>po_date_presence_eel_pass</i>: Date of construction of the eel (or eel compatible) pass</li><li><i>fishway_type_code</i>: Code of the fishway type</li><li><i>fishway_type_name</i>: Name of the fishway type</li><li><i>googlemapscoods</i>: Link to google map</li><li><i>x_espg_4326</i>: Longitude (with ESPG 4326)</li><li><i>y_espg_4326</i>: Latitude (with ESPG 4326)</li></ul><h3><strong>3.2. Modeling missing data and estimating the cumulative impact on obstacles</strong></h3><p>For those obstacles missing height information, we have calculated height using a Generalized Linear Models (GLM of log transformed height, <i>family = gaussian, link = identity</i>. In France the <a href="https://forgemia.inra.fr/pole-migrateurs/eda/dbeel/-/blob/main/eda2.3/report/Dams.Rmd">model</a> was based on river segment slope, river segment median flow and hydrographic basin. In the Iberian Peninsula, we have implemented a simpler <a href="https://forgemia.inra.fr/pole-migrateurs/eda/dbeel/-/blob/main/eda2.3/report/E221_data_collection_storage_sp_pt.Rmd">model</a> based on obstacle type, as information about flow or slope was not available for all river segments.</p><p>The cumulated impact of obstacles was assessed by creating a table joining each river segment with all the dams located in the downstream course. Using this, various metrics were computed using different assumptions concerning the effect of obstacles. The heights were power transformed to test for a different effect of obstacle's height (the cumulated effect of two obstacles of 1 m might be different than the cumulated effect of a single obstacle of 2 m), and functions were developed to calculate cumulated obstacle transformed variables. Other variables were also tested. In fact, tests in France have shown that factors such as presence of a fish pass, and eel passability did not improve the <a href="https://forgemia.inra.fr/pole-migrateurs/eda/eda_model/-/blob/main/S4/BaseEdaRiosRiversegmentsDam.R\#L624">model performance</a>. For this reason, but also because in the Iberian Peninsula this type of information was too limited, we used dam height to model the cumulative height of obstacles at a given river segment. </p><p>The variables in the <strong>cumulated_dam_impact_SUDOANG </strong>table (format Rdata - to be read with the R software, this will load as a data.frame called datadam) are:</p><ul><li><i>cs_height_08_n</i>: Cumulated height from the sea, dam height transformed with power 0.8, no prediction for missing values</li><li><i>cs_height_08_n</i>.: Same variable but truncated to 300</li><li><i>cs_height_08_p</i>: Cumulated height from the sea, dam height transformed with power 0.8, with prediction for missing values</li><li><i>cs_height_08_p</i>.: Same variable but truncated to 300</li><li><i>cs_height_08_pps </i>Cumulated height from the sea, dam height transformed with power 0.8, with prediction for missing values, the height of dam is set to zero if equiped with an efficient fishway for eel</li><li><i>cs_height_10_FR</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams from France are considered when building on a transnational water course</li><li><i>cs_height_10_n</i>: Cumulated height from the sea, no transformation, no prediction for missing values</li><li><i>cs_height_10_n</i>.: Same variable but truncated to 200</li><li><i>cs_height_10_p</i>: Cumulated height from the sea, no transformation, missing height are extrapolated from two different models in France and the Iberian Peninsula <i>cs_height_10_p</i>.: Same variable but truncated to 200</li><li><i>cs_height_10_pass0</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams without pass are used to build the cumulated value</li><li><i>cs_height_10_pass1</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams with pass are used to build the cumulated value</li><li><i>cs_height_10_pp</i>: Cumulated height from the sea, no transformation, with prediction for missing values, the height of dam is set to zero if equiped with an efficient fishway for eel</li><li><i>cs_height_10_ppass0</i>: Cumulated height from the sea, no transformation, including prediction for missing values, only the dams without pass are used to build the cumulated value</li><li><i>cs_height_10_ppass1</i>: Cumulated height from the sea, no transformation, including prediction for missing values, only the dams with pass are used to build the cumulated value</li><li><i>cs_height_10_pps</i>: Cumulated height from the sea, no transformation, with prediction for missing values, the height of dam is set to zero if a score of efficient passage was attributed for eel on this structure</li><li><i>cs_height_10_pscore0</i>: Cumulated height from the sea, no transformation, including prediction for missing values, only the dams without score are used to build the cumulated value</li><li><i>cs_height_10_pscore1</i>: Cumulated height from the sea, no transformation, including prediction for missing values, only the dams with score (that have been expertised as no or small barrier for eel) are used to build the cumulated value</li><li><i>cs_height_10_PT</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams from Portugal are considered when building on a transnational water course</li><li><i>cs_height_10_score0</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams without score are used to build the cumulated value</li><li><i>cs_height_10_score1</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams with score (that have been expertised as no or small barrier for eel) are used to build the cumulated value</li><li><i>cs_height_10_SP</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams from Spain are considered when building on a transnational water course</li><li><i>cs_height_12_n</i>: Cumulated height from the sea, dam height transformed with power 1.2, no prediction for missing values</li><li><i>cs_height_12_n</i>: Same variable but truncated to 500</li><li><i>cs_height_12_p</i>: Cumulated height from the sea, dam height transformed with power 1.2, with prediction for missing values</li><li><i>cs_height_12_p.</i>: Same variable but truncated to 500</li><li><i>cs_height_12_pp</i>: Cumulated height from the sea, dam height transformed with power 1.2, with prediction for missing values, the height of dam is set to zero if equiped with an efficient fishway for eel</li><li><i>cs_height_12_pps</i>: Cumulated height from the sea, dam height transformed with power 1.2, with prediction for missing values, the height of dam is set to zero if a score of efficient passage was attributed for eel on this structure</li><li><i>cs_height_15_n</i>: Cumulated height from the sea, dam height transformed with power 1.5, no prediction for missing values</li><li><i>cs_height_15_n:</i> Same variable but truncated to 800</li><li><i>cs_height_15_p</i>: Cumulated height from the sea, dam height transformed with power 1.5, with prediction for missing values</li><li><i>cs_height_15_p.:</i> Same variable but truncated to 800</li><li><i>cs_height_15_pp</i>: Cumulated height from the sea, dam height transformed with power 1.5, with prediction for missing values, the height of dam is set to zero if equiped with an efficient fishway for eel</li><li><i>cs_height_15_pps</i>: Cumulated height from the sea, dam height transformed with power 1.5, with prediction for missing values, the height of dam is set to zero if a score of efficient passage was attributed for eel on this structure </li><li><i>cumnbdamp</i>: Cumulated number of dam from the sea</li><li><i>cumnbdamso</i>: duplicate of cumnbdamp</li><li><i>idsegment</i>: Unique identifier of the segment [data type: UUID]. Use the <a href="https://doi.org/10.5281/zenodo.7546419">Atlas</a> to link with spatial table in PostgreSQL</li></ul><h2><strong>4. VERSIONS</strong></h2><ul><li><a href="https://doi.org/10.5281/zenodo.7825552">10.5281/zenodo.7825552 </a>1.0.0 - 2023-04-15 - Initial Upload (closed access)</li><li><a href="https://doi.org/10.5281/zenodo.8348374">10.5281/zenodo.8348374</a> 1.0.1 - 2023-09-15 - Update provider and names (closed access)</li><li><a href="https://doi.org/10.5281/zenodo.8348374">10.5281/zenodo.8348374</a> 1.0.1 - 2023-11-08 - Final version (open access)</li></ul><h2><strong>5. READ MORE</strong></h2><ul><li>Atlas of European Eel Distribution (<i>Anguilla anguilla</i>) in Portugal, Spain and France (<a href="https://doi.org/10.5281/zenodo.7546419">10.5281/zenodo.7546419</a>)</li><li>Electrofishing data for eel in the Iberian Peninsula (SUDOANG project) (<a href="https://doi.org/10.5281/zenodo.8348353">10.5281/zenodo.8348353</a>)</li><li>Eel data (<i>Anguilla anguilla</i>) and associated environment variables used to fit the EDA model in the SUDOE area (SUDOANG project) (<a href="https://doi.org/10.5281/zenodo.6397009">10.5281/zenodo.6397009</a>)</li></ul><h2><strong>6. FUNDING</strong></h2><p>Project co-financed by the INTERREG SUDOE Programme through the European Regional Development Fund (ERDF).</p>
Electrofishing data for eel in the Iberian Peninsula (SUDOANG project)
<h2><strong>1. SUDOANG PROJECT</strong></h2><p>The <a href="https://sudoang.eu/en/">SUDOANG</a> project has provided common tools to managers to support eel conservation in the SUDOE area (Spain, France and Portugal). One of the goals of the project was to develop an eel abundance and distribution <a href="https://zenodo.org/record/7546419">atlas</a> in the three countries, based on the results of the implementation of Eel Density Analysis (<a href="http://chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://sudoang.eu/wp-content/uploads/2022/02/E411_Briand_et_al_2022_EDA_report_opt-1.pdf">EDA</a>). This model extrapolates eel abundance from a range of river segments sampled by electrofishing, to the whole river and lake network, by considering how eel abundance, size and sex vary according to different parameters related to eel habitat.</p><h2><strong>2. SUDOANG DATABASE</strong></h2><p>Electrofishing data for Spain and Portugal were imported in the SUDOANG database (<a href="https://sudoang.eu/wp-content/uploads/2020/06/E121_import_tool-2.html#2_source_of_data">deliverable 1.2.1</a>), whose structure is inherited from the DataBase for EEl (DBEEL), developed during a European research project (POSE - Pilot projects to estimate potential and actual escapement of silver eel, Walker et al., 2011). This database is designed to contain all data relative to eel biology and anthropogenic pressures applying to eel. During the course of SUDOANG, this database was used and ameliorated. </p><p>The data providers (mainly SUDOE water managers, SUDOANG researchers and pilot basins from <a href="https://sudoang.eu/en/task-groups/">GT6: Task Group on eel stock monitoring transnational network</a>) are listed below:</p><ul><li><strong>SPAIN</strong><ul><li>Ministry for ecological transition and the demographic challenge (MITECO)</li><li>Spanish Fsih Chart (SIBIC): Data from different sources (indicated in the data)</li><li>Basque Water Agency (URA) - Basque Country</li><li>Gipuzkoa Council - Gipuzkoa (Basque Country)</li><li>Navarra Council (GAN-NIK) - Navarra</li><li>Asturias Council (DGPM) - Asturias</li><li>Xunta de Galicia, Consellería de Medio Ambiente, Territorio e Vivenda - Galicia</li><li>University of Córdoba (UCO) - Andalucía</li><li>Valencian Regional Hunting and Fishing Service (GVA) - Valencia</li><li>Catalan Water Agency (ACA) - Catalonia: <i>ACUERDO GOV/139/2013, de 15 de octubre, por el que se aprueba el Programa de seguimiento y control del Distrito de cuenca fluvial de Catalunya para el período 2013-2018</i></li></ul></li><li><strong>PORTUGAL</strong><ul><li>University of Porto (UP), CIIMAR</li><li>University of Lisbon, MARE: Data from different sources (indicated in the data)</li></ul></li></ul><h2><strong>3. DATA DESCRIPTION</strong></h2><p>Electrofishing data is mainly based on fishing stations, operations and eel biometry.</p><p>The <strong>station</strong> level corresponds to a location, identified by coordinates (Spatial Reference System 4326). The attributes associated with stations are:</p><ul><li><i>op_id</i>: identifier [data type: UUID]</li><li><i>institution</i>: data provider [data type: character]</li><li><i>ref_article</i>: reference to the article from which the data originated (only for SIBIC data source) [data type: character]</li><li><i>articletitle</i>: reference linked with data (only for SIBIC data source) [data type: character]</li><li><i>op_placename</i>: station name [data type: character]</li><li><i>x_espg_4326</i>: longitude (EPSG: 4326) [data type: numeric]</li><li><i>y_espg_4326</i>: latitude (EPSG: 4326) [data type: numeric]</li><li><i>country</i>: country [data type: character]</li></ul><p>The <strong>operation</strong> level corresponds to an event occurring at a specific date. At this level, a few more details such as the method or the material used, the wetted area, and electrofished length and width are added. The total number of eels, the numbers collected at each pass, and the number of measured eels are also included. The type of sampling used could not be specified but it is mostly single or several pass surveys. Therefore, the type was set to an unknown type of fishing. All electrofishing reporting eels in the second pass were considered as full electrofishing. The attributes associated with operation are:</p><ul><li><i>id</i>: identifier [data type: UUID]</li><li><i>op_id</i>: station identifier [data type: UUID]</li><li><i>op_gis_layername</i>: data provider [data type: character]</li><li><i>data_provider</i>: data provider [data type: character]</li><li><i>op_placename</i>: station name [data type: character]</li><li><i>ob_id</i>: observation identifier [data type: UUID]</li><li><i>ob_starting_date</i>: period starting date</li><li><i>ef_wetted_area</i>: wetted area of the station, in m2 [data type: numeric]</li><li><i>ef_nbpas</i>: the number of electrofishing pass during the observation [data type: numeric]</li><li><i>ef_fished_length</i>: the electrofished river length, in m [data type: numeric]</li><li><i>ef_fished_width</i>: the electrofished river width, in m [data type: numeric]</li><li><i>ob_origin</i>: origin of the observation, raw data [data type: character]</li><li><i>ob_type</i>: type of observation, electro-fishing [data type: character]</li><li><i>ob_period</i>: time step used for observation period, daily [data type: character]</li><li><i>ef_fishingmethod</i>: type of method used during the scientific sampling [data type: character]</li><li><i>ef_electrofishing_mean</i>: mean used to realize the scientific sampling, by foot [data type: character]</li><li><i>density</i>: density of eels collected, in nb/m2 [data type: numeric]</li><li><i>totalnumber</i>: total number of eels collected [data type: numeric]</li><li><i>nbp1</i>: number of eels collected during the 1st pass [data type: numeric]</li><li><i>nbp2</i>: number of eels collected during the 2nd pass [data type: numeric]</li><li><i>nbp3</i>: number of eels collected during the 3rd pass [data type: numeric]</li><li><i>nb_size_measured</i>: number of measured eels [data type: numeric]</li></ul><p>The <strong>individual </strong>level corresponds to the biological characteristics (length and weight) of the measured eels. The attributes associated with indivial are:</p><ul><li><i>dp_name</i>: name of data provider [data type: character]</li><li><i>ob_id</i>: observation identifier [data type: UUID]</li><li><i>bc_id</i>: batch (eels sampled during the observation) identifier [data type: UUID]</li><li><i>bc_ba_id</i>: sub-batch identifier [data type: UUID]</li><li><i>size</i>: total length of eel, in mm [data type: numeric]</li><li><i>fish_id</i>: individual identifier [data type: UUID]</li><li><i>weight</i>: body weight of eel, in g [data type: numeric]</li></ul><p>The three tables can be related to each other through the identifiers, meaning that the eels measured in the <strong>individual</strong> table can be identified with the <strong>operations</strong> through the <i>ob_id</i> identifier, and these operations can be linked to the <strong>stations </strong>through the <i>op_id</i> identifier, allowing for a comprehensive view of the electrofishing sampling collected for Spain and Portugal.</p><h2><strong>4. VERSIONS</strong></h2><ul><li><a href="https://doi.org/10.5281/zenodo.8009823">10.5281/zenodo.8009823 </a>1.0.0 - 2023-06-06 - Initial upload (closed access)</li><li><a href="https://doi.org/10.5281/zenodo.8207785">10.5281/zenodo.8207785</a> 1.0.1 - 2023-08-02 - Fixed data provider in station and operation tables (closed access)</li><li><a href="https://doi.org/10.5281/zenodo.8348353">10.5281/zenodo.8348353</a> 1.0.2 - 2023-15-09 - Fixed stations (providers) (closed access)</li><li><a href="https://doi.org/10.5281/zenodo.8348353">10.5281/zenodo.8348353</a> 1.0.2 - 2023-11-08 - Final version (open access)</li></ul><h2><strong>5. READ MORE</strong></h2><ul><li>Atlas of European Eel Distribution (<i>Anguilla anguilla</i>) in Portugal, Spain and France (<a href="https://doi.org/10.5281/zenodo.7546419">10.5281/zenodo.7546419</a>)</li><li>Eel data (<i>Anguilla anguilla</i>) and associated environment variables used to fit the EDA model in the SUDOE area (SUDOANG project) (<a href="https://doi.org/10.5281/zenodo.6397009">10.5281/zenodo.6397009</a>)</li><li>Cumulated dam impact in France and the Iberian Peninsula (SUDOANG project) (<a href="https://doi.org/10.5281/zenodo.8348374">10.5281/zenodo.8348374</a>)</li></ul><h2><strong>6. FUNDING</strong></h2><p>Project co-financed by the INTERREG SUDOE Programme through the European Regional Development Fund (ERDF).</p>
BVNA Community Informatics Project StoryMap
<p>This collection represents the Annexation History StoryMap created for the Beaverdam Valley Neighborhood Association Community Informatics Project, containing the associated geojson, pdf, and tiff files. The StoryMap describes the annexation history of Beaverdam Valley (including the registered neighborhoods of Beaverdam Valley, Hills of Beaverdam, and Beaverdam Run) in Asheville, North Carolina from 1929 until August 2023 with the most recent annexation being in 2005. The collection includes the following associated files: </p> <ul> <li>"<a href="https://zenodo.org/api/files/53a59d4e-6f52-4b69-a634-f468c4fcce7c/Annex%201929-1960.tiff?versionId=4f2e551a-975a-461b-9c7d-9632ab598fa1">Annex 1929-1960.tiff</a>": image file showing Annexations in Beaverdam Valley through 1960</li> <li>"<a href="https://zenodo.org/api/files/53a59d4e-6f52-4b69-a634-f468c4fcce7c/Annex%201929-1964.tiff?versionId=063b677a-45a1-4b14-8586-884f91be865a">Annex 1929-1964.tiff</a>": image file showing Annexations in Beaverdam Valley through 1964</li> <li>"<a href="https://zenodo.org/api/files/53a59d4e-6f52-4b69-a634-f468c4fcce7c/Annex%201929-1980.tiff?versionId=9388d5e7-75e4-4025-8e9c-e661a7cf46f1">Annex 1929-1980.tiff</a>": image file showing Annexations in Beaverdam Valley through 1980</li> <li>"<a href="https://zenodo.org/api/files/53a59d4e-6f52-4b69-a634-f468c4fcce7c/Annex%201929-1991.tiff?versionId=e0f49d03-59b9-4ba3-95e4-a31ce5991603">Annex 1929-1991.tiff</a>" image file showing Annexations in Beaverdam Valley through 1991</li> <li>"<a href="https://zenodo.org/api/files/53a59d4e-6f52-4b69-a634-f468c4fcce7c/Annex%201929-1995.tiff?versionId=4412d044-aae1-4cc3-8826-7429519c9358">Annex 1929-1995.tiff</a>": image file showing Annexations in Beaverdam Valley through 1995</li> <li>"<a href="https://zenodo.org/api/files/53a59d4e-6f52-4b69-a634-f468c4fcce7c/Annex%201929-2005.tiff?versionId=0469a2e7-7132-46cd-b5df-5769be0082f2">Annex 1929-2005.tiff</a>": image file showing Annexations in Beaverdam Valley through 2005</li> <li>"<a href="https://zenodo.org/api/files/53a59d4e-6f52-4b69-a634-f468c4fcce7c/Annex%201929.tiff?versionId=d0666b27-80b8-48bd-b4cf-a43cedde1f32">Annex 1929.tiff</a>": image file showing Annexation in Beaverdam Valley in 1929 </li> <li>"Asheville_Annexation_History.geojson": City of Asheville GIS Annexation history</li> <li>"Asheville_Annexation_History.qmd": QGIS metadata for the above</li> <li>"Beaverdam_Annexation_Storymap_Final.pdf": pdf of StoryMap</li> </ul> <p>About the Project: The Beaverdam Valley Neighborhood Association (BVNA) Community Informatics Project aims to gain deeper understanding of the Beaverdam Valley community and to work towards gathering and sharing information about the community and its history. This collection represents a deliverable produced under the 2022-2023 City of Asheville Neighborhood Matching Grant program. </p>
IRISTECH Research Project
<p>irisTECH aspires to develop a novel, easy-to-use and low-cost, variable rate fertilization system for application in linear crops. The project is based on hyperspectral sensors and actuators combined with the functions of a system utilizing artificial intelligence technology in order to perform real time identification of crop/soil system in fertilizer and then apply the necessary quantity of granular fertilizers if needed. The proposed approach is opposed to existing solutions that require information collection, offline map editing and mapping, and then re-visit to the field for application. irisTECH utilizes state-of-the-art technologies and in particular emerging developments in the field of internet of things (IoT), artificial intelligence, embedded systems, cloud computing and image processing. In this rapidly expanding market, from a commercial perspective, the proposed project aspires to be an important research and innovation action with a targeted impact at national and wider international level.</p>
Inferring whole-genome histories in large population datasets: inferred tree sequences for Simons Genome Diversity Project
<p>Tree sequences inferred for the SGDP autosomes using <a href="https://tsinfer.readthedocs.io/">tsinfer</a> version 0.1.4 and compressed using <a href="https://tszip.readthedocs.io/en/stable/">tszip</a>. Tree sequences can be decompressed as follows:</p> <pre><code class="language-bash">$ tsunzip sgdp_chr1.trees.tsz</code></pre> <p>Once decompressed, trees files can be loaded and processed using <a href="https://tskit.readthedocs.io">tskit</a>. </p> <pre><code class="language-python">import tskit ts = tskit.load("sgdp_chr1.trees") # ts is an instance of tskit.TreeSequence print("Chromosome 1 contains {} trees".format(ts.num_trees))</code></pre> <p>Metadata associated with individuals and populations was derived from the original <a href="https://sharehost.hms.harvard.edu/genetics/reich_lab/sgdp/SGDP_metadata.279public.21signedLetter.samples.txt">source</a> and converted to JSON form. For example, to access individual metadata we can use:</p> <pre><code class="language-python">import tskit import json ts = tskit.load("sgdp_chr1.trees") ind = ts.individual(0) metadata_dict = json.loads(ind.metadata)</code></pre> <p>The metadata_dict variable will now contain all the metadata for the individual with ID 0 as a dictionary. Metadata associated with populations can be found in a similar way. Population IDs are associated with individuals via their constituent nodes. For example,</p> <pre><code class="language-python">pop_metadata = [json.loads(pop.metadata) for pop in ts.populations()] ind_node = ts.node(ind.nodes[0]) ind_pop_metadata = pop_metadata[ind_node.population]</code></pre> <p>After this, the ind_pop_metadata variable will contain the population level metadata for individual ID 0.</p> <p>The full data pipeline used to generate these tree sequences and associated metadata is available on <a href="https://github.com/mcveanlab/treeseq-inference/tree/master/human-data">GitHub</a>.</p>
Central Valley Project, Genetic Determination of Population of Origin 2011-2021
Central Valley Chinook Salmon populations differ in their Endangered Species Act listing status. It is often difficult to distinguish individuals from the different Evolutionarily Significant Units. As such, many of the salmon monitoring and evaluation efforts in the Central Valley and San Francisco Bay-Delta are hampered by uncertainty about population (stock) identification and proportional effects of management actions (Dekar et al. 2013; IEP 2019). Studies have identified that the current identification method (length-at-date models) of juvenile Chinook salmon (Fisher 1992) captured in the watershed vary in their accuracy, particularly for spring-run (NMFS 2013; Harvey et al. 2014; Merz et al. 2014). The inaccuracy of the size-based methods is likely due to differences in fish distribution during early rearing, habitat-specific growth rates, and inter-annual variability in temperatures and food availability that lead to overlap in size ranges among stocks. The primary objective of this project was the genetic classification (to race; Evolutionary Significant Unit) of Chinook Salmon captured from State Water Project and Central Valley Project fish protection facilities and Interagency Ecological Program monitoring programs. The population-of-origin was determined for sampled fish by comparing their genotypes to reference genetic baselines. Genetic methods, having less statistical uncertainty that size-based models for population identification, were intended to directly target (and reduce) one source of uncertainty in the estimation of loss (take) from water diversions (operations) and develop the information necessary for understanding stock-specific distribution, habitat utilization, abundance, and life history variation. This project supports recommendations from the Interagency Ecological Program’s Salmon and Sturgeon Assessment of Indicators by Life Stage and Interagency Ecological Program Science Agenda efforts to improve Central Valley salmonid monitoring
El Yunque National Forest Vegetation Monitoring Project data, 2019-2021
This data package includes data collected as part of the El Yunque National Forest (EYNF) Vegetation Monitoring Project, the first phase of which was conducted between January 2019 and April 2021 and is now completed. EYNF is coterminous with the Luquillo Experimental Forest. The project was implemented as a collaborative endeavor between the Amigos de El Yunque Foundation, the USDA Forest Service, and the University of Puerto Rico-Río Piedras Campus. Funding was provided by the Forest Service. It includes data for 40 0.1-ha circular plots located in secondary forest within the subtropical moist and wet life zones, ranging in elevation from approximately 100-600 m asl. Plots are classified into three groups based on combinations of their historical canopy cover and post-agricultural regeneration pathways. The first group corresponds to secondary forest plots with >50% canopy cover in 1936 that have continued to recover via passive natural regeneration (>50 P plots). The second group corresponds to secondary forest plots with <50% canopy cover in 1936 that have continued to recover via passive natural regeneration (<50 P plots). The third group corresponds to secondary forest plots that also had <50% cover in 1936 and experienced a combination of both assisted and passive natural regeneration (50 A+P plots). The assisted regeneration occurred up to the early 1980s. Since the 1980s this third group of plots has only undergone exclusively passive natural restoration. Eleven plots (total area = 1.1 ha) are classified as >50 P, 21 plots (total area = 2.1 ha) are classified as <50 P, and 8 plots (total area = 0.8 ha) as <50 A+P. There are two data sets. The first represents general plot and ground cover data for the 40 plots. The second represents tree composition, structure, biomass, and ecosystem service data for 4242 trees within the 40 plots. Data were collected using i-Tree Eco methodology.
REU data set from summer of 2022. Project was designed to understand how crayfish (Faxonius rusticus) respond to chemical cues from largemouth bass predators under different shelter distributions.
Research into predator–prey interactions has focused on the landscape of fear and nonconsumptive effects that result from prey responses. Prey behavior is influenced by predator presence and the location and quality of foraging resources in habitats. These areas have been fruitful, but the role of prey refuges has lagged. We investigated how refuge spatial distribution and quality influence prey behavior. To determine the role of the landscape of safety (LOS) in prey decision-making, we altered spatial relationships between refuges, refuge quality, and predation threats in mesocosms. Mesocosms were constructed such that prey only received predatory chemical cues. We employed a behavioral assay including largemouth bass (Micropterus salmoides (Lacepède, 1802): predator) and virile crayfish (Faxonius rusticus (Girard, 1852): prey). Crayfish shelter use was significantly influenced by quality and spatial relationship of shelters to predatory threats, and the interaction of these two factors. Particularly, crayfish used high-quality shelters more often when located closer to predatory cues than farther away and did not use low-quality shelters more than controls. High-quality shelter usage decreased as threat level (measured by gape ratio) decreased. These results support the idea that prey utilize an LOS, and information contained in these two landscapes may alter behavioral decisions.
South Bay Salt Pond Restoration Project – Phase-1 (2010-2012) Sentinel Species Health Monitoring.
The South Bay Salt Pond Restoration Program (SBSPRP) is the largest wetland restoration project in the western United States, restoring approximately 15,000 acres of former salt evaporation ponds (southbaysaltpond.org) to benefit wildlife and fish populations. Restoration on a large scale comes with many risks and uncertainties. Therefore, restoration was planned in several phases, with an adaptive management approach and applied scientific studies to address the uncertainty of different restoration strategies. These strategies included breaching ponds to create fully tidal habitats, installing tide gates to create muted tidal habitats and active management of existing ponds. This mosaic of restoration designs was intended to benefit many species of salt marsh dependent biota, including birds, fish and mammalian species. The Longjaw Mudusucker (Gillichthys mirabilis) is a resident estuarine fish, ranging from Mexico to Humboldt Bay, California, USA, and is one of the most abundant fishes in high intertidal salt-marsh habitat. The Longjaw Mudsucker depends on high intertidal creeks in marshes dominated by pickleweed (Sarcocornia sp). The fish reside within burrows in soft sediments and is the only fish species that can remain in intertidal creeks during low tide when the creeks completely de-water. Longjaw Mudsucker have a wide tolerance range for salinity, up to 80-ppt and can be the only fish species to occupy industrial salt ponds in the San Francisco Estuary. In this study, UC Davis conducted minnow trap sampling in remnant pickleweed marshes and adjacent salt pond restorations to document the distribution, relative abundance, and condition (length-weight) of fish occupying these extant and restored habitats. During the pilot effort in late summer-fall of 2010 we conducted minnow trap sampling across a number of sites in the Alviso Marsh, Eden Landing Marsh, Ravenswood Marsh and Bair Island Marsh, sampling muted restoration ponds, tidal restoration ponds and remn
The Jefferson Project 2021 water quality data from three vertical profiler stations in Lake George, NY, USA.
The Jefferson Project at Lake George -- a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association -- combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2021, The Jefferson Project deployed three vertical profiler stations on the lake, collecting data on water quality and weather. Meteorological data have been included with the Jefferson Project Weather Station dataset for 2021. These vertical profiler stations are named VP_AnthonysNose, VP_HarrisBay, and VP_TeaIsland. The water quality data are collected by a YSI EXO2 Multi-parameter sonde sensors. The sensors collect data at 1 meter or less depth increments, starting at 1 meter and proceeding to 2 meters off bottom. The data are transferred in near real-time to an off-site database for monitoring and review. The data provided here have undergone data correction by Jefferson Project researchers.
The Jefferson Project 2021 hydrologic, water quality, and soil quality data from 12 Tributary Stations within the Lake George basin, NY, USA.
The Jefferson Project at Lake George -- a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association -- combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2021, The Jefferson Project had twelve tributary monitoring stations around the lake collecting data on water quality, soil quality, and hydrology. These stations are TS_Finkle, TS_Hague, TS_Indian, TS_NorthwestBay, TS_Outlet, TS_PoleHill, TS_English, TS_Sunset, TS_Sucker, TS_ShelvingRock, TS_East, and TS_West. The stations have a sensor payload that may include some or all of the following sensors: YSI EXO2 Multi-parameter sonde, Campbell Scientific CS451 pressure transducer, SonTek-IQ+ multi-beam acoustic flow meter, Sontek-SL Doppler current meter, YSI WaterLOG® H-3123 submersible pressure transducer, and Stevens HydraProbe soil moisture sensor. The sensors collect data at high-frequency (~1 sample per minute) and the data are transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. The data provided here are level 4 data which underwent data correction and downsampling to an hourly frequency.
The Jefferson Project 2021 weather data from ten surface weather stations on Lake George, NY, USA.
The Jefferson Project at Lake George – a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association – combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2021, The Jefferson Project had ten weather monitoring stations on and around the lake collecting data on precipitation, temperature, wind speed, wind direction, barometric pressure, and relative humidity. These stations are WX_CedarLane, WX_DFWI, WX_GullRock, WX_MossyPoint, WX_WhaleRock, WX_PilotKnob, WX_Glenburnie, VP_TeaIsland, VP_AnthonysNose, and VP_HarrisBay. The stations have a sensor payload that include some combination of the following sensors: Rotronic HC2-S3 sensor, Campbell Scientific CS616 soil moisture sensor, Li-Cor LI-200R pyranometers, RM Young 85006 anemometer, Vaisala Weather Transmitter WXT series (520 & 530 models), HyQuest TB3 tipping bucket rain gauge, and N-Con wet deposition sampler. The sensors collect data at high-frequency (~1 sample per minute) and the data are transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. The data provided here are level 4 data which has undergone data correction and downsampling to an hourly frequency.
Repeated vegetation monitoring for riparian forest restoration project, Santa Clara River, CA, 2015-2023.
We implemented a spatially-patterned methodology to restore 87 ha of riparian forest habitat, selectively applying multiple restoration approaches based on localized differences in degradation severity throughout the project area. This work was conducted as part of a large, collaborative effort to control invasive Arundo donax and reestablish contiguous natural habitat throughout the Santa Clara River floodplain in southern California.
The Jefferson Project 2022 weather data from nine surface weather stations on Lake George, NY, USA.
The Jefferson Project at Lake George – a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association – combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2022, The Jefferson Project had nine weather monitoring stations on and around the lake collecting data on precipitation, temperature, wind speed, wind direction, barometric pressure, and relative humidity. These stations are WX_CedarLane, WX_DFWI, WX_GullRock, WX_MossyPoint, WX_WhaleRock, WX_PilotKnob, WX_Glenburnie, VP_TeaIsland, and VP_HarrisBay. The stations have a sensor payload that include some combination of the following sensors: Rotronic HC2-S3 sensor, Campbell Scientific CS616 soil moisture sensor, Li-Cor LI-200R pyranometers, RM Young 85006 anemometer, Vaisala Weather Transmitter WXT series (520 & 530 models), HyQuest TB3 tipping bucket rain gauge, and N-Con wet deposition sampler. The sensors collect data at high-frequency (~1 sample per minute) and the data are transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. The data provided here are level 4 data which has undergone data correction and downsampling to an hourly frequency.
The Jefferson Project 2022 hydrologic, water quality, and soil quality data from 11 Tributary Stations within the Lake George basin, NY, USA.
The Jefferson Project at Lake George -- a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association -- combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2022, The Jefferson Project had eleven tributary monitoring stations around the lake collecting data on water quality, soil quality, and hydrology. These stations are TS_Finkle, TS_Hague, TS_Indian, TS_NorthwestBay, TS_Outlet, TS_PoleHill, TS_English, TS_Sunset, TS_ShelvingRock, TS_East, and TS_West. The stations have a sensor payload that may include some or all of the following sensors: YSI EXO2 Multi-parameter sonde, Campbell Scientific CS451 pressure transducer, SonTek-IQ+ multi-beam acoustic flow meter, Sontek-SL Doppler current meter, YSI WaterLOG® H-3123 submersible pressure transducer, and Stevens HydraProbe soil moisture sensor. The sensors collect data at high-frequency (~1 sample per minute) and the data are transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. The data provided here are level 4 data which underwent data correction and downsampling to an hourly frequency.
The Jefferson Project 2022 water quality data from two vertical profiler stations in Lake George, NY, USA.
The Jefferson Project at Lake George -- a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association -- combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2022, The Jefferson Project deployed two vertical profiler stations on the lake, collecting data on water quality and weather. Meteorological data have been included with the Jefferson Project Weather Station dataset for 2022. These vertical profiler stations are named VP_HarrisBay and VP_TeaIsland. The water quality data are collected by a YSI EXO2 Multi-parameter sonde sensors. The sensors collect data at 1 meter or less depth increments, starting at 1 meter and proceeding to 2 meters off bottom. The data are transferred in near real-time to an off-site database for monitoring and review. The data provided here have undergone data correction by Jefferson Project researchers.
Iran Deposit Refund System Pilot Project for Beverage Containers - 2024 - 2025
The dataset is the result of the first-ever deposit refund system (DRS) pilot project conducted in Iran, including the accurate daily intake of containers by the Reverse Vending Machine in the project, the number of existing and new users and the number of fulfilled recycling cycle (session) per day. The pilot was performed in Iran University of Science and Technology central campus in Tehran for total 12 months and evaluated the effectiveness of different incentive mechanisms for beverage container recycling. The study compared three approaches using in-house labeling of the single use beverage containers and a barcode-tracking system: reward-based recycling, voluntary recycling, and deposit-based recycling. Results demonstrated that the deposit-based system significantly outperformed other methods.
Qualitative Larval Fish Sampling at the California Department of Water Resource’s State Water Project
The California Department of Water Resource’s State Water Project utilizes the John E. Skinner Delta Fish Protective Facility (Skinner Fish Facility) to salvage fishes that would otherwise become entrained during operations to divert water from the Sacramento-San Joaquin River Delta (Delta). Water is diverted from the Delta to meet California’s agricultural, municipal, industrial, and environmental needs. The Skinner Fish Facility, located in Contra Costa County and situated ahead of the Harvey O. Banks Pumping Plant, began salvaging fish in 1968 but historically, only recorded fork length measurements for fish greater than 20 millimeters. Beginning in 2009, the Skinner Fish Facility implemented qualitative larval sampling in response to the 2008 U.S. Fish and Wildlife Service Biological Opinion on the coordinated operations of the Central Valley Project (CVP) and State Water Project (SWP). This entailed collecting, retaining, and identifying larval fishes to better understand SWP impacts on Delta Smelt. Qualitative larval sampling took place annually from 2009 through 2025, during the Old and Middle River management period and based upon Delta Smelt spawning (typically mid-February to June). The California Department of Water Resources collected and processed samples from 2020 through 2025. Data from 2009 through 2019 were processed and retained by others and are not included in this dataset.
Chinook Salmon genetic assignments for the Central Valley Project (CVP) and State Water Projects (SWP), Sacramento and San Joaquin Delta Waters, CA, 2024-25
Central Valley Chinook Salmon populations differ in their Endangered Species Act listing status. It is difficult to visually distinguish individuals from the different Evolutionarily Significant Units. As such, many of the salmon monitoring and evaluation efforts in the Central Valley and San Francisco Bay-Delta are hampered by uncertainty about population (stock) identification and proportional effects of management actions (Dekar et al. 2013; IEP 2019). Studies have identified that the current identification method (length-at-date models) of juvenile Chinook salmon (Fisher 1992) captured in the watershed vary in their accuracy, particularly for spring-run (NMFS 2013; Harvey et al. 2014; Merz et al. 2014). The inaccuracy of the size-based methods is likely due to differences in fish distribution during early rearing, habitat-specific growth rates, and inter-annual variability in temperatures and food availability that lead to overlap in size ranges among stocks. The primary objective of this project was the genetic classification (to genetic lineage; Evolutionary Significant Unit) of Chinook Salmon captured from State Water Project and Central Valley Project fish protection facilities and Interagency Ecological Program compliance monitoring programs. The genetic lineage was determined for sampled fish by comparing their genotypes to reference genetic baselines. Genetic methods, having less statistical uncertainty that size-based models for population identification, were intended to directly target (and reduce) one source of uncertainty in the estimation of loss (take) from water diversions (operations) and develop the information necessary for understanding stock-specific distribution, habitat utilization, abundance, and life history variation. This project supports recommendations from the Interagency Ecological Program’s Salmon and Sturgeon Assessment of Indicators by Life Stage and Interagency Ecological Program Science Agenda efforts to improve Central Valley
SGS-LTER Long-Term Monitoring Project: Vegetation Cover on Small Mammal Trapping Webs on the Central Plains Experimental Range, Nunn, Colorado, USA 1999 -2006, ARS Study Number 118 (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-sgs/140/17. The abstract below was extracted from the Level 0 data package and is included for context: This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. Additional information and referenced materials can be found: http://hdl.handle.net/10217/83458. The abundance and diversity of small mammals in shortgrass steppe is strongly influenced by the structure and composition of vegetation. Vegetation structure provides cover from predators and harsh abiotic conditions. Plant species composition affects the types of seeds and herbaceous material available to granivores and herbivores, and influences arthropod populations, which are important prey for the omnivorous species that dominate in shortgrass steppe. Both vegetation structure and plant community composition are sensitive to the availability of precipitation as well as the activity of large mammalian herbivores. In 1999, we began measuring vegetation structure and plant community composition on the three grassland and three shrubland trapping webs where we live-trap small mammals
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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.