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[Dataset] Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects - Raw Data
<p><strong>Explanation/Overview:</strong></p> <p>Corresponding raw data for the analyses described in D3.3 (can be found here), which are the result of our research that culminated into the publication "Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects", a conference paper for the conference CollabTech 2022: <a href="https://link.springer.com/book/10.1007/978-3-031-20218-6">Collaboration Technologies and Social Computing</a> and published as part of the <a href="https://link.springer.com/bookseries/558">Lecture Notes in Computer Science</a> book series (LNCS,volume 13632) <a href="https://link.springer.com/chapter/10.1007/978-3-031-20218-6_5">here</a>. Usernames have been anonymised.</p> <p>The raw data is in the <code>.json</code> format and can be read by most languages/tools. It is recommended to import the data into a MongoDB to work with it.</p> <p><strong>Purpose:</strong></p> <p>The purpose of this dataset is to provide the basis for possible further examinations, involving additional (not yet analysed) features such as the content of the comments etc. and also new ways of extracting networks.</p> <p><strong>Relatedness:</strong></p> <p>The data of the different projects was derived from the forums of 7 Zooniverse projects based on similar discussion board features. The projects are: 'Galaxy Zoo', 'Gravity Spy', 'Seabirdwatch', 'Snapshot Wisconsin', 'Wildwatch Kenya', 'Galaxy Nurseries', 'Penguin Watch'.</p> <p><strong>Content:</strong></p> <p>The dataset contains three files:</p> <ul> <li><code>Comments.json</code> <ul> <li>contains the basic data representation with multiple fields (e.g., <code>time_created</code>, <code>user_login</code>). Each data field represents a comment.</li> </ul> </li> <li><code>Discussions.json</code> <ul> <li><code></code>contains all discussions. Each data field is a discussion, with multiple fields (e.g., <code>comments_count</code>, <code>user_login</code>)</li> </ul> </li> <li><code>Projects.json</code> <ul> <li><code></code>contains all projects. Each data field is a project, with multiple fields (e.g., <code>project_id</code>, <code>description</code>)</li> </ul> </li> </ul> <p><strong>Grouping:</strong></p> <p>The projects (and thus the corresponding discussions and comments) were collected on the basis of common forum features such as the discussion boards.</p>
Uncertainty in Migration Scenarios. QuantMig Project Deliverable D9.2 Data Description
<p>This open data deposit contains the data and code accompanying used in the report: Barker and Bijak (2021), Uncertainty in Migration Scenarios, QuantMig Project Deliverable D9.2. The cover note should be read in conjunction with the report, available via www.quantmig.eu, and with the individual readme files in the data folders that can be found within this Zenodo repository (DOI: 10.5281/zenodo.7709443).</p>
Soil moisture sensor network, design, location attributes and soil properties, Hainich, Germany, project AquaDiva
<p>This dataset contains information of the small scale highly resolved soil moisture measurement network that is part of the of the AquaDiva Critical Zone exploratory, Hainich National Park, Germany. The dataset contains information on soil measurement locations, as well as attributes to the location, the design type (random locations vs transects), as well as locations attributes like distance to the next tree and soil properties. Measurement design was first introduced by Metzger et al., (2017), and used in Fischer et al., 2023. See there for more information.</p> <p><strong>References</strong></p> <p>Fischer-Bedtke, C., Metzger, J. C., Demir, G., Wutzler, T., and Hildebrandt, A.: Throughfall spatial patterns translate into spatial patterns of soil moisture dynamics – empirical evidence, Hydrology and Earth System Sciences, https://doi.org/10.5194/hess-2022-418, 2023.</p> <p>Metzger, J. C., Wutzler, T., Dalla Valle, N., Filipzik, J., Grauer, C., Lehmann, R., Roggenbuck, M., Schelhorn, D., Weckmüller, J., Küsel, K., Totsche, K. U., Trumbore, S., and Hildebrandt, A.: Vegetation impacts soil water content patterns by shaping canopy water fluxes and soil properties, Hydrological Processes, 31, 3783–3795, https://doi.org/10.1002/hyp.11274, 2017.</p>
Princeton Ethiopian, Eritrean, and Egyptian Miracles of Mary (PEMM) Project
<p>The Princeton Ethiopian, Eritrean, and Egyptian Miracles of Mary digital humanities project (PEMM) is a comprehensive resource for the miracle stories about the Virgin Mary in Ethiopia, Eritrea, and Egypt, and preserved in Gəˁəz parchment manuscripts between 1300 and the present. Directed by Prof. Wendy Laura Belcher and then managed by Evgeniia Lambrinaki, PEMM was launched in March 2018, using as its base the miracle story identifications William F. Macomber made in the 1980s.</p> <p><strong>Dataset</strong>. PEMM 2.0 includes the data collected by the project in Google Sheets from its inception to July 4, 2023. This date marked the end of our use of Google Sheets as our database and the end of Jeremy Brown's full-time involvement with the project (when he moved to be the cataloger of Ethiopic manuscripts at HMML). This data includes 1,002 identified stories (or 940 separate stories) (called Canonical Stories); 549 stories translated into English (288 stories translated by PEMM team; 223 stories translated and published by others) and another 200 stories summarized; 676 fully cataloged manuscripts (with another 334 identified, but awaiting digitization) (in Gəˁəz and a few in Arabic) (called Manuscripts); 51,690 stories documented in those manuscripts (called Story Instances); 21,403 typed Gəˁəz incipits (unique first lines) for those stories; and 2,547 paintings with 4,205 scenes in 262 manuscripts (called Paintings). The manuscripts come from 92 repositories and libraries around the world (called Collections) and the stories were composed in Ethiopia, Eritrea, and Egypt (and probably Nubia, although not confirmed), as well as Europe and the Levant (called Story Origins).</p> <p><strong>Database</strong>. The PEMM Project began by using Google Sheets as a lightweight relational database. To learn about this innovative digital humanities approach by Princeton’s CDH’s, read the “Is a Spreadsheet a Database?” (February 21, 2021) article by PEMM lead developer, Rebecca Sutton Koeser. Due to our extremely large dataset (7 Google sheets in one workbook, each with at least 40 columns, and one with 50,000 rows, with dozens of complex formulas linking the fields in the various sheets), Google Sheets would repeatedly hang up. So, in July we migrated all our data to an Aurora PostgreSQL database, accessing it with a content management system called Directus. However, this Zenodo dataset represents the data as it last appeared in Google Sheets.</p> <p><strong>Website</strong>. The current PEMM website (not yet its web application and data portal) is at https://pemm.princeton.edu. We will launch the full web application and data portal in mid-fall 2023.</p> <p><strong>Team</strong>. PEMM was created in collaboration with Princeton’s Center for Digital Humanities (mainly with Rebecca Sutton Koesser, Jean Bauer, and Nicholas Budak, but with additional support from Gissoo Doroudian, Rebecca Munson [of beloved memory], and Kevin McElwee); directed by Prof. Wendy Laura Belcher; managed primarily by Evgeniia Lambrinaki into mid-2022 and then by Blaine Kebede; contributed to by catalogers Jeremy Brown, Mehari Worku, Dawit Muluneh, Solomon Gebreyes, Vitagrazia Pisani, Ekaterina Pukhovaia, and Steve Delamarter; web programmed by Henok Alem, who was assisted by Pak Hei Li, Ayomikun M. Gbadamosi, and Marew Masresha; edited by Taylor Eggan, assisted by Bret Windhauser; assisted by Hanni Makonnen for geolocating; typed by volunteers (including Mihret Melaku, Tariku Abas Sherif, Beimnet Beyene Kassaye, Annabel S. Lemma, Tsega-ab Hailemichael, Chiara Lombardi, and Ellen Perleberg); and translatated and/or summarized by Princeton undergraduates (including Lauren D. Johnson, Sana Khan, Jason O. Seavey, Leia R. Walker, Nati Arbelaez Solano, Daniel Somwaru, Mika J. Hyman, Grace Matthews, Allie V. Mangel, Ellen Li, Elliot Galvis). Support at Princeton is provided by Michael Franz and Amanda M. Arcamone.</p> <p><strong>Partners</strong>. Among its board members are Elias Wondimu, Melaku Terefe, Solomon Gebreyes, Eyob Derillo, Meron Gebreananaye, Sofanit T. Abebe, Habte Michael Kidane, Hagos Abrha, Mussie Berhe, Woldesemait Teklehaymanot, and Alessandro Bausi. Among PEMM’s institutional partners are Beta Maṣāḥǝft: Manuscripts of Ethiopia and Eritrea at the Hiob Ludolf Centre for Ethiopian Studies of the Universität Hamburg, created and directed by Principal Investigator Alessandro Bausi; Hill Museum & Manuscript Library, led by Father Columba Stewart; and the British Library, Asian and African Collections, with Eyob Derillo as cataloger.</p> <p><strong>Internal Funding</strong>. PEMM’s first and second phase were made possible by the Princeton Center for Digital Humanities, directed by Meredith Martin, and its team of Natalia Ermolaev, Rebecca Sutton Koeser, Gissoo Doroudian, Rebecca Munson (of beloved memory), Nick Budak, and Kevin McElwee. The second phase was supported by a CDH Research Partnership grant. The third phase was funded by the Princeton Humanities Council, executive directed by Kathleen Crown, through the David A. Gardner Innovation Grants for New Projects in the Humanities, and the University Committee on Research in the Humanities and Social Sciences. Other important funders throughout were the Princeton Department of African American Studies, directed by the Eddie S. Glaude, as well as the Program in Gender and Sexuality Studies (directed by Wallace Best), the Program in African Studies (directed by Emmanuel Kreike and now Chika Okeke-Agulu), the Center for the Study of Religion (directed by Jonathan Gold), and the Department of Comparative Literature (directed by Thomas Hare).</p> <p><strong>External funding</strong>. PEMM’s fourth phase was made possible by two major grants from the National Endowment for the Humanities, awarded for work from fall 2021 through summer 2024. In the 1970s, NEH provided funding for the Ethiopian Manuscript Microfilm Library (EMML), which microfilmed thousands of manuscripts in Ethiopia, which serve as the backbone for the PEMM project. Today, the NEH Scholarly Editions and Scholarly Translations Grant funds the team of experienced researchers with rare language skills to catalog stories in parchment manuscripts, translate stories into English, and write short introductions to them. The NEH Digital Humanities Advancement Grant funds a public-facing open-access web application and data portal to share the stories in, images about, translations of, and scholarship on this crucial body of medieval African literature and to build upon our innovative prototype tool for searching in Gəˁəz.</p>
The Jefferson Project 2017 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 and overall water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2017, The Jefferson Project deployed two vertical profiler stations on the lake, collecting data on water quality and meteorology. Meteorological data have been included with the Jefferson Project Weather Station dataset for 2017. These vertical profiler stations are named VP_AnthonysNose and VP_TeaIsland. The water quality data are collected by EXO2 Multi-parameter sonde sensors. The sensors collect data at 1 meter depth increments, starting at 1 meter and proceeding to 2 meters off bottom. The data is transferred in near real-time to an off-site database for monitoring and review. The data provided have undergone data correction by Jefferson Project researchers.
The Jefferson Project 2017 weather data from seven 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 and overall water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2017, The Jefferson Project had five weather monitoring stations around the lake collecting data on precipitation, temperature, wind, and air quality. These stations are 'WX-CedarLane', 'WX-DFWI', 'WX-GullRock', 'WX-MossyPoint' and 'WX-WhaleRock'. Weather data from two vertical profiler sites, 'VP-AnthonysNose' and 'VP-TeaIsland', are also included in this dataset. The stations have a sensor payload that include some combination of the following sensors: HC2-S3 sensor, Campbell Scientific CS616 soil moisture sensor, LiCor LI-200R pyranometers, RM Young 85006 anemometer, Vaisala Weather Transmitter WXT series (520 & 530 models), HyQuest TB3 tipping bucket rain gauge, N-Con wet deposition sampler. The sensors collect data at high-frequency (~1 sample per minute) and the data is transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. Data provided is level 4 data which has undergone data correction and down sampling to an hourly frequency.
Directed Outflow Project Lower Trophic Study
The upper San Francisco Bay Estuary and Sacramento San Joaquin Delta is critical habitat for the endemic Delta Smelt (Hypomesus transpacificus), an endangered planktivorous fish with an annual, semi-anadromous life cycle. Freshwater outflow actions during the fall season are hypothesized to improve Delta Smelt habitat and therefore Delta Smelt condition before the spawning life stage. As part of efforts to evaluate the effectiveness of such actions to benefit Delta Smelt populations, various studies were initiated by the U.S. Bureau of Reclamation under the Directed Outflow Project (DOP). One component of the DOP was to evaluate how the Fall X2 freshwater outflow action changed the lower trophic prey available to Delta Smelt. The action is hypothesized to change abiotic and biotic aspects that can benefit Delta Smelt habitat by using freshwater outflow to maintain the position of X2 (defined as the distance in kilometers from the Golden Gate Bridge to the tidally averaged 2 ppt salinity isohaline) at around 70 km during wet or above normal precipitation water years. This moves the low salinity zone (0.5 - 6 ppt) further seaward into Suisun Bay which increases the area of preferred Delta Smelt habitat. Field sampling began the September of 2017 and occurred bi-weekly through the end of November, paired with the U.S. Fish and Wildlife Service Enhanced Delta Smelt Monitoring program. Three random sites were sampled per target region (Suisun Bay, Suisun Marsh, the Lower Sacramento River, the Cache Slough Complex, and the Sacramento Deep Water Ship Channel) per week. Sites were chosen using a Generalized Random Tessellation Stratified design. During 2018, sampling was increased to weekly and in 2019-2020 sampling began in April and ended in November. Sampling occurs for three different habitat types: shoal, channel surface and channel deep at each site depending on the depth of the water column. The shoal habitat is less than 10 feet, channel habitat is greater than 10 f
The Jefferson Project 2018 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 2018, 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 is transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. Data provided is level 4 data which underwent data correction and downsampling to an hourly frequency.
The Jefferson Project 2019 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 2019, 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 is transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. Data provided is level 4 data which underwent data correction and downsampling to an hourly frequency.
The Jefferson Project 2018 weather data from eight 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 2018, The Jefferson Project had six weather monitoring stations around the lake collecting data on precipitation, temperature, wind, and air quality. These stations are WX_CedarLane, WX_DFWI, WX_PilotKnob, WX_GullRock, WX_MossyPoint, and WX_WhaleRock. Weather data from two vertical profiler sites, VP_AnthonysNose and VP_TeaIsland, are also included in this dataset. 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, N-Con wet deposition sampler. The sensors collect data at high-frequency (~1 sample per minute) and the data is transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. Data provided is level 4 data which has undergone data correction and downsampling to an hourly frequency.
The Jefferson Project 2019 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 2019, The Jefferson Project had seven weather monitoring stations around the lake collecting data on precipitation, temperature, wind speed, wind direction, barometric pressure, and relative humidiity. These stations are WX_CedarLane, WX_DFWI, WX_GullRock, WX_MossyPoint, WX_WhaleRock, WX_PilotKnob, and WX_Glenburnie. Weather data from three vertical profiler sites (VP_AnthonysNose, VP_CalvesPen, and VP_TeaIsland) are also included in this dataset. 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, N-Con wet deposition sampler. The sensors collect data at high-frequency (~1 sample per minute) and the data is transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. Data provided is level 4 data which has undergone data correction and downsampling to an hourly frequency.
The Jefferson Project 2018 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 2018, 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 2018. These vertical profiler stations are named VP_AnthonysNose and VP_TeaIsland. The water quality data are collected by YSI EXO2 Multi-parameter sonde sensors. The sensors collect data at 1 meter depth increments, starting at 1 meter and proceeding to 2 meters off bottom. The data is transferred in near real-time to an off-site database for monitoring and review. The data provided have undergone data correction by Jefferson Project researchers.
The Jefferson Project 2019 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 2019, 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 2019. These vertical profiler stations are named VP_AnthonysNose, VP_CalvesPen, and VP_TeaIsland. The water quality data are collected by a YSI EXO2 Multi-parameter sonde sensors. The sensors collect data at 1 meter 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 2020 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 2020, 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 2020 weather data from seven 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 2020, The Jefferson Project had seven weather monitoring stations around the lake collecting data on precipitation, temperature, wind speed, wind direction, barometric pressure, and relative humidiity. These stations are WX_CedarLane, WX_DFWI, WX_GullRock, WX_MossyPoint, WX_WhaleRock, WX_PilotKnob, and WX_Glenburnie. 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.
Densities and cover data for intertidal organisms from an LTREB project in the Gulf of Maine, USA, from 1996 to 2023.
Experimental clearings in macroalgal (Ascophyllum nodosum) stands were made in 1996 to determine if mussel beds and macroalgal stands on protected intertidal shores in New England represent alternative community states. Uncleared control plots and four sizes of circular clearings (1m, 2m, 4m and 8m in diameter), which mimicked ice scour events, were established in A. nodosum stands at 12 sites on Swan’s Island, Maine, USA. The purpose of these datasets is to provide access to data on densities and percentage cover in the 60 experimental plots from 1996 to 2023. Earlier versions of the data prior to 2007 can be found in Ecological Archives ( E087-047 and E089-032). The current EDI version includes corrections of errors in the versions in Ecological Archives. Data include densities of mussels (Mytilus edulis), an herbivorous limpet (Testudinalia testudinalis), herbivorous snails (Littorina littorea, Littorina obtusata), a predatory snail (Nucella lapillus), a barnacle (Semibalanus balanoides), and fucoid algae (Ascophyllum nodosum and Fucus vesiculosus), and percentage cover by mussels, barnacles, fucoids and other sessile organisms. Research was funding by NSF's LTREB program.
State Water Project, Genetic Determination of Population of Origin 2011-2024
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
Central Valley Project, Genetic Determination of Population of Origin 2011-2024
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
California's Central Valley Project Improvement Act Predation Contact Point Study - 2022: Predator-prey interactions under low artificial lighting in a laboratory setting
The highest rates of piscivorous predation in the field have been recorded during crepuscular light levels associated with sunrise and sunset or artificial lighting at night (ALAN). We conducted a laboratory study where groups of predator-naïve, hatchery-raised juvenile rainbow trout (Oncorhynchus mykiss) were exposed to natural-origin piscivorous largemouth bass (Micropterus salmoides) under three light treatments representative of brighter crepuscular periods or direct ALAN illumination (“high” treatment), dimmer crepuscular periods or sky glow from ALAN (“medium” treatment), and night or no ALAN (“low” treatment). We then statistically evaluated potential associations between light treatment, prey group cohesion, and predator activity.
Percent cover of under- and mid-story vegetation and seedling counts in the Future of Oak Forests project at Black Rock Forest, Cornwall, NY.
Black Rock Forest established a series of 12, 0.56 ha plots in 2005 to assess impacts of the loss of tree in the genus Quercus on the forest ecosystem (entitled the Future of Oak Forests experiment). Three trunk girdling treatments, with control plots were instituted in 2008. Each plot also contained an ~10m by ~15m deer exclosure to assess the impact of herbivory post-disturbance. In 2006 and 2008, before exclosures were erected, pre-treatment surveys were conducted in all unexclosed (n=120) quadrats. Surveys of all 240 understory quadrats were conducted annually in late summer (August to September) from 2009 to 2018 and then again in 2021. At each quadrat, trained observers identified all vascular plants to species and assigned each species a percent cover value. The percent cover of moss was also recorded but moss species were not identified. Counts of tree seedlings and some woody shrubs were also recorded in addition to percent cover values. Seedlings were considered saplings, and therefore not counted, once they reached 1.3 m tall (breast height).
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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.