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858 results for “monitoring data”
SoilCare WP5 database with experimental monitoring data
<p>Here the database with the experimental data collected in all SoilCare study sites is included. The SoilCare database: schema (empty database) and Report 34 (D5.1) which reports and explains the database, which the SoilCare project developed and used for storing the monitoring results from the tested cropping systems and/or field agricultural experiments in the 16 Study sites can be accessed in <a href="https://doi.org/10.5281/zenodo.5541295">10.5281/zenodo.5541295</a>. </p> <p> </p> <p> </p>
MEaSUREs blue band total column water vapor sample data for the Ozone Monitoring Instrument
<p>This dataset contains the MEaSUREs OMI Total Column Water Vapor (TCWV) data and their related data used in the paper titled “Development of the MEaSUREs blue band water vapor algorithm – Towards a long-term data record” by Wang et al. (2023). The unzipped archive contains the following three directories. </p> <ol> <li>OMI-H2O-L2/ contains the MEaSUREs Level 2 data (in molecules/cm2) in netCDF4 format for January and July 2005 and 2006. Selected supporting data are also included in each file.</li> <li>OMI-H2O-L3/ contains MRaSUREs Level 3 data (0.25 degree by 0.25 degree, in molecules/cm2) generated using the standard filtering criteria in netCDF4 format for January and July 2005 and 2006. Selected supporting data are also included.</li> <li>Model3_ncresult/ contains netCDF4 formatted files for the MEaSUREs OMI TCWV data (in mm), the AMSR_E TCWV data sampled onto the corresponding OMI pixel locations, and the LightGBM model 3 predictions for the OMI pixels.</li> </ol> <p>The linux command ‘ncdump -h filename’ can be used to examine the contents of netCDF4 files. Due to the current size limit of Zenodo, only a small subset of the MEaSUREs data is archived here. The full dataset will be released elsewhere, e.g., NASA EARTHDATA GES DISC.</p>
Data from: Non-invasive Assessment of Cartilage Damage of the Human Knee using Acoustic Emission Monitoring: a Pilot Cadaver Study
<p>This dataset accompanies the following article: "Non-invasive Assessment of Cartilage Damage of the Human Knee using Acoustic Emission Monitoring: a Pilot Cadaver Study," in <em>IEEE Transactions on Biomedical Engineering</em>, doi: 10.1109/TBME.2023.3263388.</p> <p>Knee acoustic emissions (AE) recorded in the 100-450 kHz and 15-200kHz frequency ranges from a cadaver specimen knee in flexion/extension. Four stages of artificially inflicted cartilage damage and two sensor positions were investigated. </p> <p><em><strong>Stages of artificially inflicted cartilage damage:</strong></em> the cartilage surface damage on the medial compartment, KL III; the cartilage surface damage on the medial compartment plus patellofemoral surface, KL III; the cartilage surface damage on the medial compartment plus on the patellofemoral surface KL IV; the cartilage surface damage on the medial compartment plus on the patellofemoral surface and lateral compartment.</p> <p><strong><em>Sensor positions</em></strong>: medial and lateral knee</p>
US National Native Bee Monitoring RCN Data Management Workshop: Public Domain Videos
<p>The US National Native Bee Monitoring Research Coordination Network (RCN) held a two-day workshop on data management best practices for native bee inventory, survey, and monitoring data on March 28 and 30, 2023. Videos in this data set were played at the workshop. These videos are released into the public domain. This data set includes the following videos:</p> <ul> <li>Ecological Metadata Standards to Enable Data Reuse by Julien Brun</li> <li>Useful Photo Management for Bee Species by Sam Droege</li> <li>Trait Data Models and Vocabulary by Jen Hammock</li> <li>Symbiota: open-source community portals for insect data management by Andrew Johnston</li> <li>Moving data from the field to the world by Jonathan Koch</li> <li>Exploring data using Discover Life by Clare Maffei</li> <li>USDA Data Sharing Policies and Opportunities by Cynthia Sims Parr</li> <li>Why Share Species Interaction Data? by Jorrit H. Poelen</li> <li>Big-Bee: Sharing Bee Interactions & Traits by Katja C. Seltmann</li> <li>Let’s talk about data by Katja C. Seltmann</li> <li>Responsible use of museum specimens & their data by Erika M. Tucker</li> </ul>
US National Native Bee Monitoring RCN Data Management Workshop: CC BY Videos
<p>The US National Native Bee Monitoring Research Coordination Network (RCN) held a two-day workshop on data management best practices for native bee inventory, survey, and monitoring data on March 28 and 30, 2023. Videos in this data set were played at the workshop. These videos are released with a CC BY license. Please cite the presenter(s) of the video(s) you use. This data set includes the following videos:</p> <ul> <li>The ABeeCs of Data Attribution: Please use your magic words by David Bloom</li> <li>Darwin Core Geography: How to make your locality data complete and accurate by David Bloom</li> <li>Best Practices for Managing Native Bee Molecular Data by Michael G. Branstetter</li> <li>A Trait Database for Bees by Elizabeth A. Crisfield</li> <li>Biotic interaction data and invasive species assessment by Quentin Groom</li> <li>OpenTraits Network (OTN) & TRY Plant Trait Database by Jens Kattge</li> <li>Semantics modeling of phenotypic trait data with ontologies by Diego S. Porto</li> <li>WorldFAIR: towards making plant-pollinator data FAIR by Maarten Trekels</li> </ul>
Data from: Trends in butterfly populations in UK gardens – new evidence from citizen science monitoring
<p>This data package describes the annual abundance indices and trend estimates for 22 butterfly species in UK gardens for the period 2007-2020.</p> <p>These data form the basis of the results presented in: Plummer, K.E., Dadam, D., Brereton, T., Dennis, E.B., Massimino, D., Risely, K. et al. (2023) Trends in butterfly populations in UK gardens—New evidence from citizen science monitoring. <em>Insect Conservation and Diversity</em>, 1– 13. Available from: <a href="https://doi.org/10.1111/icad.12645">https://doi.org/10.1111/icad.12645</a></p> <p>Please refer to the paper for an explanation of the underlying BTO Garden BirdWatch (GBW) data and modelling protocols used to produce the datasets included here.</p> <p>We would also greatly appreciate if you could fill out <a href="https://forms.gle/DCc58VXpdmqnTmTk8" target="_blank" rel="noopener">this very short form</a> to tell us how you intend to use these data. Thanks in advance!</p>
Dataset: all TROPOMI detected plumes for 2021. [Schuit et al. 2023: Automated detection and monitoring of methane super-emitters using satellite data]
<p>Dataset of all TROPOMI detected methane plumes in 2021, including estimates for the source location, emission quantification and source type. Corresponds to Figure 6 of Schuit et al. 2023 [Automated detection and monitoring of methane super-emitters using satellite data, https://doi.org/10.5194/acp-23-9071-2023]. Additional details and context are provided in Section 3 of the paper.</p> <p> </p> <p><em>Contents and data formats</em></p> <p><strong>date</strong>, date of the TROPOMI observation. format: YYYYMMDD</p> <p><strong>time_UTC</strong>, time of the TROPOMI observation in UTC. format: HH:MM:SS</p> <p><strong>lat</strong>, latitude of the center of the TROPOMI pixel at the estimated source location. format: float</p> <p><strong>lon</strong>, longitude of the center of the TROPOMI pixel at the estimated source location. format: float</p> <p><strong>source_rate_t/h</strong>, estimated emission source rate in tonnes per hour, the methodology is described in Section 2.5.1 of the paper. format: int</p> <p><strong>uncertainty_t/h</strong>, the uncertainty of the emission source rate in tonnes per hour, the methodology is described in Section 2.5.1 of the paper. format: int</p> <p><strong>estimated_source_type</strong>, the locally dominant anthropogenic source sector based on bottom-up inventories, the methodology is described in Section 2.5.3 of the paper. format: str</p> <p> </p> <p>Full citation of the paper:</p> <p>Schuit, B. J., Maasakkers, J. D., Bijl, P., Mahapatra, G., van den Berg, A.-W., Pandey, S., Lorente, A., Borsdorff, T., Houweling, S., Varon, D. J., McKeever, J., Jervis, D., Girard, M., Irakulis-Loitxate, I., Gorroño, J., Guanter, L., Cusworth, D. H., and Aben, I.: Automated detection and monitoring of methane super-emitters using satellite data, Atmos. Chem. Phys., 23, 9071–9098, https://doi.org/10.5194/acp-23-9071-2023, 2023.</p>
Björkö Wind Turbine Version 1 (45kW) high frequency Structural Health Monitoring (SHM) data
<p>The Chalmers wind turbine has variable speed operation with a direct driven generator and a frequency converter, it also has a digital control system developed by Chalmers. The wind turbine has a rated power of 45 kW and rated speed of 75 rpm. The wooden tower is 30 m high, the blades of carbon fibres are 7.5 m long, and the turbine diameter is 15.9 m. The individually blade pitch system is electrical. The turbine is situated on the island Björkö at Skarviksvägen, 20 km west of Göteborg city. The coordinates are: 57.71818820625921, 11.683382148764485.</p> <p><br> 69 SCADA and structural vibration and loads Channels timeseries (sampled at 20 and 100 Hz) such as nacelle accelerations, tower and blades bending moments are included.</p> <p><br> Structured metadata about wind turbine characteristics, SCADA, vibration and loads channels are included as JSON files and CSV.</p> <p>This particular dataset consisting of high frequency sampled data, is intended for condition and structural health analysis.</p> <p><strong>The data covers:</strong></p> <ul> <li>the measurements sampled at 100 Hz correspond to the period from 05 July 2022 to 9 June 2023</li> <li>the measurements sampled at 20 Hz correspond to the period from 05 July 2022 to 2 August 2023</li> </ul> <p><strong>This repository includes:</strong></p> <p><strong>Time-series data in csv format:</strong></p> <ul> <li>B1_CL4_20.csv (this is the data sampled at 20 Hz)</li> <li>B1_CL4_100.csv (this is the data sampled at 100 Hz)</li> </ul> <p><strong>Metadata:</strong></p> <ul> <li>Bjorko_Sensors_Specs_Metadata.csv (Sensors signals specification in csv format)</li> <li>Bjorko_modes_mapping.csv (numerical integer value representing the wind turbine controller system mode in csv format)</li> <li>Bjorko_modes_mapping.json (numerical integer value representing the wind turbine controller system mode in csv JSON format)</li> <li>Bjorko_digital_io_states_mappings.csv (Description of digital input and output states in the wind turbine controller system in csv format)</li> </ul> <p><strong>Media:</strong></p> <ul> <li>Chalmers-Wind turbine.pdf (description of the wind turbine including pictures)</li> <li>Chalmers wind turbine description 220121-short.pdf (description of the wind turbine including pictures)</li> </ul> <p><strong>Semantic artifacts:</strong></p> <ul> <li>N/A</li> </ul> <p><strong>Other:</strong></p> <ul> <li>N/A</li> </ul> <p>Additional information is available upon request.</p>
Aventa AV-7 ETH Zurich Research Wind Turbine SCADA and high frequency Structural Health Monitoring (SHM) data
<p><strong>General description of wind turbine: </strong>The ETH owned wind turbine is Aventa AV-7, manufactured by Aventa AG in Switzerland and was commissioned in December 2002. The turbine is operated via a belt-driven generator and a frequency converter with a variable speed drive. The rated power of the Aventa AV-7 is 7 kW, beginning production at a wind speed of 2 m/s and having a cut-off speed of 14 m/s. The rotor diameter is 12.8 m with 3 rotor blades, and a hub height is 18m. The maximum rotational speed of the turbine is 63 rpm. The tower is a tubular steel-reinforced concrete structure, supported on concrete foundation, while the blades are made of glassfiber with a tubular steel main-spar. The turbine is regulated via a variable-speed and variable pitch control system.</p> <p><strong>Location of site: </strong>The wind turbine is located in Taggenberg, about 5 km from the city centre of Winterthur, Switzerland. This site is easily accessible by public transport and on foot with direct road access right next to the turbine. This prime location reduces the cost of site visits and allows for frequent personal monitoring of the site when test equipment is installed. The coordinates of the site are: 47°31'12.2"N 8°40'55.7"E.</p> <p><strong>Control and measurement systems and signals: </strong>The turbine is regulated via a variable-speed and collective variable pitch control system.</p> <p><strong>SHM Motivation: </strong>Designed and commissioned in 2002, the Aventa wind turbine in Winterthur is soon reaching its end of design lifetime. In order to assess the various techniques of predicting the remaining useful lifetime, a Structural Health Monitoring (SHM) campaign was implemented by ETH Zurich. The monitoring campaign started in 2020, and is still ongoing. In addition, the setup is used as a research platform on topics such as system identification, operational modal analysis, faults/damage detection and classification. We analyze the influence of operational and environmental conditions on the modal parameters and to further infer Performance Indicators (PIs) for assessing structural behavior in terms of deterioration processes.</p> <p><strong>Data Description: </strong>The tower and nacelle have been instrumented with 11 accelerometers distributed along the length of the tower, nacelle main frame, main bearing and generator. Two full bridge strain gauges are installed on the concrete tower based measuring fore-aft and side-side strain (and can be converted to bending moments) – all acceleration and strain signals sampled at 200Hz. Temperature and humidity are measured at the tower base – 1Hz data. In additional we are collecting operational performance data (SCADA), namely: wind speed, nacelle yaw orientation, rotor RPM, power output and turbine status – SCADA signals are sampled at 10Hz. See appendix for further details of the sensors layout.</p> <p>The measurements/instrumentation setup, type and layout is provided in the pdf files.</p> <p><strong>The data:</strong> the data is provided in zip files corresponding to four use-cases as follows:</p> <ul> <li>Normal operation data for system identification</li> <li>Aerodynamic imbalance on one blade</li> <li>Rotor icing event</li> <li>Failure of the flexible coupling of the linear drive of the collective pitch system</li> </ul> <p>The data for each of the four uses-cases is organized in zip files. The content of each zip file is as follows:</p> <ul> <li>Time-series data in HDF5 format</li> <li>Metadata: <ul> <li>Turbine specification (Aventa-AV-7.json and Aventa-AV-7.yaml)</li> <li>Sensor specification (Aventa_sensors.json )</li> <li>Unstructured description of the Aventa Turbine and the installed sensors (Aventa_Sensors_Specs.xlsx)</li> </ul> </li> <li>Semantic artifacts: <ul> <li>WindIO Wind Turbine YAML schema describing turbine specifications (IEAontology_schema.yaml)</li> <li>Sensor specification JSON schema (sensors_schema.json)</li> </ul> </li> <li>Media: Pictures of leading edge roughness and a clip of wind turbine operation</li> <li>Code: Jupyter notebook containing example code to load metadata from JSON and data from HDF5 files (example.ipynb)</li> </ul> <p>Additional data is available upon request, please contact:</p> <ul> <li>Prof. Dr. Eleni Chatzi (chatzi@ibk.baug.ethz.ch)</li> <li>Dr. Imad Abdallah (ai@rtdt.ai , abdallah@ibk.baug.ethz.ch)</li> </ul> <p>For further details or questions, please contact:</p> <p>Prof. Dr. Eleni Chatzi<br> Chair of Structural Mechanics & Monitoring</p> <p>ETH Zürich<br> <a href="http://www.chatzi.ibk.ethz.ch/">http://www.chatzi.ibk.ethz.ch/</a></p>
Covid-19 Vaccine Monitoring project (CVM)-Electronic Health Record data sources Codelist
<p>This is the code list that was used to identify outcomes and covariates (those tagged as in narrow) in electronic health records of participating data sources in the the CVM study which was addressing the following questions</p> <p> </p> <p>1)<strong> To create and assess readiness of electronic health record data sources for rapid evaluation of safety signals by </strong></p> <ul> <li> <p>Providing an overview of the methods for identification of COVID-19 vaccine exposure in the data sources </p> </li> <li> <p>Monitoring the number of individuals exposed to any COVID-19 vaccine and to compare this to COVID-19 vaccine exposure (benchmark: ECDC vaccine tracker)1 </p> </li> <li> <p>Generation of updated background rates for AESIs </p> </li> </ul> <p><strong>2) To conduct rapid safety assessment studies using electronic healthcare records and support EMA safety assessments. </strong></p> <p>The protocol for this study is publicly available www.encepp.eu/encepp/viewResource.htm?id=42637. The report with results using the code list is publicly available on Zenodo as well. </p> <p> </p> <p> </p> <p> </p>
Processed metabolomic data from the EXPOsOMICS Personal Exposure Monitoring study
<p>Metabolomic data from the 'Variability of the Human Serum Metabolome over 3 Months in the EXPOsOMICS Personal Exposure Monitoring Study' paper <a href="https://doi.org/10.1021/acs.est.3c03233">DOI: 10.1021/acs.est.3c03233</a> . </p> <p>The data was originally collected and generated by the multicenter EXPOsOMICS Personal Exposure Monitoring study. Details on data collection and processing are described in the aforementioned paper. The statistical analysis from that paper is available at <a href="https://github.com/moosterwegel/variability-metabolites-paper">https://github.com/moosterwegel/variability-metabolites-paper</a> and may contain useful information/code to work with this data.</p> <p>`processed_covariate_data.csv`:<br> ```<br> Rows: 298<br> Columns: 7<br> $ subjectid: hashed identifier subject<br> $ sample_code: indicates if it's the first (A) or second (B) blood sample<br> $ centre: indicates in which centre the data was collected<br> $ age_cat: indicates age category at the time of a PEM session<br> $ sq_sex: indicates the sex of the participant (male, female) as filled in during the screening questionaire<br> $ traf: indicates the exposure to traffic (PM2.5 and UFP) as measured during the PEM sessions. <br> $ bmi_cat: indicates BMI category at the time of a PEM session<br> ```</p> <p>`processed_lcms_data data.csv` contains the processed LCMS data:<br> ```<br> Rows: 298<br> Columns: 4297<br> $ subjectid: hashed identifier subject<br> $ sample_code: indicates if it's the first (A) or second (B) blood sample<br> $ centre: indicates in which centre the data was collected<br> $ compounds: measured features (compounds) are prefixed by the letter X. The name contains information on the measured monoisotopicmass_retentiontime.<br> Non-detects (below limit of detection (LOD) are coded as 1 for the compounds.<br> ....<br> ```<br> In the datasets each row indicates a measurement on a day (`sample_code`) and person (`subjectid`). The datasets can be joined on these variables.</p> <p>The other data files (`annotations.xslx`, `ancestors_annotations.xlsx`, `annotations_plus_kegg_pathways.csv`) contain the annotations, ancestors of the annotations (to assign a class to a compound based on ChEBI ontology, see our paper for details), annotations plus KEGG pathways respectively. </p>
Ground surface temperature data 2007-2021 at different sites of the PERMATHERMAL monitoring network in Livingston and Deception Islands, SouthShetland Archipelago, Antarctica.
<p>Ground Surface Temperature (GST) corrected data adquired between 2007 and 2021 at different stations of the PERMATHERMAL monitoring network at Livingston and Deception Islands, South Shetland Archipelago, Antarctica.</p> <p>(To be completed)</p>
Petit-lac-Saint-François surface water quality monitoring data
Water Quality samples were collected at Lake Inlet, Outlet, and In-Lake sites between October 2009 and September 2020. Water Samples were collected on a weekly, same-day-of-the-week basis, between 10 a.m. and noon, year-round. Field sampling was conducted by the same technician during the entire period to ensure method consistency and sampling frequency was maintained throughout, except for the winter of 2017, during which sampling was suspended. Epilimnion samples were collected using a swing sampler with a wide neck, polyethylene bottle (Nasco Sampling, Madison, WI, USA), and composited in an acid-washed, opaque, 4-L polyethylene bottle pre-conditioned with lake water. Four grab samples were taken to account for spatial heterogeneity that can be substantial within distances of a few meters, particularly when surface phytoplankton blooms are present. The grab samples were collected by tilting the swing sampler bottle approximately 45 degrees and allowing it to fill as it was submerged 8 to 15 cm below the surface. During periods of ice cover, access holes were drilled or cut through the ice to collect samples. For dissolved parameters, samples were filtered on the same day as collection. Quality control measures, including field, transport, and laboratory blanks, consisting of HPLC grade water and preservative where required, as well as split samples for interlaboratory comparisons, were included quarterly in the sampling program along with regular samples. These were used to establish practicable detection limits and to monitor for levels of contaminants to which field samples might be exposed.
FISHPASS ASSESSMENT PLAN LONG-TERM MONITORING OF HYDROLOGIC AND WATER QUALITY DATA
The Great Lakes Fishery Commissions’ (GLFC) FishPass project seeks to reconnect the waterscape for only desired species (i.e., selective passage) by integrating a multitude of existing and novel passage techniques and technologies. The probability of a fish passing through a sorting system is dependent on environmental conditions and a fish’s motivation ─ its internal state in relation to environmental stimuli. While fish decision making abilities introduce complexity to the sorting operations, they also provide an opportunity to exploit behavioral tendencies and abilities to achieve selective sorting. The FishPass Assessment Plan details a monitoring program aimed at quantifying fish movement and sorting capabilities associated with both individual mechanisms and integrated sorting systems. The results of the monitoring program will be used to inform future adjustments to the selection of techniques and technologies and their configuration to optimize passage of desirable species while blocking and/or removing undesirable species. A key component to the Assessment Plan is the long-term monitoring of abiotic variables in and around FishPass. This data set contains the hydrologic (e.g., river discharge, water level) and water quality data (e.g., temperature, specific conductivity, conductivity, and turbidity) collected at mostly static stations throughout the Boardman/Ottaway River. The dataset is updated annually. These data are collected until the initiation and/or substantial completion of the FishPass structure. Collection of this type of data are expected to continue after FishPass construction completion but modifications to the extent and location of monitoring stations are anticipated. As a result, a new dataset will be updated in the future containing all long term hydrologic and water quality monitoring post construction. R. Swanson, GLFC Assessment Biologist, is primarily responsible for maintaining the monitoring equipment, data retrieval, quality assuran
Lake ice surveys, 1874-2022, Adirondack Long-Term Ecological Monitoring Program Project No. 8 by Adirondack Ecological Center of the State University of New York College of Environmental Science and Forestry, Newcomb, New York. Environmental Data Initiative.
The objective of this dataset is to document ice-in and ice-out dates on several lakes on the State University of New York College of Environmental Science and Forestry's Huntington Wildlife Forest (HWF). Lakes include: Arbutus, Catlin, Deer, Military, Rich, Wolf and Lodo Pond; some records exist for Long Pond and other water bodies but they are not included here except in some comment fields.
DNR Citizen Monitoring Lake Level Data from 2010 to 2015 in Wisconsin
The lake level data from the DNR citizen monitoring project. This ongoing project has been collecting lake level data from 20 lakes in 11 counties in Wisconsin since 2010. It features the citizen voluntary action in the monitoring work. The data are hosted by Surface Water Integrated Monitoring System so its short name in this dataset is SWIMS.
Photosynthetically Active Radiation data taken with the Delta-T SunScan wand every 15 cm of 1m x 1m chamber flux and point frame plots as well as four remotely monitored canopies at the Toolik Field Station in AK, Summer 2012.
Within-canopy PAR was measured with a Delta-T SunScan wand every 15 cm from the ground to above the canopy under both direct and diffuse light. The data includes all outputs from the SunScan wand: time of measurement, spread of PAR sensors, total irradiance, total diffuse light, and individual outputs of 64-PAR sensors on the SunScan wand. These measurements were taken for 1m x 1m chamber flux (n=14) and point frame (n=19) plots as well as sites four montitored remotely by PAR sensors located above, within, and below shrub canopies. All plots were dominated either by tall Salix pulchra and Betula nana species and were located near the LTER shrub plots at Toolik Field Station , AK in the summer of 2012.
Monitoring permafrost thaw in Denali National Park and Preserve: year one of data collection 2013
Transects were established at six sites in the Toklat river basin, Denali National Park and Preserve. This was the fist year of what will be a long-term monitoring project of permafrost and ecosystem characteristics. Transects were co-located with known thaw subsidence (thermokarst) features and run from outside (relatively undisturbed tundra) through the feature.
Throw trap and electrofishing data collected during 1996–2022 from the Everglades, Florida, United States for the publication "Contrasting invasion histories and effects of three non-native fishes observed with long-term monitoring data"
This dataset was used to analyze the effects of three non-native fishes in the Florida Everglades for a publication in the journal Biological Invasions. The dataset incorporates plot-level mean densities (# of individuals per square meter) of common aquatic animals collected during 1996–2022 from 17 sites across three regions of the Everglades: Taylor Slough, Shark River Slough, and Water Conservation Area 3A. Prey species included are nine common small fishes and three common decapod species (two crayfish species and grass shrimp). The dataset includes throw trap data on three predator taxa: African Jewelfish (Hemichromis letourneuxi), Mayan Cichlids (Mayaheros uruphthalmus), and sunfishes (Lepomis spp.). Annual indices of mean wet season electrofishing catch-per-unit-effort of Asian Swamp Eels (Monopterus albus/javanesis), Mayan Cichlids, sunfishes, and the three other large 'top predator' fishes (Amia calva, Lepisosteus platyrhincus, Micropterus salmoides) are included for plots where electrofishing was performed from 1997-2021. Hydrologic measures used in analyses and R code used to conduct analyses are also included.
Optimizing sampling across methods improves the power of ecological monitoring data
Transect-based monitoring has long been a valuable tool in ecosystem monitoring. These transects are often used to measure multiple ecosystem attributes. The line-point intercept (LPI), vegetation height, and canopy gap intercept methods comprise a set of core methods, which provide indicators of ecosystem condition. However, users struggle to design a sampling strategy that optimizes the ability to detect ecological change using transect-based methods. We assessed the sensitivity of these core methods on a one-hectare plot to transect length, number, and sampling interval to determine: 1) minimum sampling required to describe ecosystem characteristics and detect change for each method and 2) optimal transect length and number for all three methods to make recommendations for future analyses and monitoring efforts. We used data from 13 National Wind Erosion Research Network locations spanning the western US, which included 151 measurements over time across five biomes. We found that longer and increased numbers of transects were more important for reducing sampling error than increased sample intensity along transects. For all methods and indicators across plots, three 100-m transects reduced sampling error so that indicator estimates fall within an 95% confidence interval of +/- 5% for canopy gap intercept and LPI-total foliar cover, +/- 5 cm for height and +/- two species for LPI-species counts. For the same criteria at 80% confidence intervals, two 100-m transects are needed. Site-scale inference was strongly affected by sample design, consequently our understanding of ecological dynamics may be influenced by sampling decisions.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.