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13,146 results for “Integration”
Interagency Ecological Program: Integrated Dataset of Phytoplankton Enumeration Data in the San Francisco Estuary, 1992-2024
Phytoplankton community composition is an important driver of zooplankton productivity and food supply for higher trophic levels in the San Francisco Estuary. Various monitoring surveys throughout the region collect phytoplankton enumeration data dating back to the 1990s. These include surveys from the CA Department of Water Resources (CADWR), CA Department of Fish and Wildlife (CDFW), the US Bureau of Reclamation (USBR), and the US Geological Survey (USGS). These surveys collect data via various sampling and laboratory methods which are not always directly comparable. This integrated dataset includes both enumeration counts and well-documented metadata to allow for informed decision-making in the integration of these data. It also standardizes taxonomic names between groups via a key list. Note that, in this dataset, we make conservative decisions about taxonomic resolution to ensure maximum compatibility between groups. For more detailed metadata and higher taxonomic resolution, refer to individual surveys’ publications or reach out to their primary contact.
Santa Barbara Channel Marine BON: Nearshore kelp forest integrated benthic cover, 1980-ongoing
The Santa Barbara Channel Marine Biodiversity Observation Network (SBCMBON) tracks long-term patterns in species abundance and diversity. This dataset contains cover of kelp forest sessile invertebrates, understory macroalgae, and substrate types by integrating data from four contributing projects working in the kelp forests of the Santa Barbara Channel, USA. Divers collect data on using either uniform point contact (UPC) or random point contact (RPC) methods. The four contributing projects are two research projects: The Santa Barbara Coastal LTER (SBC LTER) and the Partnership for Interdisciplinary Studies of Coastal Oceans (PISCO), the kelp forest monitoring program of the Santa Barbara Channel National Park, and the San Nicolas Island monitoring program supported by USGS. Together, these projects have recorded data for more than 200 species at approximately 100 sites on both the mainland coast and on the Santa Barbara Channel Islands. Sampling began in 1982 and is ongoing. Data were collected by human observation (divers using SCUBA) during regular surveys. Percent cover is recorded for taxa where individuals cannot be counted. Cover can be calculated from the data here as the fraction of total points at which the taxon was present x 100. With UPC and RPC methods, multiple species can be recorded at any given point. The total percent cover of all species combined using this method can exceed 100%; however, the percent cover of any single species cannot exceed 100%. See Methods for information on integration and data processing. MBON is funded by National Aeronautics and Space Administration (NASA), Bureau of Ocean Energy Management (BOEM), and National Oceanic and Atmospheric Administration (NOAA). For users who are interested in using all or part of this integrated datasets, please contact data owners to discuss your research interests, data-related issues or any other questions. A recommended citation for the data package is available from the download page. In
Santa Barbara Channel Marine BON: Nearshore kelp forest integrated fish, 1981-ongoing
The Santa Barbara Channel Marine Biodiversity Observation Network (SBCMBON) tracks long-term patterns in species abundance and diversity. This dataset contains counts of fish (including cryptic fish, which are deliberately sought out) produced by integrating data from four contributing projects working in the kelp forests of the Santa Barbara Channel, USA. The four contributing projects are two research projects, the Santa Barbara Coastal LTER (SBC LTER) and the Partnership for Interdisciplinary Studies of Coastal Oceans (PISCO), and the kelp forest monitoring program of the Santa Barbara Channel National Park, and the San Nicolas Island monitoring program supported by USGS. Together, these projects have recorded data for more than 200 species at approximately 100 sites on both the mainland coast and on the Santa Barbara Channel Islands. Sampling began in 1980 and is ongoing. Data were collected by human observation (divers using SCUBA) during regular surveys. This dataset includes five entities, three data tables and two R scripts. The main data table contains counts of organisms, the bottom-area and height of the water column over which the fish were surveyed. The column labeled “count” records the number of organisms found in each plot/transect at a given timestamp. A second data table contains place names and geolocation for sampling sites. Information is sufficient for the calculation of fish density, which is left to the user. The third data table contains the depths of each transect the for the fish survey. Sample R script is included to illustrate generation of a basic table of areal density by taxa and sampling site. A second R script is included to convert these data from their primary MBON structure to a Darwin Core Archive (and available through multiple sources). MBON is funded by National Aeronautics and Space Administration (NASA), Bureau of Ocean Energy Management (BOEM), and National Oceanic and Atmospheric Administration (NOAA). For users who are i
Interagency Ecological Program: Zooplankton abundance in the Upper San Francisco Estuary from 1972-2021, an integration of 7 long-term monitoring programs
The upper San Francisco Estuary is an inland inverse delta formed by the confluence of 5 major rivers that drain 40% of the land in California (USA). It is a central hub of water delivery in California and home to a number of commercially important and endangered fish, such as Chinook Salmon, Green Sturgeon, and Delta Smelt. To monitor the environmental impacts of water exports from this system, extensive ecological monitoring has been conducted since the 1960s. To track lower trophic levels, zooplankton abundance has been monitored from 1972 to present. Starting with just one survey (the CA Department of Water Resources’ [CDWR] and California Department of Fish and Wildlife’s [CDFW] Environmental Monitoring Program) in 1972, the suite of zooplankton surveys gradually expanded with time. Several surveys traditionally focused on monitoring fish abundance added zooplankton nets to their sampling programs. The CDFW 20-mm larval fish survey added zooplankton sampling in 1995, the CDWR Yolo Bypass Fish Monitoring Program add zooplankton in 1999, the CDFW Summer Townet Survey added zooplankton in 2005, and the Fall Midwater Trawl added zooplankton in 2007. Two new sampling programs, the Fish Restoration Program and Directed Outflow Project, began in 2015 and 2017, respectively. All sampling programs continue today. Each survey samples once or twice monthly at set of fixed or random stations that varies across surveys depending on their objectives. While the Environmental Monitoring Program samples year-round, the other surveys are mostly seasonal, although additional months were sampled in some years. Most surveys target open channels although the Fish Restoration Program samples in or near shallow tidal wetlands. Three size classes of zooplankton are targeted by these sampling programs with different net mesh sizes: micro zooplankton (copepods and rotifers) are targeted with a 43 µm mesh net, meso zooplankton (copepods and cladocerans) are targeted with 150 - 160 µm mesh
Santa Barbara Channel Marine BON: Nearshore kelp forest integrated quad and swath survey, 1980-ongoing
The Santa Barbara Channel Marine Biodiversity Observation Network (SBCMBON) tracks long-term patterns in species abundance and diversity. This dataset contains counts of algae and invertebrates (both sessile and mobile) by integrating data from four contributing projects working in the kelp forests of the Santa Barbara Channel, USA. The four contributing projects are two research projects: The Santa Barbara Coastal LTER (SBC LTER) and the Partnership for Interdisciplinary Studies of Coastal Oceans (PISCO), the kelp forest monitoring program of the Santa Barbara Channel National Park, and the San Nicolas Island monitoring program supported by USGS. Together, these projects have recorded data for more than 200 species at approximately 100 sites on both the mainland coast and on the Santa Barbara Channel Islands. Sampling began in 1982 and is ongoing. Data were collected by human observation (divers using SCUBA) during regular surveys. The data table documents the number of organisms and the area over which that number was counted for calculation of areal abundance. Data were collected by human observation (divers using SCUBA) during regular surveys. The algae and invertebrate counts record the number of taxa found in each plot, including quad (small square plots such as 1 or 2 m2) and swath (large linear plots such as 60 m2). See Method and protocol for information on integration and data processing. MBON is funded by National Aeronautics and Space Administration (NASA), Bureau of Ocean Energy Management (BOEM), and National Oceanic and Atmospheric Administration (NOAA). For users who are interested in using all or part of this integrated datasets, please contact data owners to discuss your research interests, data-related issues or any other questions. A recommended citation for the data package is available from the download page. In addition, any manuscript generated using this dataset is expected to be sent to the data owners before publication so we can be sure t
Santa Barbara Channel Marine BON: Nearshore kelp forest integrated taxa, 1980-ongoing
The Santa Barbara Channel Marine Biodiversity Network (SBC MBON) tracks long-term patterns in species abundance and diversity. By integrating research and monitoring efforts (both existing and de novo data), we provide a comprehensive view of biodiversity in the region. This dataset is a combined species list from four datasets currently handled and integrated by SBC MBON, with identifiers from an appropriate taxonomic registry included for each taxon. Because this species list reflects the integration of multiple collections, it will be updated as SBC MBON integration efforts develop. Santa Barbara Channel Marine BON: Integrated kelp forest/reef: Fish https://portal.edirepository.org/nis/mapbrowse?scope=edi&identifier=5&revision=newest Santa Barbara Channel Marine BON: Integrated kelp forest/reef: Quad and swath cover https://portal.edirepository.org/nis/mapbrowse?scope=edi&identifier=6&revision=newest Santa Barbara Channel Marine BON: Integrated kelp forest/reef: Benthic cover https://portal.edirepository.org/nis/mapbrowse?scope=edi&identifier=3&revision=newest As of 2018, this dataset contains approximately 400 taxa from combined observations beginning in 1980. Data include codes for the originating project and sampling method, plus basic taxonomic lineage. MBON is funded by National Aeronautics and Space Administration (NASA), Bureau of Ocean Energy Management (BOEM), and National Oceanic and Atmospheric Administration (NOAA). For users who are interested in using all or part of this integrated datasets, please contact data owners to discuss your research interests, data-related issues or any other questions. A recommended citation for the data package is available from the download page. In addition, any manuscript generated using this dataset is expected to be sent to the data owners before publication so we can be sure the data is used in the proper context and methods are reported accurately: Santa Barbara Coastal LTER (LTER): Dan Ree
Integrated Topography and Bathymetry for the Eastern Shore of Virginia
Description This dataset integrates elevation and bathymetry data from multiple sources into a single mostly-seamless digital elevation model (DEM) that covers the entire Eastern Shore of Virginia and its surrounding coastal waters. Data sources include airborn LiDAR, VCRLTER and ODU bathymetric surveys, NOAA navigational data, NOS oceanographic surveys, USGS NED data plus contours and features from topo quad maps, and VGIN-VBMP aerial imagery. Proposed uses for the dataset include deriving detailed watershed boundaries, hypsometric curves, tidal prisms, 3-D physical models, and input for numerical hydrodynamic simulation models. The version 2.0 DEM (August 2014) is suitable for both local- and regional-scale analyses. It has a resolution matched to the LiDAR data (3.048 m. or 10 ft.), resulting in 31,152 x 46,002 cells (5.4 Gigabyte in ESRI GRID format). Users can aggregate to coarser cell resolutions as needed. The data is projected in UTM Zone 18 North coordinates relative to the WGS84 horizontal datum. All elevations are in meters relative to the NAVD88 vertical datum. Spatially modelled conversion factors between NAVD88 and local sea level datums (MSL, MLW, MHW, etc.) based on NOAA VDATUM data are available as a separate dataset (VCR13215). See "METHODS" for full details on data sources and integration methodologies.
Water Quality Sampling - integrated measurements for the Virginia Coast, 1992-2025
This dataset contains information about the aquatic environment along two transects that run from inlet to the mainland shore on the southern part of the Delmarva Peninsula since 1992. It has a large number of columns (111) that integrate information on water column and benthic measurements. Frequency of sampling varies from monthly to quarterly, and not all variables are necessarily measured on the same dates. However, all the data from a given date and location appears on a single line of the dataset.
Integration of sweet taste and metabolism determines carbohydrate reward-study 3
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A multi-modal human neuroimaging dataset for data integration: simultaneous EEG and fMRI acquisition during a motor imagery neurofeedback task: XP1
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JasonAlongTrack: A reformatted version of the Integrated Multi-Mission Ocean Altimeter Data for Climate Research Version 5.1
<p>JasonAlongTrack contains geo-registered along-track sea surface height anomalies with respect to the DTU15 mean sea surface at 1-second intervals from Jason-class altimeters, reformatted for convenience into a 3D array with dimensions of along-track direction by geographically sorted track number by cycle.</p><p>This is a reformatted version of Beckley et al.'s <i>Integrated Multi-Mission Ocean Altimeter Data for Climate Research complete time series Version 5.1</i> dataset, available from <a href="https://podaac.jpl.nasa.gov/dataset/MERGED_TP_J1_OSTM_OST_ALL_V51">https://podaac.jpl.nasa.gov/dataset/MERGED_TP_J1_OSTM_OST_ALL_V51</a>. </p><p>The changes are as follows. Altimeter passes are sorted according to their initial longitude, then split into descending and ascending potions with all descending tracks preceding all ascending tracks. Descending tracks are then flipped so that latitude increases in the alongtrack direction for all tracks. This leads to a 3373 x 254 matrix of observational locations, with the first dimension being the along-track location and the second dimension being the track index. Sea surface height anomaly, time, and flag values are then placed into their correct locations within this matrix, such that these three variables are all of size 3373 x 254 x K where K is the number of cycles, currently 1087. A very good approximation to the time at each of the 3373 x 254 x K observation points is constructed with a length K array of cycles times together with a 3373 x 254 array of time offsets. A median-based editing criterion in introduced to identify a small number of suspect data points. These are set to a value of NaN in sla, but their positions and values are recorded in rejected_index and rejected_values, respectively. The DTU15 mean dynamic topography (mdt) is included, in addition to the mean sea surface field already provided, interpolated onto the track locations using bicubic interpolation. Finally, an estimate of the small-scale noise level, sigma, is produced using a wavelet transform filter.</p>
The International Soundscape Database: An integrated multimedia database of urban soundscape surveys -- questionnaires with acoustical and contextual information
<h1>Introduction</h1> <p>The International Soundscape Database contains the results of a series of soundscape assessment campaigns carried out across Europe and China. The data collection process was conducted according to the <a href="https://www.mdpi.com/2076-3417/10/7/2397">SSID Protocol [1]</a> which integrates in situ questionnaires about users' soundscape experience, with binaural recordings, sound level meter readings, and 360 degree video. The core of this database are individual soundscape questionnaires collected for 3,500+ participants completed in situ in cities across Europe and China, and the psychoacoustic analysis of 30s binaural recordings which can be matched up to each questionnaire.</p> <p>The SSID Protocol was based on the ISO 12913 standard for soundscape data collection [2]. For more information on the specifics of how this data is collected, please see [1].</p> <p>It is the intention that this dataset be added to and augmented with new locations, cities, and contexts in the future. This will be done both by the SSID team at University College London, but we also strongly welcome contributions from other researchers and practicioners. If a soundscape assessment is collected according to the SSID Protocol, it can be integrated with the rest of the database to form a large, cohesive, and ever-growing database of soundscape assessments. </p> <h2>Analysis</h2> <p>Code for exploring and analysing this dataset is included as part of the <a href="https://soundscapy.readthedocs.io/en/latest/">Soundscapy package</a>.</p> <h2>Included Files</h2> <p>This dataset incorporates surveys taken in multiple urban public spaces across several cities in Europe and China. These urban spaces include places like parks, urban squares, green spaces, and market streets. At each location, up to 100 questionnaires were collected over a series of multi-hour long sessions. Therefore the data is organised by LocationID, then SessionID, then GroupID.</p> <p>The basic directory structure and contents can be found below. </p> <h3>Survey Data (.csv)</h3> <p>'ISD v1.0 Data.csv' organises the data according to the labels given above.</p> <h3>Survey Metadata (.xlsx)</h3> <p>In addition a metadata file ('ISD v1.0 Metadata.xlsx') with photos and descriptions of each of the locations is provided. This metadata file also includes Data Dictionaries for each of the survey instrument versions included. These data dictionaries document precisely the questions asked and the available reponse labels and coding, along with the relevant translations.</p> <h3>Psychoacoustic Analysis (.csv)</h3> <p>The compiled csv file is formatted with a row for each individual participant's questionnaire response, then includes the psychoacoustic analysis of the 30s binaural recording taken while the participant was completing the questionnaire. Details about the psychoacoustic analyses is given in the 'Acoustic Settings' tab in the metadata file.</p> <p>The compiled survey and psychoacoustic analysis data is contained in 'ISD v1.0 Data.csv'. This is compiled from raw survey data files contained in 'Survey_Data', with individual cleaned survey and psychoacoustic data files included in 'Survey_Data/Interim_<date>'. The scripts for compiling this data are included in 'Scripts/'.</p> <h3>Sound Level Meter logs (.xlsx)</h3> <p>'SLM_<city>/' folders include session-long (i.e. ~3hrs) sound level meter log data in.xlsx files for each SessionID.</p> <h3>Binaural Recordings (32-bit floating point .wav)</h3> <p>'WAV_<city>/' folders include the ~30s binaural recordings in 32 bit floating point .wav format. Within each city folder are a set of LocationID folders containing their associated recordings. The wav files are titled with its GroupID, which is matched to the corresponding survey GroupIDs. </p> <h3>Cleaning and Compilation Scripts (.py)</h3> <p>Python code for cleaning and compiling the data from the raw survey data (within Survey_Data/source_data) are provided. These can be run within the provided demo notebook, or from the terminal by calling 'python -m ISDv1_main' with the relevant arguments. See the README.md file in this directory for more information.</p> <pre><code><br>├── ISD v1.0 Data.csv ├── ISD v1.0 Metadata.xlsx ├── SLM_Granada │ ├── CampoPrincipe1_SLM.xlsx │ ├── ... ├── SLM_Groningen │ └── Noorderplantsoen1_SLM.xlsx ├── SLM_etc ├── Scripts │ ├── ISDcleanDemo.ipynb │ ├── ISDcleaning.py │ ├── ISDpsycho.py │ ├── ISDv1_main.py │ ├── README.md │ └── pyproject.toml ├── Survey_Data │ ├── Interim_2024-02-08_cleaned │ └── source_data ├── WAV_Granada_1 │ ├── CampoPrincipe │ ├── ... ├── WAV_etc</code></pre> <p><strong>Citation</strong>: If you use the ISD or part of it, please cite our paper describing the data collection protocol [1] and this dataset itself.</p> <p><strong>License and reuse</strong>: All ISD recordings are provided under the Creative Commons Attribution 4.0 International (CC BY 4.0) License and are free to use. We encourage other researchers to replicate the SSID protocol and contribute new locations to the dataset. We also encourage the use of these recordings and the perceptual data for further soundscape research purposes. Please provide the proper attribution and get in touch with the authors if you would like to contribute new data or for any other collaborations.</p> <p> </p> <p>[1] Mitchell A, Oberman T, Aletta F, Erfanian M, Kachlicka M, Lionello M, Kang J. The Soundscape Indices (SSID) Protocol: A Method for Urban Soundscape Surveys—Questionnaires with Acoustical and Contextual Information. <em>Applied Sciences</em>. 2020; 10(7):2397. <a href="https://www.mdpi.com/2076-3417/10/7/2397">https://doi.org/10.3390/app10072397 </a></p> <p>[2] ISO/TS 12913-2:2018 (2018). “Acoustics – Soundscape – Part 2: Data collection and reporting requirements” International Organization for Standardization, Geneva, Switzerland, 2018</p> <p>[3] Mitchell A, Oberman T, Aletta F, Kachlicka M, Lionello M, Erfanian M, Kang J. Investigating Urban Soundscapes of the COVID-19 Lockdown: A predictive soundscape modeling approach.<em> Journal of the Acoustical Society of America</em>. 2021.</p>
Integrated database on adaptation and mitigation measures in Europe
<p>Climate action is far from meeting the internationally agreed adaptation and mitigation goals. Even though climate action planning has increased since the Paris Agreement in 2015, the implementation rate of those plans remains low. Climate planning literature claims that accounting for long-term planning and implementation times, accurately estimating costs, identifying synergies and trade-offs between measures, or considering justice and equity issues might increase the quality of climate plans and facilitate the further implementation of climate actions.</p> <p>Also, there is no uniform way of responding to the climate crisis. Existing climate action databases typically focus on a particular type of response, sector, hazard, or type. In parallel, national governments and international initiatives provide tools and guidelines to facilitate the development of climate action plans. However, the primary climate action recording and monitoring initiatives and projects do not share the same framework as those tools, resulting in a lost opportunity to improve climate actions' knowledge transferability.</p> <p>Thus, we reviewed nine existing databases of adaptation and five mitigation databases, comprising a total of 7.130 adaptation actions and 11.409 mitigation actions, and detected a lack of alignment with climate planning practices and claims. Furthermore, we revealed a lack of coherency regarding the level of abstraction of climate actions and their role in the implementation process. Not all climate actions are meant to operate similarly from a planning perspective: while some had a direct outcome on the target indicators, others are thought to facilitate their implementation.</p> <p>Ultimately, we created a new integrated database of adaptation and mitigation measures in Europe, focusing exclusively on climate planning and implementation practices. First, we identified specific and transferable mitigation and adaptation measures and instruments through an originally designed decision tree. Second, we harmonised the collection of climate actions in a unique framework based on one of the biggest climate planning initiatives: the Sustainable and Energy Climate Action Plans by the Covenant of Mayors. Our integrated database of adaptation and mitigation measures (1) classifies and relates the different types of climate actions; (2) provides data that may improve the quality of climate plans and facilitate implementation; (3) allows a better perspective of systematic problems by identifying potential synergies and trade-offs; and (4) defines and characterises measures using a framework that draws on actual practice. The database compiles a total of 191 adaptation measures, 188 mitigation measures, and 97 measures that account for each, and a total of 609 associated instruments. For monitoring their outcomes, 93 SDG relevant indicators are included.</p>
Dataset related to the manuscript: "An open-source integrated framework for the automation of citation collection and screening in systematic reviews"
<p>Dataset related to the manuscript: “An open-source integrated framework for the automation of citation collection and screening in systematic reviews”, to be used together with the code stored at https://github.com/AD-Papers-Material/BART_SystReviewClassifier to reproduce the results.</p> <p>There are three datasets:<br> - The Record data collected from the online scientific databases;<br> - The session journal which describes the search session, i.e., how many records were collected and from which source, for each query/session pairs.<br> - The session data which is the outcome of the classification and review tasks;</p>
Integrated analysis of anatomical and electrophysiological human intracranial data
<p>The exquisite spatiotemporal precision of human intracranial EEG recordings (iEEG) permits characterizing neural processing with a level of detail that is inaccessible to scalp-EEG, MEG, or fMRI. However, the same qualities that make iEEG an exceptionally powerful tool also present unique challenges. Until now, the fusion of anatomical data (MRI and CT images) with the electrophysiological data and its subsequent analysis has relied on technologically and conceptually challenging combinations of software. Here, we describe a comprehensive protocol that addresses the complexities associated with human iEEG, providing complete transparency and flexibility in the evolution of raw data into illustrative representations. The protocol is directly integrated with an open source toolbox for electrophysiological data analysis (FieldTrip). This allows iEEG researchers to build on a continuously growing body of scriptable and reproducible analysis methods that, over the past decade, have been developed and employed by a large research community. We demonstrate the protocol for an example complex iEEG data set to provide an intuitive and rapid approach to dealing with both neuroanatomical information and large electrophysiological data sets. We explain how the protocol can be largely automated and readily adjusted to iEEG data sets with other characteristics. The protocol can be implemented by a graduate student or post-doctoral fellow with minimal MATLAB experience and takes approximately an hour, excluding the automated cortical surface extraction.</p> <p>This collection contains the data described in the protocol and that can be used to replicate all results.</p>
CATCH-EyoU: Exploiting European data and testing the integrated theory of youth active EU citizenship: EACEA subset analysis
<p>This dataset was created within the research project Constructing AcTive CitizensHip with European Youth: Policies, Practices, Challenges and Solutions (CATCH-EyoU) funded by European Union, Horizon 2020 Programme, Grant Agreement No 649538. Work Package 4 of this project (Exploiting European data and testing the integrated theory of youth active EU citizenship) is focused on the re-analysis of existing European data. This dataset contains a subset of data originally collected within the project “<em>EACEA 2010/03: Youth Participation in Democratic Life</em>”, coordinated by the London School of Economic and Political Science. Specifically, an online questionnaire survey in seven European countries was conducted among young people age 15-30 in 2011. This dataset contains a subset of 22 variables that were employed for the reanalysis within the CATCH-EyoU project.</p>
Six decades (1959-2022) of water quality in the upper San Francisco Estuary: an integrated database of 16 discrete monitoring surveys in the Sacramento San Joaquin Delta, Suisun Bay, Suisun Marsh, and San Francisco Bay
The upper San Francisco Estuary (SFE) is simultaneously a central hub of water delivery in California and home to commercially important and endangered fishes, such as Chinook Salmon, Green Sturgeon, and Delta and Longfin Smelt. Extensive ecological monitoring has been conducted for over 50 years, mainly under the auspices of the Interagency Ecological Program for the San Francisco Estuary (https://iep.ca.gov/). We integrated water quality data from 16 boat-based long-term monitoring surveys in the upper SFE. This integrated dataset includes measurements of temperature (surface and bottom), conductivity (surface), salinity (surface), Secchi depth, qualitative concentration of the toxic alga Microcystis (surface), Chlorophyll-a concentration (surface), nutrients (surface), and other parameters from 1959 - 2022. The component surveys range in sampling frequency from thrice weekly to monthly and range in duration from 5 – 60 years. Most component surveys sample at fixed stations, but the Enhanced Delta Smelt Monitoring survey uses random sites and some stations (with “EZ” in the station name) of the Environmental Monitoring Program follow the salinity field. It is highly recommended to inspect the documentation of the component surveys for more information on their methods.
Continuous depth-integrated fluorometric phytoplankton measurements in Lake Bonney, McMurdo Dry Valleys, Antarctica (2013-2017)
Phytoplankton are key primary producers in the photic zones of permanently ice-covered lakes in Antarctica’s McMurdo Dry Valleys (MDV), playing a crucial role in regional carbon cycling. However, their seasonal dynamics remain poorly understood, particularly during winter, when the region is inaccessible to researchers. To address this gap, the Autonomous Lake Profiler and Samplers (ALPS) project, part of the McMurdo Dry Valleys Long Term Ecological Research (MCM LTER) program, deployed sensors and samplers to collect year-round data on phytoplankton dynamics. This data package provides continuous, depth-integrated fluorometric measurements of phytoplankton communities in Lake Bonney from 2013 to 2017. A submersible spectrofluorometer (bbe Moldaenke FluoroProbe) was used to quantify the vertical distribution of key algal classes – brown/mixed algae, green algae, and cryptophytes – within the deep photic zone of the lake’s eastern (21–24 m) and western (16–24 m) lobes. Lake Bonney serves as a year-round refugium for life in extreme environments, but rapid lake level rise over the past three decades has introduced new uncertainties regarding phytoplankton community dynamics. These data contribute to ongoing efforts to understand how environmental change influences microbial ecology in polar aquatic ecosystems.
An integrated polygenic tool substantially enhances coronary artery disease prediction
<p>Summary-level CAD GWAS data generated by Genomics plc as presented in:</p> <p>Riveros-Mckay F. et al. An integrated polygenic tool substantially enhances coronary artery disease prediction. Circulation: Genomics and Precision Medicine (in press). </p> <p>If you have any questions or comments regarding these files, please contact Genomics plc at research@genomicsplc.com</p> <p> </p> <p>NOTES<br> -----------------------------<br> These analyses were carried out using the full UK Biobank imputation data release (v3b). Analyses were restricted to a subset of UK Biobank, described as “Group I” in the published paper. Group I, “no PCE/QRISK3 available”, included 114,196 European-ancestry individuals with missing data that prevented PCE or QRISK3 calculation.</p> <p>CAD case phenotypes were defined as described in the “Phenotype definitions” section of the paper’s Supplementary Materials, using both prevalent (pre-baseline) and incident (post-baseline) events.</p> <p>All analyses included Age at assessment, sex, genotyping chip, and 10 principal components as covariates. </p> <p>We used plink2.0 logistic regression. For chromosome X variants males were treated as having 0 or 2 alternative alleles. </p> <p>The results are not adjusted for genomic control.</p> <p> </p> <p>DATA FILE CONTENT DESCRIPTION<br> -----------------------------<br> cpra Variant ID in ‘CPRA’ format. Position reflects position in b37. <br> chrom Chromosome<br> pos Position in base pairs (b37, 1-based)<br> alt Alternative allele (effect allele)<br> beta Effect size (log odds ratio)<br> standard_error Standard error of beta <br> minus_log10_p Minus log(base 10) of P-value<br> ref Reference allele (non-effect allele)<br> ncase Number of cases<br> ncontrol Number of controls</p>
Data and R code for Tansley review New Phytologist 2021: "An integrated framework of plant form and function: The belowground perspective"
<p>The files in this archive are related to the paper of Weigelt, Mommer, Andraczek et al. (2021) An integrated framework of plant form and function: The belowground perspective. Tansley Review New Phytologist. The paper developed and tested a new conceptual framework of plant form and function linking above and belowground traits of 2510 species. We found that an integrated, whole-plant trait space required as much as four axes. The two main axes represented the fast-slow ‘conservation’ gradient on which leaf and fine-root traits were well aligned, and the ‘collaboration’ gradient in roots. The two additional axes were separate, orthogonal plant size axes for height and rooting depth.</p> <p>This archives contains four files:</p> <ol> <li><strong>Weigelt et al.2021RCode.DataCleaning.txt</strong> - RCode for the complete data processing starting with the downloaded database files from the Plant Trait Database version 5.0 (TRY, Kattge et al. 2020), the Global Root Trait database (GRooT, Guerrero-Ramirez et al. 2020) and a small number of additional data files listed in Table S2 of the original paper. Additional information was later incorporated using FungalRoot Database (Soudzilovkaia et al. 2020), nodDB Database (Tedersoo et al. 2018) and a compiled dataset on rooting depth (Fan et al. 2017). The code processes, cleans and merges the data and produces a final table for PCA analysis of species specific mean traits. This final table is provided as a second file in this archive (Weigelt_et_al_2021_Main.PCA.Matrix.xlsx). A second part of the RCode.DataCleaning extracts species-specific individual trait data where root and shoot traits were measured on the same plant individual or plot. This data was compiled from 43 studies identified in Table S2 of the original publication. The final table for individual trait data is the third file in this archive (Weigelt_et_al_2021_Individual.PCA.Matrix.xlsx).</li> <li><strong>Weigelt_et_al_2021_Main.PCA.Matrix.xlsx</strong> – Datafile with species-specific global mean trait data for 17 traits of 2510 species with at least one root and one shoot trait available. Meta-data is provided in the data file.</li> <li><strong>Weigelt_et_al_2021_Individual.PCA.Matrix.xlsx</strong> – Datafile with species-specific trait data where root and shoot traits were measured on the same individual or plot for 6 traits of 455 species. Meta-data is provided in the data file.</li> <li><strong>Weigelt et al.2021RCode.Analysis.txt – </strong>RCode for all analyses and figures provided in the paper for both the species mean and individual based dataset. The Code is annotated to help reproducibility of the analysis.</li> </ol>
ScienceDex guides
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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.