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Net community production (NCP) and gross oxygen production (GOP), based on oxygen-argon ratios and triple oxygen isotopes, from seasonal NES-LTER Transect cruises in 2021
This data package provides net community production (NCP) and gross oxygen production (GOP, a measure of gross primary production) for the winter and summer Northeast U.S. Shelf Long-Term Ecological Research (NES-LTER) Transect cruises in 2021. Two tables are provided: a high-frequency table with NCP rates calculated from measurements of O2/Ar made continuously by an at-sea equilibrator inlet mass spectrometer (EIMS), and a low-frequency table with both NCP and GOP rates calculated for discrete samples measured post-cruise. The GOP rates were calculated from triple O2 isotopic (TOI) ratios. These data are derived from the EIMS and TOI data for the NES-LTER Transect cruises in EDI data package knb-lter-nes.6.3.
Net community production (NCP) and gross oxygen production (GOP), based on oxygen-argon ratios and triple oxygen isotopes, from seasonal NES-LTER Transect cruises in 2022
This data package provides net community production (NCP) and gross oxygen production (GOP, a measure of gross primary production) for the winter and summer Northeast U.S. Shelf Long-Term Ecological Research (NES-LTER) Transect cruises in 2022. Two tables are provided: a high-frequency table with NCP rates calculated from measurements of O2/Ar made continuously by an at-sea equilibrator inlet mass spectrometer (EIMS), and a low-frequency table with both NCP and GOP rates calculated for discrete samples measured post-cruise. The GOP rates were calculated from triple O2 isotopic (TOI) ratios. These data are derived from the EIMS and TOI data for the NES-LTER Transect cruises in EDI data package knb-lter-nes.6.3.
Microbial Observatory at North Temperate Lakes LTER High-resolution temporal and spatial dynamics of microbial community structure in freshwater bog lakes 2005 - 2009 original format (Reformatted to a Darwin Core Archive)
This data package is formatted as a Darwin Core Archive (DwC-A, event core). For more information on Darwin Core see https://www.tdwg.org/standards/dwc/. This Level 2 data package was derived from the Level 1 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-ntl/344/6, which was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-ntl/349/4. The abstract below was extracted from the Level 0 data package and is included for context: The North Temperate Lakes - Microbial Observatory seeks to study freshwater microbes over long time scales (10+ years). Observing microbial communities over multiple years using DNA sequencing allows in-depth assessment of diversity, variability, gene content, and seasonal/annual drivers of community composition. Combining information obtained from DNA sequencing with additional experiments, such as investigating the biochemical properties of specific compounds, gene expression, or nutrient concentrations, provides insight into the functions of microbial taxa. Our 16S rRNA gene amplicon datasets were collected from bog lakes in Vilas County, WI, and from Lake Mendota in Madison, WI. Ribosomal RNA gene amplicon sequencing of freshwater environmental DNA was performed on samples from Crystal Bog, North Sparkling Bog, West Sparkling Bog, Trout Bog, South Sparkling Bog, Hell’s Kitchen, and Mary Lake. These microbial time series are valuable both for microbial ecologists seeking to understand the properties of microbial communities and for ecologists seeking to better understand how microbes contribute to ecosystem functioning in freshwater.
Lake snow removal experiment phytoplankton community data, under ice, 2019-2021
Although it is a historically understudied season, winter is now recognized as a time of biological activity and relevant to the annual cycle of north-temperate lakes. Emerging research points to a future of reduced ice cover duration and changing snow conditions that will impact aquatic ecosystems. The aim of the study was to explore how altered snow and ice conditions, and subsequent changes to under-ice light environment, might impact ecosystem dynamics in a north, temperate bog lake in northern Wisconsin, USA. This dataset resulted from a snow removal experiment that spanned the periods of ice cover on South Sparkling Bog during the winters of 2019, 2020, and 2021. During the winters 2020 and 2021, snow was removed from the surface of South Sparkling Bog using an ARGO ATV with a snow plow attached. The 2019 season served as a reference year, and snow was not removed from the lake. This dataset represents phytoplankton community samples (pooled epilimnion and hypolimnion samples representative of 7 m water column) both under-ice and during some shoulder-season (open water) dates. Samples were collected into amber bottles and preserved with Lugol's solution before they were sent to Phycotech Inc. (St. Joseph MI, USA) for phytoplankton taxonomic identification and quantification.
Alpine soil islands plant and soil microbial community composition, 2024.
High alpine ecosystems are particularly sensitive to climate-driven change, with vegetation expansion increasingly observed in historically barren soils. In late August and early September 2024, we revisited 50 previously established vegetation plots in Green Lakes Valley (Niwot Ridge LTER) to evaluate patterns of plant colonization and community change over time. Using legacy vegetation data from 2008 and 2015, we assessed changes in plant cover and composition in relation to microtopography and prior plant occurrence. Concurrently, we collected soil samples for 16S and 18S rRNA gene sequencing to characterize bacterial, archaeal, and eukaryotic microbial communities associated with these plots. Vegetation was resampled using spatially referenced 1-meter radius surveys, estimating species incidence and cover and documenting moss, lichen, sedge, and grass diversity. Together, these above- and belowground data provide insight into how priority effects, fine-scale environmental variation, and plant–microbe interactions influence alpine community dynamics, and may inform predictive models of ecosystem responses to ongoing climatic shifts.
SBC LTER: BEACH: Invertebrate community structure and ecosystem functions of 24 sandy beach sites
These data result from a survey of 24 sandy beach sites in Santa Barbara and Ventura Counties in 2017 and 2018. We quantified marine macrophyte wrack subsidies, macroinvertebrates, and five ecosystem functions on three replicate transects at each site in order to elucidate the role of marine wrack subsidies on recipient ecosystem community structure and functioning. We also measured shorebirds at each site on three replicate survey days. Data are contained in four tables: 1) wrack cover and invertebrate community data by transect for each site used in our PiecewiseSEM model, 2) wrack cover and ecosystem function data by transect for each site used in our ecosystem multifunctionality estimate, 3) invertebrate species abundance and biomass by transect for each site, and 4) shorebird species abundance by survey date for each site.
SBC LTER: Beach: CO₂ flux, wrack subsidies, invertebrate community, and consumer respiration rates for Channel Islands sandy beaches
These data result from surveys of 14 sandy beach sites on four of California’s Channel Islands from 2016 to 2018. We quantified marine macrophyte wrack subsidies, macroinvertebrates, beach physical parameters, and sediment CO2 flux at each site in order to elucidate the role of marine wrack subsidies and wrack consumers on sandy beach sediment CO2 flux. We also measured the respiration rates of the six most common wrack consumer species in the laboratory. Data are contained in two tables: 1) Mean wrack cover, invertebrate community composition (species richness, abundance, and biomass), beach physical parameters, and sediment CO2 flux, and 2) respiration rates and biomass of each replicate individual for each of the six species.
SBC LTER: Reef: Community structure and productivity of subtidal turf and foliose algal assemblages at Naples Reef, 2006
This dataset contains abundance, primary production and respiration of macroalgal and turf assemblages at Naples Reef (Santa Barbara County, CA) during 2006. It includes abundance of macroalgae is in terms of biomass (dry weight), and abundance of animals is in numbers of individuals and biomass (ash-free dry weight). Primary production and respiration of the benthos were measured in situ as changes in oxygen in closed chambers that covered 0.1m2 of the bottom. Species richness data are for macroalgae only. Abundance and diversity were measured for the same plots where oxygen measurements were recorded. These data were presented in: <ulink url="http://dx.doi.org/10.3354/meps08131">Miller, R.J., D. Reed, and M. Brzezinski. 2009. Community structure and productivity of subtidal turf and foliose algal assemblages. Marine Ecology Progress Series 388:1-11 doi: 10.3354/meps08131</ulink>.
Root-associated fungal communities exposed to experimental drought
Plant-associated fungi can ameliorate abiotic stress in their hosts, and changes in these fungal communities can alter plant productivity, species interactions, community structure and ecosystem processes. We investigated the response of root-associated fungi to experimental drought (66% reduction in growing season precipitation) across six North American grassland ecosystem types to determine how extreme drought alters root-associated fungi, and understand what abiotic factors influence root fungal community composition across grassland ecosystems. Next generation sequencing of the fungal ITS2 region demonstrated that drought primarily re-ordered fungal species’ relative abundances within host plant species, with different fungal responses depending on host identity. Grass species that declined more under drought trended toward less community re-ordering of root fungi than species less sensitive to drought. Host identity and grassland ecosystem type defined the magnitude of drought effects on community composition, diversity, and root colonization, and the most important factor affecting fungal composition was plant species identity.
Comparative Bird Community Assessments in Grassland, Shrubland, and Woodland Habitats at the Sevilleta National Wildlife Refuge, New Mexico (1991-1997 and 2022-2023)
Across North America, avifauna abundance has declined by 30% since 1970 (Rosenberg, K.V. et al. 2019). Direct mortality from anthropogenic sources (pets, cars, collisions with building, power lines, wind turbines, etc.) and indirect mortality (habitat loss, disturbance, climate change, etc.) have both been major contributors to these declines (Loss, S.R. et al. 2015 and Calvert, A.M. et al. 2013). Variables such as migration patterns, family, breeding and non-breeding biomes show differing rates of decline (Rosenberg, K.V. et al. 2019). In New Mexico, there are three breeding biomes all classified with declining avian abundance. Avian abundance in grasslands has declined by 53.3% since 1970, western forests by 29.5% and arid lands by 17.0% (Rosenberg, K.V. et al. 2019). All three of these biomes also occur at the Sevilleta National Wildlife Refuge thus temporal declines in species richness and abundance are expected. This project was originally designed to sample the species richness and abundance of birds on the Sevilleta National Wildlife Refuge in three types of habitat: grassland, creosote shrubland and pinyon-juniper woodland. Surveys were conducted between January 1991 and May 1997 (Parmenter, R. 2016). Surveys were re-established in 2022 to document current species richness and abundance and to capture any temporal changes from the 90s data. Avian point count survey stations in grassland, creosote and pinyon-juniper habitats run through existing study sites which have all been subjected to intense research activity. Literature Cited A. M. Calvert, C. A. Bishop, R. D. Elliot, E. A. Krebs, T. M. Kydd, C. S. Machtans, G. J. Robertson, A synthesis of human-related avian mortality in Canada. Avian Conserv. Ecol. 8, art11 (2013). https://www.ace-eco.org/vol8/iss2/art11/ Loss, S. R., Will, T., Marra, P. P. 2015. Direct Mortality of Birds from Anthropogenic causes. Annu. Rev. Ecol. Evol. Syst. 46, 99–120. https://www.annualreviews.org/doi/10.1146/annurev-ecolsys-1124
Shrub Edge Community and Microclimate Data from Hog Island, VA 2021-2022
This data was collected from 2021-2023 on Hog Island, VA in the swale along the south end of the island where there is active shrub expansion of Morella cerifera (Southern wax myrtle). The microclimate data includes water table depth, soil moisture, soil nutrients, PAR (Photosynthetically active radiation), and temperature in (℃). These data were collected along the shrub edge and in the open grassland plots (n=10). The vegetation community data includes percent cover, stem density, and height measured in a 0.25 m2 plot. The 4 species identified to dominate in both communities were: Andropogon virginicus, Spartina patens, Panicum amarum, and Solidago sempervirens. These species were used for trait analyses. The trait data includes: specific leaf area (SLA), stem specific density (SSD), leaf dry matter content (LDMC), and leaf nutrients.
The Research Software Alliance (ReSA) and the community landscape
<p>The Research Software Alliance (ReSA)’s mission is to bring research software communities together to collaborate on the advancement of research software. ReSA has formed taskforces and one of them revolves around a <strong>software landscape analysis</strong> aiming to answer the question "How can we identify the different communities and topics of interest for the research software community (e.g., preservation, RSEs, citation, productivity, sustainability)?"</p> <p>Here we include text describing the work of the taskforce to date (see <a href="https://zenodo.org/api/files/5a1e0c32-cbf7-4b9a-9c02-139b2ce66e85/2020-03-11-ReSA-landscape.md?versionId=ffedcfe9-33f4-4aba-9d6b-9975fc1c548d">2020-03-11-ReSA-landscape.md</a>), as well as plans for the future, and an invitation to readers to contribute to the ReSA list of research software communities. We are a;sp including the current version of the list in a CSV file (see <a href="https://zenodo.org/api/files/5a1e0c32-cbf7-4b9a-9c02-139b2ce66e85/2020-03-11-ReSA-landscape.csv?versionId=be4bbcdd-79a8-4444-a764-98bf0d548922">2020-03-11-ReSA-landscape.csv </a>), and we welcome contributions on the live spreadsheet that can be found via this <a href="https://docs.google.com/spreadsheets/d/15JHqOxR4HIKHYe821IPvbxIuXP1zMjXKGEIJwB-GPqE/edit#gid=0">link</a>.</p>
Data set: Can ocean community production and respiration be determined by measuring high-frequency oxygen profiles from autonomous floats?
<p>Relevant autonomous float data for <a href="https://doi.org/10.5194/bg-17-4119-2020">Gordon et al. (2020)</a>. Following a similar structure to the Argo network's "synthetic" profile files, one file per float is produced with all relevant variables (temperature, salinity, chlorophyll, backscatter, dissolved oxygen) on a common depth and time grid. The Electro-Magnetic Autonomous Profiling Explorer (EM-APEX) floats were deployed in the northern Gulf of Mexico in May 2017 - see <a href="https://doi.org/10.1109/CWTM43797.2019.8955168">Shay et al. (2019)</a> for more information. </p> <p>The data published here contains a timestamp for each data point. Another version of this data which contains some additional variables is hosted on <a href="https://data.gulfresearchinitiative.org/data/R5.x275.281:0001">GRIIDC</a>, but does not contain a timestamp for each data point, but rather for each profile. </p>
Data and code from: Insect biomass decline scaled to species diversity: General patterns derived from a hoverfly community
<p>To study changes in flying insect communities, and hoverflies in particular, malaise trap samples from a German site were compared between two years (Hallmann et al. 2020). The data files deposited here contain data obtained from six malaise traps in the Wahnbachtal (North Rhine-Westphalia, Germany, 50.851944N, 7.320833E) that were deployed in 1989 and again in 2014, at the exact same locations. Traps were situated in wet meadows as well as tall perennial meadows, in close proximity to shrub corridors, to forest–grassland borders, and to the Wahnbach River and surrounded by agricultural land, essentially a rather heterogeneous habitat. The Wahnbach River and the greater part of the valley are protected for watershed purposes and are subject to nature conservation management by the Wahnbach Talperrenverband. Hence, several restrictions apply to safeguard against water contamination.</p> <p>Total insect biomass collected with these traps was already included in Hallmann et al. (2017), but here we focus on additional information: the abundance and richness of hoverflies (Syrphidae) in each of the collected samples (pots). Methodologies of collection are described in Sorg (1990), Schwan et al. (1993), Sorg et al. (2013), Hallmann et al. (2017), and Ssymank et al. (2018). In brief, malaise traps were deployed throughout the growing season and operated continuously (day and night). Malaise trap construction (e.g., size, material, colouring, and ground sealing) and placing (e.g., positioning, orientation, and slope of the locations) were standardised in all aspects. Insect samples were preserved in 80% ethanol solution. Catches of the six traps investigated in the present study were emptied regularly: On average exposure intervals were 7.0 d (SD = 0.5) in 1989 and 16.7 d (SD = 5.6) in 2014. Across the six traps in 2014 the total exposure time (in number of days) was 42% higher compared to 1989. All collected samples (n = 196) were used in the present analysis with in total 19,604 individual hoverflies counted, distributed over 162 species and 59 genera.</p> <p>To assess how environmental conditions have changed over the 25 year, several additional datasets were assembled. Climatic<br> data were obtained from 169 climatic stations and were used to interpolate daily weather variables to each trap location, using spatiotemporal kriging. These steps are described in detail in Hallmann et al. (2017).</p> <p>Our analysis (see R code) consists of three components. First, we considered total abundance, species richness, and species diversity, at two temporal scales: pooled per year, i.e., across the sampling season, and seasonally (i.e., per day), and we compared these metrics between 1989 and 2014. Second, we examined how total flying biomass (i.e., the weight of all trapped insects, of which hoverflies are only a small proportion) related to total abundance as well as species richness of hoverflies. Third, we derived persistence probabilities and population growth rate trends per species, to examine interspecific variation in these parameters.</p> <p>Descriptions of the deposited files:</p> <p><strong>Groups.csv</strong><br> MF_NR = identifier of each of the six malaise trap locations<br> yrf = year of sampling<br> pot = sample identifier<br> dt = number of sampling days<br> from.dnr = day-of-the-year on which a pot was attached to a malaise trap<br> to.dnr = day-of-the-year on which a pot was collected from a malaise trap<br> mean.daynr = mean day-of-the-year of the sampling period<br> Nspec = number of different hoverfly species found in a pot<br> Nind = number of hoverfly individuals found in a pot</p> <p><strong>Counts.csv</strong><br> A matrix of counts of individual hoverflies per pot per species. The 196 rows represent the pots in the same order as in the file 'Groups.csv'. The columns represent the 162 different hoverfly species found. The scientific species names are indicated in the column headers.</p> <p><strong>PairedData.csv</strong><br> pot = sample identifier<br> JAHR = year of sampling<br> MF_NR = identifier of each of the six malaise trap locations<br> dt = number of sampling days<br> from.dnr = day-of-the-year on which a pot was attached to a malaise trap<br> to.dnr = day-of-the-year on which a pot was collected from a malaise trap<br> NI = number of hoverfly individuals found in a potbiomass.daily<br> NSP = number of different hoverfly species found in a pot<br> biomass.daily = daily fresh weight [gram] of flying insects: total fresh weight in a pot divided by the number of sampling days.</p> <p><strong>ModelFrame.csv</strong><br> MF_NR = identifier of each of the six malaise trap locations<br> yrf = year of sampling<br> pot = sample identifier<br> dt = number of sampling days<br> from.dnr = day-of-the-year on which a pot was attached to a malaise trap<br> to.dnr = day-of-the-year on which a pot was collected from a malaise trap<br> mean.daynr = mean day-of-the-year of the sampling period<br> plot = identifier of each of the six malaise trap locations<br> date = date for which the weather variables are interpolated<br> daynr = day-of-the-year for which the weather variables are interpolated<br> altitude = altitude [m] of the malaise trap locations<br> year = year of sampling<br> temperature = interpolated temperature [degrees Celsius]<br> precipitation = interpolated precipitation [mm per day]<br> wind.speed = interpolated wind speed [m/s]</p> <p><strong>Data_Rcode.pdf</strong><br> This pdf provides the R-code behind the analysis of the Hoverfly data. Three datasets are provided along with this R-code document, namely "Counts.csv", "Groups.csv", "PairedData.csv" and "ModelFrame.csv". Additionally, the BUGS-code ""syrphidModel.jag" is required for running the daily-activity model in JAGS.</p> <p><strong>syrphidModel.jag</strong><br> This BUGS-code is required for running the daily-activity model in JAGS.</p>
Supplementary material for the publication: J.D. Nixon, K. Bhargava and E. Gaura, Energy Performance Gap in Community-Based Solar Energy Interventions: Lessons from two Rwandan Refugee Camps, 2020
<p>The dataset deposited here was prepared under the EPSRC-funded <a href="http://heed-refugee.coventry.ac.uk/">Humanitarian Engineering and Energy for Displacement</a> research project (EP/P029531/1). The project aimed to understand energy needs of displaced communities, create an evidence base on the usage of different energy interventions and provide recommendations for improved design of future energy interventions to better meet the needs of people. </p> <p>As part of the project, we deployed a Standalone Solar System for a Community Hall in Nyabiheke camp, Rwanda, and a PV-battery Microgrid in Kigeme camp, Rwanda. The microgrid supplies power to a playground and two nursery buildings. It powers a total of 20 CPE (each with 3 LEDs) and 10 sockets. The standalone system at Hall powers 7 CPE (with 3 LEDs each) and 4 sockets. The aim of the study was to (a) understand the energy consumption behaviour, light usage and other enabled uses within the set location in each camp (b) create an evidence base on the value of energy and its benefits in displaced contexts (c) identify best practice in the construction, control and operation of the respective systems as a shared energy resource.</p> <p>The system data used for the performance analysis for this study (July 2019 and March 2020) is deposited here along with the metadata. The results from analysis are presented in a paper titled '<strong>Energy Performance Gap in Community-Based Solar Energy Interventions: Lessons from two Rwandan Refugee Camps</strong>' (currently under submission). The scripts for analysis can be found at our Github account <a href="https://github.com/cogent-computing">Cogent Labs</a> under HEED-Microgrid and HEED-Hall repositories.</p>
Data from: Consistent trait-environment relationships within and across tundra plant communities
<p>A fundamental assumption in trait-based ecology is that relationships between traits and environmental conditions are globally consistent. We use field-quantified microclimate and soil data to explore if trait-environment relationships are generalisable across plant communities and spatial scales. We collected data from 6720 plots and 217 species across four distinct tundra regions from both hemispheres. We combine this data with over 76000 database trait records to relate local plant community trait composition to broad gradients of key environmental drivers: soil moisture, soil temperature, soil pH, and potential solar radiation. Results revealed strong, consistent trait-environment relationships across Arctic and Antarctic regions. This indicates that the detected relationships are transferable between tundra plant communities also when fine-scale environmental heterogeneity is accounted for, and that variation in local conditions heavily influences both structural and leaf economic traits. Our results strengthen the biological and mechanistic basis for climate change impact predictions of vulnerable high-latitude ecosystems.</p> <p>Kemppinen, Niittynen, le Roux, Momberg, Happonen, Aalto, Rautakoski, Enquist, Vandvik, Halbritter, Maitner & Luoto (2021). Consistent trait-environment relationships within and across tundra plant communities. Nature Ecology and Evolution</p> <p>These are the data and codes from Kemppinen et al. (2021).</p>
Data from: Choosy beetles: how host trees and southern boreal forest naturalness may determine dead wood beetle communities
<p>See methods section of paper for detailed information on dataset and sources; briefly, these .csv files includes numbers of each beetle species captured at all sites used in the project, as well as information about each site and about each species.</p> <p> </p> <p>Data from:</p> <p><strong>Choosy beetles: how host trees and southern boreal forest naturalness may determine dead wood beetle communitie</strong><strong>s</strong></p> <p>Ryan C. Burner, Tone Birkemoe, Jörg G. Stephan, Lukas Drag, Jörg Muller, Otso Ovakainen, Mária Potterf, Olav Skarpaas, Tord Snall, Anne Sverdrup-Thygeson</p> <p>Forest Ecology and Management, 2021</p> <p> </p> <p>From abstract of paper:</p> <p>Wood-living beetles make up a large proportion of forest biodiversity, and contribute to important ecosystem services, including decomposition. Beetle communities in managed southern boreal forests are less species rich than in natural and near-natural forest stands. In addition, many beetle species rely primarily on specific tree species. Yet, the associations between individual beetle species, forest management category, and tree species are seldom quantified, even for red-listed beetles. We compiled a beetle capture dataset from flight intercept traps placed in Norway spruce (<em>Picea abies</em>), oak (<em>Quercus sp.</em>), and Eurasian aspen (<em>Populus tremulae</em>) trees in 413 sites in mature managed forest, near-natural forest, and clear-cuts in southeastern Norway. We used joint species distribution models to estimate the strength of associations for 368 saproxylic beetle species (including 20 vulnerable, endangered, or critical red-listed species) for each forest management category and tree species. Tree species on which traps were mounted had the largest effect on beetle communities; oaks had the most highly associated beetle species, including most of the red-listed species, followed by Norway spruce and Eurasian aspen. Most beetle species were more likely to be captured in near-natural than in mature managed forest. Our estimated associations were compatible – for many species – with categorical classifications found in several existing databases of saproxylic beetle preferences. These quantitative beetle-habitat associations will improve future analyses that have typically relied on categorical classifications. Our results highlight the need to prioritize conservation of near-natural forests and oak trees in Scandinavia to protect the habitat of many red-listed species in particular. Furthermore, we underline the importance of carefully considering the species of trees on which traps are mounted in order to representatively sample beetle communities in forest stands.</p>
Normalized community CHP profiles
<p>Normalized community CHP profiles for Austria, France, Italy, Spain and Sweden for year 2018/2019.</p>
Convex inference for community discovery in signed networks (European Parliament Voting Dataset)
<p>This repository contains the necessary tools to reproduce the experiments of the paper</p> <ul> <li>G. Santatmaría, V. Gómez (2015)<br> Convex inference for community discovery in signed networks.<br> NIPS 2015 Workshop: Networks in the Social and Information Sciences</li> </ul> <p>The method first maps the MAP problem on the Potts model as a hinge-loss minimization problem (see the paper for details). To run the code you need to install psl (included here) and if you want to additionally compare with other inference methods, such as max prod belief propagation or junction tree, you need to install the libDAI library (also included here)</p> <p>The directory europeanCongressData/ (~500 Mb) contains the votings of the EU parlament, including 300 votings events from the actual term, from May 2014 to June 2015, obtained from http://www.votewatch.eu/</p> <ul> <li>data/ : json files with the european votes</li> <li>network.net : signed network built from the votes</li> <li>political_parties.txt : "ground truth" party</li> <li>community_results/ : results for different number of communities and initial vertices</li> <li>dataComputations.py : used to build the signed network</li> <li>dataProcessing.py : used to build the signed network</li> </ul> <p>We would appreciate if you cite the paper after using the data or the code.</p> <p>DEPENDENCIES</p> <p>The code has been tested in Linux Mint 18.1 Serena and Ubuntu 14.04</p> <p>- For PSL library, you need to have<br> java 1.8<br> you may need to export JAVAHOME='/usr/lib/jvm/YOURJAVA1.8FOLDER'<br> maven 3.x</p> <p>- For libDAI you will need:<br> make doxygen graphviz libboost-dev libboost-graph-dev libboost-program-options-dev libboost-test-dev libgmp-dev cimg-dev libgmp-dev</p> <p>CODE TO RUN THE FOLLOWING EXPERIMENTS:</p> <p>Compare the performance in terms of structural balance of max prod bp and our method against an exact inference method (junction tree), with different number of communities</p> <p>INSTALL</p> <p>To install the experiments you have to follow the next steps:</p> <p>1 Build the libdai library by doing: make -B on the folder (libdai)</p> <p>2 Generate the class path of the groovy project:<br> mvn clean install<br> mvn dependency:build-classpath-Dmdep.outputFile=classpath.out</p> <p>on the psl root folder (You need to have java 1.8 and maven 3.x installed)</p> <p>3 Grant exec permissions to the run.sh script</p> <p>Options</p> <p>The main python file to run the experiments is</p> <p>evaluatebalanceon_sn.py.</p> <p>It accepts the following parameters:</p> <p>1 (Int) Nodes of the graph. In order to run the junction tree we recommend to set this paremeter to 150 or less<br> 2 (Int) The number of underlying communities<br> 3 (Float) The maximum amount of unbalance for the experiments. We recommend 0.45<br> 4 (Bool) Whether to use an heuristic to find the initial node for each community or to use directly random nodes from the ground truth communities. This heuristic looks alternatively for the nodes with highest negative degree and highest positive degree. For the case when the number of communities is equal to 2 (Ising Model), the heuristic is used by default.</p> <p>An example of execution would be:</p> <p>python evaluate_balance_on_sn.py 120 3 0.45 True True</p> <p>The results of the experiments are save in the folder results/<br> Scripts</p> <p>The main script of the hinge-loss method can be found in the folder psl/psl-example/src/main/java/edu/umd/cs/example/PottsCommunities.groovy</p> <p>Authors:</p> <p>Guillermo Santamaria & Vicenc Gomez<br> Mar 5, 2017</p> <p>For further questions, please contact vicen.gomez@upf.edu</p>
Sunburned plankton: Ultraviolet radiation inhibition of phytoplankton photosynthesis in the Community Earth System Model version 2
<p>Climate model output for paper describing CESM2-UVphyto.</p>
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.