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8,828 results for “productivity”
Total Annual Aboveground Net Primary Productivity across grassland-shrubland ecotones at 3 sites in the Jornada Basin, 2006-ongoing
The objective of this ongoing study is to investigate how pulses of precipitation translate into pulses of plant aboveground net primary productivity (NPP) across grassland to shrubland ecotones in the northern Chihuahuan Desert. This dataset consists of annual aboveground net primary productivity estimates in three habitat vegetation zones (grassland, ecotone, and shrubland) at three grassland-to-shrubland ecotone sites in the Jornada Basin, Dona Ana County, New Mexico, USA. The annual ANPP estimates are derived from plant cover measurements (see methods). Due to its growth form, Yucca elata (YUEL) has been found to produce large errors in interyear biomass estimates. This data package contains annual ANPP estimates both with and without YUEL, but the authors strongly recommend using the non-YUEL estimates for most purposes. Data collection is ongoing with new observations in spring and fall of each year; data from both annual sampling times are required to estimate annual ANPP.
PAB01 Aboveground net primary productivity of tallgrass prairie based on accumulated plant biomass on core LTER watersheds (001d, 004b, 020b)
Data set contains estimates of end-of-season standing crop biomass (grams per square meter) of live graminoids, forbs, woody plants, and previous year's dead vegetation for 2 soil types (shallow and deep) on three core LTER watersheds representing three fire frequency treatments. Twenty quadrats (0.1 square meters) are harvested for each soil/treatment type. NOTE: Early (April) and mid-season (July) biomass was collected from 1983-1988, and these data are available by request.
PRP01 Konza prairie long term restoration study of aboveground annual net primary productivity (ANPP)
The experiment is a randomized complete block design with four whole plot hetereogeneity treatments replicated within each of four blocks (n=16 whole plots). The whole plot treatments were created using different combinations of soil depth and nutrient manipulations. The control plots contained no depth or nutrient manipulations. The 'maximum hetereogeneity' plots contained three 2 m x 8 m vertical strips assigned to ambient, enriched and reduced N treatments and four 2 m x 6 m horizontal strips assigned to deep and shallow soil to result in six treatment combinations. The maximum heterogeneity plots are a split-block design. Each plot contained 12 subplots (2 m x 2 m) for sampling. All of the plots had surface soil temporarily removed to a depth of approximately 25 cm and natural limestone slabs were laid in strips assigned to the shallow soil treatment. The soil from all plots was then replaced, leveled, and disked (2-3 cm deep). In February 1998, we incorporated sawdust (49% C; C:N ratio=122) into the strips assigned to the reduced-N treatment. The average C concentration and bulk density in the surface 15 cm following long-term cultivation was 1.5% and 1.2 g cm-3, respectively. Sawdust was tilled into the soil at a rate of 5.5 kg dry wt./m2 to achieve a C concentration representative of native prairie soil (approx.3% C). Surface applications of granular sugar were initiated in 2004 at a rate of 200 g sucrose m-2 (84.22 g C/m2) 3-4 times each growing season. Strips assigned to the enriched-N treatment were fertilized with 5 g N m2/y (applied as ammonium-nitrate) in July of the first growing season and early June of each subsequent year.
North Temperate Lakes LTER: Primary Production - Trout Lake Area 1986 - 2007
Crystal, Sparkling and Trout lakes are sampled approximately fortnightly throughout the ice free period and water is returned to the lab for 3 hour in vivo incubations at ambient temperature. On an annual rotation, one of the lakes is sampled to the bottom of the photic zone (ie. 0.5% of surface light) while only the epilimnion of the other 2 lakes is sampled. All samples are integrated by thermal layer and 14C uptake is deternimed at 10 light levels using a metal-halide lamp. All samples are acidified and bubbled before liquid scintillation counting and uptake is dark bottle corrected. DIC and chlorophyll are also measured. Each P-I (photosynthetic uptake vs irradiance ) set is further reduced to 3 parameters (alpha, beta, and Pmax) based on the work of Platt 1980. In addition, thermal information and lake light transparency profiles (collected approximately fortnightly on these three LTER lakes) along with daily incident PAR at 30 minute intervals (measured at Noble F. Lee Municipal Airport, Woodruff, WI) provide input to a mechanistic model of lake primary productivity. Primary productivity for a lake is calculated from productivity parameters derived from the laboratory uptake experiments, coupled with the lakes' thermal and light regimes. 1. PI parameters: Productivity (P), as a function of irradiance (I), is described by a hyperbolic tangent curve with three parameters (Platt 1980). The three parameters describe the initial slope of the curve, the I at which maximum P occurs, and the decay rate of P, following maximum P. All three parameters are fit simultaneously to laboratory observations, using minimization of a least squares objective function (Matlab v. 6, Mathworks, Inc.). The parameters allow for the calculation of P, as a function of I, for a given thermal stratum of a given lake on a given day. 2. LEC: Light extinction coefficients (LEC) for each lake are calculated as the slope of the best fit line through the natural logs of observations of light at
Primary Production and Species Richness in Lake Communities 1997 - 2000
An understanding of the relationship between species richness and productivity is crucial to understanding biodiversity in lakes. We investigated the relationship between the primary productivity of lake ecosystems and the number of species for lacustrine phytoplankton, rotifers, cladocerans, copepods, macrophytes, and fish. Our study includes two parts: (1) a survey of 33 well-studied lakes for which data on six major taxonomic groups were available; and (2) a comparison of the effects of short- and long-term whole-lake nutrient addition on primary productivity and planktonic species richness Dodson, Stanley I., Shelley E. Arnott, and Kathryn L. Cottingham. 2000. The relationship in lake communities between primary productivity and species richness. Ecology 81:2662-79. Number of sites: 33
North Temperate Lakes LTER: Primary Production - Trout Lake Area 1986 - 1995
Primary production on three lakes (Crystal, Sparkling, Trout) is measured using C14 laboratory incubation under controlled temperature and light conditions. Sampling Frequency: fortnightly during ice-free season Number of sites: 3 Please consult NTL's website for information on experimental lake manipulations and the DNR's website for management activities
Production, biomass, and yield estimates for walleye populations in the Ceded Territory of Wisconsin from 1990-2017
Recreational fisheries are valued at $190B globally and constitute the predominant use of wild fish stocks in developed countries, with inland systems contributing the dominant fraction of recreational fisheries. Although inland recreational fisheries are thought to be highly resilient and self-regulating, the rapid pace of environmental change is increasing the vulnerability of these fisheries to overharvest and collapse. We evaluate an approach for detecting hidden overharvest of inland recreational fisheries based on empirical comparisons of harvest and biomass production. Using an extensive 28-year dataset of the walleye fisheries in Northern Wisconsin, USA, we compare empirical biomass harvest (Y) and calculated production (P) and biomass (B) for 390 lake-year combinations. Overharvest occurs when harvest exceeds production in that year. Biomass and biomass turnover (P/B) both declined by about 30% and about 20% over time while biomass harvest did not change, causing overharvest to increase. Our analysis revealed 40% of populations were production-overharvested, a rate about 10x higher than current estimates based on numerical harvest used by fisheries managers. Our study highlights the need for novel approaches to evaluate and conserve inland fisheries in the face of global change.
Microbial Bacterial Production in Lakes at North Temperate Lakes LTER 2000 - 2002
Net production of bacteria passing a 70 micron mesh, or bacteria passing a 1 micron mesh, calculated incorporation of 3H labeled leucine into cell proteins. Method based on microcentrifuge method of Smith, David C., and Farooq Azam. 1992. A simple, economical method for measuring bacterial protein synthesis rates in seawater using 3H-leucine, Marine Microbial Food Webs 6(2):107-114. Nanomolar treatments refer to leucine concentration for calculation of isotopic dilution and necessary leucine concentration to saturate uptake into bacterial cells. For a good example on how to calculate isotopic dilution see Pace. Michael L., and Jonathan J. Cole. Primary and bacterial production in lakes: are they coupled over depth? Plankton Research 16(6):661-672. Sampling Frequency: fortnightly during ice-free season - every 6 weeks during ice-covered season Number of sites: 4
SBC LTER: Reef: Seasonal Kelp Forest Community Dynamics: Taxon-specific seasonal net primary production (NPP) for macroalgae
This dataset provides estimates of seasonal net primary production (NPP) for all taxa of macroalgae sampled in fixed plots of the SBC LTER's seasonal kelp forest monitoring sites. The five reefs (Arroyo Quemada 34°28.048’N, 120°07.031’W; Carpinteria 34°23.474’N, 119°32.510’W; Isla Vista 34°23.275’N, 119°32.792’W; Mohawk 34°23.649’N, 119°43.762’W; and Naples 34° 25.342’N, 119° 57.102’W) ranged in depth from 5.8 m to 8.9 m (MLLW) and were chosen to represent a range of physical and biological characteristics known to influence subtidal macroalgal assemblages in the region. NPP of understory taxa was calculated using field measurements of irradiance and biomass (derived from abundance) and laboratory estimates of taxon-specific photosynthetic parameters. NPP for the giant kelp, Macrocystis pyrifera, was calculated using linear relationships between frond density in a given season and average NPP for that season.
SBC LTER: Reef: Long-term experiment: Taxon-specific seasonal net primary production (NPP) for macroalgae
This dataset provides estimates of seasonal net primary production (NPP) for all taxa of macroalgae sampled in fixed plots of the SBC LTER's long-term kelp removal experiment sites. The experiment was initiated in 2008 at 4 sites; a fifth site as added in 2011. Data collection is ongoing. NPP of understory taxa was calculated using field measurements of irradiance and biomass (derived from abundance) and laboratory estimates of taxon-specific photosynthetic parameters. NPP for the giant kelp, Macrocystis pyrifera, was calculated using linear relationships between frond density in a given season and average NPP for that season.
Tree Mast Production in Pinyon-Juniper-Oak Forests at the Sevilleta National Wildlife Refuge, New Mexico
The purpose of this study is to monitor the fruit production of three common woody tree species that occur on the Sevilleta National Wildlife Refuge (NWR) in New Mexico. We estimate fruit production of two monoecious species, Pinus edulis (pinon pine) and Quercus turbinella (Sonoran scrub oak) and one dioecious species, Juniperus monosperma (one-seed juniper). During August – November, we estimate fruit production for these three species at up to six sites within the Sevilleta NWR. Different protocols are used for each species (see Methods). In addition, the age and/or size of each individual tree was assessed at the beginning of the study, excepting in one site (WM) where fruit counts occurred at the plot, rather than individual tree, scale. For P. edulis and J. monosperma, trees wre binned into categories of young, medium, old, or very old trees. For Q. turbinella, we estimate canopy surface area in m 2
Precipitation-productivity relationships in desert grassland: a test of the double asymmetry hypothesis.
The purpose of this data package is to provide the derived data and R code for analyses presented in the manuscript by Collins et al. Knowing the relationship between precipitation (PPT) and aboveground net primary productivity (ANPP) is essential for understanding and modeling the global carbon cycle. Across grassland to forest gradients, the PPT-ANPP relationship is well-defined and non-linear. Temporal patterns within a site over time, however, are more variable than spatial patterns and nearly always linear. Linear relationships, however, are inconsistent with positive asymmetry occurring when the increase in ANPP in a wet year is greater than the decline in a dry year. The double asymmetry model predicts that concave down non-linearities will occur when extreme high and low PPT years are included in a time series. We used long-term ANPP data from ambient plots, plus rainfall addition and reduction experiments to test the predictions of the double asymmetry model. By combining experimental drought, plus water and nitrogen addition experiments we found some support for the double asymmetry model. However, the response was concave up not down under high precipitation coupled with nitrogen addition. By experimentally extending the range of monsoon precipitation we generated a significant although weak, non-linear PPT-ANPP relationship, but only when nutrient limitation was alleviated. Our results demonstrate that multiple interacting factors govern the PPT-ANPP relationship within a site over time.
S38 | SOLNSLMCTPS | SOLUTIONS Predicted Transformation Products by LMC
<p>This is the collection associated with list S38 SOLNSLMCTPS on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>S38 | SOLNSLMCTPS | <strong>SOLUTIONS Predicted Transformation Products by LMC</strong></p> <p>Predicted Transformation Products calculated by LMC during the SOLUTIONS project, interactive table available <a href="https://www.normandata.eu/solutions/modelsTransformationProducts.php">here</a>.</p> <p>14/11/19 update: added CSV version. 9/7/2025: fixed several corrupt SMILES and added InChIKeys to XLSX/CSV. Note that the author had to be changed to the University to satisfy Zenodo upload requirements, the original authors were listed as <a href="https://oasis-lmc.org/about/contacts.aspx">LMC</a>. </p>
Product Images for Life Cycle Assessment Dataset For Peritoneal Dialysis and Haemodialysis in Modena
<p>The database contains a collection of images showcasing the individual components of peritoneal dialysis (PD) products, along with their corresponding weights. These images serve as a visual record for life cycle assessment (LCA) purposes, focusing on the material composition and environmental impact of each product.</p> <ol> <li> <p><strong>Patient Education Materials</strong>: Photographs of educational materials provided to patients, with accompanying data on the weight of the paper and packaging.</p> </li> <li> <p><strong>Catheters and Surgical Kits</strong>: Images display the disassembled components of PD catheters and surgical kits, including tubing, connectors, and packaging. Each image is annotated with the precise weight of the individual components.</p> </li> <li> <p><strong>Dialysis Solution Bags</strong>: The database includes images of both CAPD and APD solution bags, separated into their constituent parts (e.g., plastic bag, solution, and protective wrapping), with weights noted for each component.</p> </li> <li> <p><strong>Connection Devices and Consumables</strong>: Detailed images of connection devices, clamps, and other consumable items, with individual component weights clearly labeled.</p> </li> <li> <p><strong>Packaging and Transport Materials</strong>: Photographs of transport packaging, such as cardboard boxes and plastic wraps, alongside recorded weights for each element.</p> </li> <li> <p><strong>Maintenance Items</strong>: Visuals of terminal catheter sets, cleaning agents, and related products, each accompanied by their respective weight data.</p> </li> <li> <p><strong>Disposal Components</strong>: Images of used solution bags, syringes, and other single-use items, separated into recyclable and non-recyclable components, with weights specified for each.</p> </li> </ol> <p>This image-based database provides a clear and comprehensive reference for the material breakdown and weight distribution of PD product components, essential for conducting a thorough LCA and identifying areas for environmental improvement.</p>
1600 years of modelled energy production and demand for European Countries (Norway, France, Italy, Spain, and Sweden)
<h3>Citation</h3> <p>When using this dataset, please cite the following paper: van der Most et al. Temporally compounding energy droughts in European electricity systems with hydropower, 10 January 2024, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-3796061/v1].</p> <h3>Description</h3> <p>This dataset contains daily renewable energy production and demand data used in the study "Temporally compounding energy droughts in European electricity systems with hydropower". The dataset includes production data for various renewable energy sources (offshore wind, onshore wind, solar photovoltaics, run-of-river, and hydropower reservoir inflow) and electricity demand. It was generated wit the use of 1600 years of climate model data and a daily renewable electricity production and demand modelling framework. The study focuses on five European countries with significant hydropower capacities: Norway, France, Italy, Spain, and Sweden.</p> <h3>Content</h3> <ul> <li> <p><strong>Energy Production Data</strong>:</p> <ul> <li>Offshore and Onshore Wind Power: Derived from 10 m wind speed data extrapolated to hub height, using power law equations and cubic power curves.</li> <li>Solar Photovoltaics (PV): Based on solar irradiance and temperature-dependent cell efficiency calculations.</li> <li>Hydropower: Includes inflow data for run-of-river and reservoir hydropower systems modelled with routed runoff data</li> <li>Hydropower dispatch is modelled at the national level using a linear optimization approach that aims to minimize the difference between demand and the sum of all renewable energy production over a year, directing the solution to following the load curves.</li> </ul> </li> <li> <p><strong>Energy Demand Data</strong>:</p> <ul> <li>Daily load data from ENTSO-E tranparancy fitted using a logistic smooth transmission regression approach to national mean, population-weighted daily near-surface temperatures from ERA5 reanalysis data.</li> <li>Demand curves account for weekdays and weekends but exclude cultural and socio-economic factors such as holidays.</li> </ul> </li> </ul> <h3>Methodology</h3> <p>The dataset is generated using the KNMI Large Ensemble Time Slice (KNMI-LENTIS) dataset, which includes 160 sets of 10-year physical climate model simulations of present-day climate (2000-2009). The simulations are conducted with the EC-Earth3 global climate model. The energy production and demand data are modeled to assess the impact of meteorological drivers on energy systems, with a focus on identifying periods of high residual loads (energy droughts). The model set-up has been validated with the use of ERA5 data in previous work. </p> <h3>Usage</h3> <p>This dataset is intended for researchers and policymakers interested in studying the impact of climate variability on renewable energy systems. It provides insights into how different meteorological conditions can lead to energy droughts and offers a basis for developing strategies to enhance the resilience of energy systems.</p> <p> </p>
Liquid Chromatography - Tandem Mass Spectrometry (LC-MS/MS) and Gas Chromatography - Mass Spectrometry (GC-MS) Reference Libraries from Global Natural Products Social Molecular Networking (GNPS) and National Institute of Standards and Technology (NIST) WebBook Processed for Spectral Library Matching
<div>In order to obtain a high-quality LC-MS/MS reference database for spectral library matching, we selected 22 high-quality GNPS tandem mass spectrometry databases generated under the positive ion mode. Further preprocessing similar to Huber et al involving mass-to-charge (m/z) and intensity filtering yields the database found in the file LCMS_GNPS_reference_library.csv which contains 14,705 electrospray ionization (ESI) mass spectra, each of which corresponds to a unique compound. The NIST WebBook database was used to construct GC-MS database contained in the file GCMS_NIST_WebBook.csv. This database contains 23,721 electron ionization (EI) mass spectra, each of which corresponds to a unique non-hyphenated Chemical Abstract Service (CAS) Registry Number.</div> <div> </div> <div>Both LC-MS/MS and GC-MS databases are organized into three columns: one for the identifier, one for the m/z values, and one for the intensity values. For example, if spectrum A has 20 ion fragments, then there will be 20 rows corresponding to spectrum A in the corresponding database with the identifier A repeated 20 times with the corresponding m/z and intensity values.</div>
Word Embedding of Amazon Product Review Corpus
<p>A word embedding of the <a href="https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html#datasets">Amazon Product Review Corpus</a> (<a href="https://www.doi.org/10.1145/1341531.1341560">Jindal and Liu, 2008</a>).</p> <p>Created using <a href="https://code.google.com/archive/p/word2vec/">Word2Vec</a> in CBOW mode, 500 dimensions and window size 5.</p> <p>Words have been lemmatised and particle verbs have been merged into a single token (e.g. <code>calm_down</code>).</p> <ul> </ul> <p> </p> <p><strong>Attribution</strong></p> <p>This dataset was created as part of the following publication:</p> <p>Marc Schulder, Michael Wiegand, Josef Ruppenhofer and Benjamin Roth (2017). <strong>"Towards Bootstrapping a Polarity Shifter Lexicon using Linguistic Features"</strong>. Proceedings of the 8th International Joint Conference on Natural Language Processing (IJCNLP). Taipei, Taiwan, November 27 - December 3, 2017. <a href="https://doi.org/10.5281/zenodo.3365609">DOI: 10.5281/zenodo.3365609</a>.</p> <p>If you use the data in your research or work, please cite the publication.</p>
Global monthly percentage of vegetation cover (MODIS FCover MODV1A product: America, Pacific)
<p>Monthly Global FCover product generated from MODIS data. Dataset represent monthly gap-filled FCover estimates the period 2000-2015 over Pacific and America. FCover was estimated using linear spectral mixture analysis and interpolated using empirical orthogonal functions algorithm to take advantage of all non-missing available pixels in both the spatial and temporal dimensions to gap-fill missing satellite observations. The global product of vegetation cover (as percentage of cover) based on MODIS images with monthly variation can be used as a critical support for several indicators related to ecologically based modelling.</p>
BIP! DB: A Dataset of Impact Measures for Research Products
<h2>Overview</h2> <p>This dataset contains citation-based impact indicators (also referred as <em>measures</em>) for ~296M distinct persistent identifiers (PIDs) that correspond to various types of research products (publications, datasets, software, and other products).</p> <p>The calculated indicators are organized into categories based on the aspect of impact they capture. </p> <h3>Influence indicators</h3> <p>Reflect the "total" impact of a research product; how established it is in general.</p> <ul> <li><strong><em>Citation Count:</em></strong> The total number of citations of the product, the most well-known influence indicator.</li> <li><strong><em>PageRank score:</em> </strong>An influence indicator based on the PageRank (Page et al., 1999), a popular network analysis method. PageRank estimates the influence of each product based on its centrality in the whole citation network. It alleviates some issues of the Citation Count indicator (e.g., two products with the same number of citations can have significantly different PageRank scores if the aggregated influence of the products citing them is very different - the product receiving citations from more influential products will get a larger score). </li> </ul> <h3>Popularity indicators</h3> <p>Capture the "current" impact of a research product; how popular it currently is.</p> <ul> <li><strong><em>RAM score:</em></strong> A popularity indicator based on the RAM (Ghosh et al., 2011) method. It is essentially a Citation Count where recent citations are considered as more important. This type of "time awareness" alleviates problems of methods like PageRank, which are biased against recently published products (new products need time to receive a number of citations that can be indicative for their impact).</li> <li><strong><em>AttRank score:</em></strong><strong> </strong>A popularity indicator based on the AttRank (Kanellos et al., 2020) method. AttRank alleviates PageRank's bias against recently published products by incorporating an attention-based mechanism, akin to a time-restricted version of preferential attachment, to explicitly capture a researcher's preference to examine products which received a lot of attention recently.</li> </ul> <h3>Impulse indicators</h3> <p>Measure the initial momentum that a research product received right after its publication.</p> <ul> <li><em><strong>Incubation Citation Count (3-year CC):</strong> </em>This impulse indicator is a time-restricted version of the Citation Count, where the time window length is fixed for all products and the time window depends on the publication date of the product, i.e., only citations 3 years after each product's publication are counted.</li> </ul> <h3>FIeld-weighted indicators</h3> <p>Capture the impact of a research product relative to the average performance in its field, accounting for differences in citation practices across disciplines.</p> <ul> <li><strong>Field-Weighted Citation Impact (FWCI):</strong> A field-weighted indicator that measures how a research product performs compared to the global average in its research field. An FWCI of 1.0 indicates that the product is cited exactly as expected for similar publications in the same field; values above 1.0 indicate above-average impact, while values below 1.0 indicate below-average impact.</li> <li><strong>3-year FWCI:</strong> A time-restricted version of the FWCI that considers citations received within the first three years after publication. By limiting the citation window, this indicator captures the early relative impact of a research product, providing insight into how quickly it gains influence in its field.</li> </ul> <p>In our analysis, the expected number of citations for each research product is computed by <em>grouping them by concept, publication year, and product type and then averaging the citations within each group</em>. </p> <p><em>More details about the aforementioned impact indicators, the way they are calculated and their interpretation can be found <a href="https://bip.imsi.athenarc.gr/site/indicators">here</a> and in the respective references (Kanellos et al., 2019).</em></p> <h2>Indicator calculation levels</h2> <p>The impact indicators are calculated in two levels:</p> <ul> <li><strong>PID level: </strong> assuming that each PID corresponds to a distinct research product. Currently PIDs are DOIs, PMCIDs, and PMIDs.</li> <li><strong>OpenAIRE-id level: </strong>leveraging PID synonyms based on OpenAIRE's deduplication algorithm (Manghi et al., 2020) - each distinct article has its own OpenAIRE id.</li> </ul> <h2>Impact classes</h2> <p>Each researcj product is also assigned an impact class, reflecting its percentile rank among all products in the dataset: </p> <table style="border-collapse: collapse; width: 100%; height: 39.1876px;"><colgroup><col style="width: 33.2913%;"><col style="width: 33.2913%;"><col style="width: 33.2913%;"></colgroup> <tbody> <tr style="height: 19.5938px;"> <td style="height: 19.5938px;"><strong>Class</strong></td> <td style="height: 19.5938px;"><strong>Percentile</strong></td> <td style="height: 19.5938px;"><strong>Description</strong></td> </tr> <tr style="height: 19.5938px;"> <td style="height: 19.5938px;">C1</td> <td style="height: 19.5938px;">Top 0.01%</td> <td style="height: 19.5938px;">Exceptional impact</td> </tr> <tr> <td>C2</td> <td>Top 0.1%</td> <td>Very high impact</td> </tr> <tr> <td>C3</td> <td>Top 1%</td> <td>High impact</td> </tr> <tr> <td>C4</td> <td>Top 10%</td> <td>Good impact</td> </tr> <tr> <td>C5</td> <td>Rest 90%</td> <td>Remaining products</td> </tr> </tbody> </table> <h2>File structure</h2> <p>For each calculation level (PID / OpenAIRE-id) we provide five (5) compressed CSV files (one for each measure/score provided). The structure of the files differs slightly depending on the level:</p> <ul> <li> <p><strong>PID-level files:</strong> Each line follows the format:<br><code>identifier <tab> identifier_type <tab> score <tab> class</code></p> </li> <li> <p><strong>OpenAIRE-id-level files:</strong> These files contain the keyword "openaire_ids" in the filename. Each line follows the format:<br><code>identifier <tab> score <tab> class</code></p> </li> </ul> <p><em>The parameter setting of each measure is encoded in the corresponding filename. For more details on the different measures/scores see our extensive experimental study (Kanellos et al., 2019) and the configuration of AttRank in the original paper (Kanellos et al., 2020).</em></p> <h3>Topic-related files</h3> <p>In addition to the main indicator files, the dataset also includes <em>topic-level outputs</em>, providing <em>field-weighted impact indicators</em> as well <em>percentile classes</em> within the associated <em>2nd-level concepts from OpenAlex</em>. </p> <p>Specifically, we associated all research products with their 2nd level concepts from OpenAlex (using only their <em>DOIs</em>); we kept only the three most dominant concepts for each product, based on their confidence score, and only if this score was greater than 0.3.</p> <p>Since currently only the DOIs are used to associate concepts from OpenAlex to research products, all identifiers in these files refer to DOIs. </p> <ul> <li><strong>Topic-specific impact classes file:</strong> Fore each concept and indicator, precentile classes are computed and provided in <code>topic_based_impact_classes.txt</code> in the following format:</li> </ul> <p><code>identifier <tab> concept <tab> pagerank_class <tab> attrank_class <tab> 3-cc_class <tab> cc_class</code></p> <ul> <li><strong>Field-weighted indicator files:</strong> Each line follows the format:<br><code>identifier <tab> concept <tab> score</code></li> </ul> <p><em>Note that to prevent division by zero, the score column is left empty whenever the average score for a specific combination of concept, publication year, and product type equals zero.</em></p> <h2>Data sources</h2> <p>The data used to produce the citation network on which we calculated the provided measures have been gathered from the OpenAIRE Graph v10.5.0, including data from (a) <em>OpenCitations' COCI & POCI dataset</em>, (b) <em>MAG</em> (Sinha et al, 2015; Wang et al., 2019), and (c) <em>Crossref</em>. The union of all distinct citations that could be found in these sources have been considered. </p> <p>Additionally, all topic-related computations are derived from OpenAlex concepts.</p> <h2>Access and Use</h2> <p>Find our Academic Search Engine built on top of these data <a href="https://bip.imsi.athenarc.gr/">here</a>. Further note, that we also provide all calculated scores through <a href="https://bip-api.imsi.athenarc.gr/documentation">BIP! Finder's API</a>. </p> <p><em>Terms:</em> These data are provided "as is", without any warranties of any kind. The data are provided under the CC0 license.</p> <h2>Changelog</h2> <p><strong>v19.1</strong></p> <ul> <li>[major update] Added field-weighted indicators: FWCI and 3-year FWCI.</li> </ul> <p><strong>v19.0</strong></p> <ul> <li>Added PMCID as an additional type of PID.</li> </ul> <p><strong>v15.1</strong></p> <ul> <li>Fixed missing records that were unintentionally omitted in v15.0</li> <li>Ensures all popularity indicators correctly use <code>current_year = 2025</code></li> </ul> <p><strong>v12.0</strong></p> <ul> <li>Added PMIDs as an additional type of PID.</li> </ul> <p><strong>v10.0</strong></p> <ul> <li>[Major update] Introduced deduplication of research products using the latest <a href="https://graph.openaire.eu/docs/graph-production-workflow/deduplication/research-products">OpenAIRE article deduplication algorithm</a>. Each node in the citation network is now a deduplicated product having a distinct OpenAIRE id. <ul> <li>Corrected overcounting of citations caused by multiple versions of the same product.</li> <li>PID-level scores are now derived from deduplicated OpenAIRE nodes.</li> </ul> </li> <li>Added filtering rules described <a href="https://graph.openaire.eu/docs/graph-production-workflow/aggregation/non-compatible-sources/doiboost/#crossref-filtering">here</a> to remove from dataset PIDs with problematic metadata. </li> </ul> <p><strong>v9.0</strong></p> <ul> <li>[Major update] Introduced topic-specific impact classes for PID-identified products based on OpenAlex 2nd-level concepts.</li> </ul> <p><strong>v7.0</strong></p> <ul> <li>[Major update] Added impact class labels (C1-C5) for each procuct, indicating the percentile-bsaed impact levels. <ul> <li>Classes reflect relative position within the global score distribution.</li> </ul> </li> </ul> <p><strong>v5.1</strong></p> <ul> <li>[Major update] Introduced dual-level score computation: PID level and OpenAIRE ID level.</li> </ul>
LTREB: Aboveground biomass, plant density, annual aboveground productivity, plant heights and snail observations in control and fertilized plots in a Spartina alterniflora-dominated salt marsh, North Inlet, Georgetown, SC: 1984-2025
Aboveground biomass and plant density were measured non-destructively as a component of a long-term project seeking to understand how salt marsh primary production and sediment chemistry respond to anthropogenic (e.g. eutrophication) and natural (e.g. sea-level rise) environmental change. Feedbacks between plants, sediments, nutrients and flooding were investigated with particular attention to mechanisms that keep marshes in equilibrium with sea level. Biomass was calculated from plant height measurements using allometric equations. Annual productivity was calculated from approximately-monthly biomass estimates. In addition to plant height measurements, observations of snails in sample plots were recorded. Other data collected as part of the project include marsh surface elevation and porewater nutrient concentrations. These data have been used to develop the Marsh Equilibrium Model, an important tool for coastal resource managers. Sampling occurred at Spartina alterniflora-dominated salt marsh sites in North Inlet, a relatively pristine estuary near Georgetown, SC on the SE coast of the United States. North Inlet is a tidally-dominated, bar-built estuary, with a semi-diurnal mixed tide and a tidal range of 1.4m. The 25-km2 estuary is comprised of about 20.5 km2 of intertidal salt marsh and mudflats, and 4.5 km2 of open water. Sampling began at one location in 1984, and at three additional locations in 1986. Sampling occurred approximately monthly through 2025. The study is on-going. There are four sampling locations at two sites. Two locations are in the low marsh; two locations are in the high marsh. One high marsh location had control sampling plots in addition to plots fertilized with nitrogen and phosphorus.
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.