Find research datasets worth reusing

openOpenApr 2024View details →

Bonanza Creek LTER: Hourly Snow Pillow Measurements from 2007 to Present in the Caribou-Poker Creeks Research Watershed near Fairbanks, Alaska

The snow pillow records the hourly water content of the snowpack (snow water equivalent) at the CARSNOW site within the Caribou Poker Creeks Research Watershed during the winter months. It consists of two 1m square aluminium "pillows" filled with a propylene glycol/water solution attached via piping to a druck pressure transducer. The pressure on the pillow is converted to cm of water. A manometer tube is also attached for manaul readings and calibration.

openOpenMar 2022View details →

Bonanza Creek LTER: Point Bar Vegetation Survey of Bonanza Creek LTER Research Plots (2007-Present)

Beginning in 2007 ocular vegetation estimates were replaced by the point bar system. The point bar is meant to be a more objective way of carrying out annual vegetation surveys. In particular because you are placing the point bar in the same location every time a site is visited, a better understanding of vegetation change over time is possible. This method replaces the old system of estimating percent cover visually (<a href="https://www.lter.uaf.edu/data/data-detail/id/174"> Vegetation Plots of the Bonanza Creek LTER Control Plots: Species Percent Cover (1975 - 2009) </a>), which is often subject to personal bias and small shifts in species composition are often overlooked. Data from both methods were collected during the 2007, 2008, and 2009 field seasons, and regression analysis shows unique and statistically significant relationships between the two methods depending on growth form. At each site, growth forms were evaluated separately, and at each site there is a specific regression model for each growth form. In this way a user can correlate the two methods and data collected before 2007 can be compared to data collected after the new protocol was established.

openCC0Jan 2022View details →

Long-term monitoring and research of the ecology of the Tres Rios constructed treatment wetland, Phoenix, Arizona, USA, ongoing since 2011

# Project Description In order to better understand the water, nutrient and treatment dynamics of aridland constructed treatment wetlands, we have developed datasets tracking primary productivity (aboveground and belowground), nutrient and water budget dynamics, soils, and aquatic metabolism at the Tres Rios wetlands, operated by the City of Phoenix Water Services Department, since July 2011. The 3-cell Tres Rios Wetlands were completed in 2010 and are associated with the 91st Avenue Wastewater Treatment Plant, the largest in Phoenix. This project is focused on the largest of the three wetlands treatment cells which was the first to be planted and became operational in Summer 2010. The wetland cells are bounded by roads (the "shoreline"), and the system we study is 42 ha in size, approximately half of which is open water and half of which is fringing vegetated marsh. Water depth is relatively consistent across the marsh (approximately 25cm) and effluent inflow to the cell varies seasonally from 95,000 to over 270,000 m3 d-1. Measurements are taken along two gradients representing the two hydraulic pathways of the system: The whole-system, from inflow to outflow, within the vegetated marsh itself. # Abstract Constructed treatment wetlands (CTW) provide cost effective and ecosystem-service based solutions to the problem of urban wastewater treatment. They are a particularly attractive option for water reuse in arid cities, where water resources are scarce, and understanding CTW function in these environments is critical to facilitating sustainable water use practices. Although CTW are well established and studied in mesic climates, how they function in and respond to hot, arid climates is comparatively not well understood. Specifically, large atmospheric water losses via evaporation and plant transpiration comprise a much larger component of the whole-system water budget than in mesic climates. Additionally, given the primary role that emergent macrophytes play in nit

openCC0Dec 2023View details →

Long-Term Field Research Sites at Harvard Forest since 1937

The Harvard Forest is an iconic field station, hosting hundreds of field-based studies since it was founded in 1907. Site-based, long-term research increased sharply starting in 1988, when Harvard Forest became a Long-Term Ecological Research Site. This dataset documents the locations of most long-term research studies initiated since 1988; a few studies that began earlier and are still active are also included. Short-term studies, or studies that focus on sampling individual organisms, are not included.

openCC (other)Jun 2025View details →

Zooplankton abundance from net tows on Northeast U.S. Shelf Long Term Ecological Research (NES-LTER) Transect cruises, ongoing since 2018

This data package provides abundance data for zooplankton collected during seasonal transect cruises conducted as part of the Northeast U.S. Shelf Long-Term Ecological Research (NES-LTER) program, ongoing since 2018. Zooplankton are collected at standard NES-LTER transect stations (L1–L11) and the Martha’s Vineyard Coastal Observatory (MVCO) via oblique tows, using a 61-cm Bongo net with two mesh sizes (335 µm and 150 µm). The transect extends southward from near Martha’s Vineyard, Massachusetts, reaching approximately 150 km offshore along longitude 70 deg 53 min W, covering the continental shelf from nearshore to the shelf break, with sampling depths between 20 and 200 meters. Only the 335-µm mesh data is included here, as samples from this net are preserved on board and shipped to Morski Instytut Rybacki in Szczecin, Poland, where they are counted and identified to the lowest possible taxonomic level. Counts of taxa identified are provided by the NOAA’s Northeast Fisheries Science Center. Samples from the 150 um are preserved for other purposes and will be published as a separate data package. This second version of the data package includes staged and unstaged abundance data in volumetric (100 m³) and aerial (10 m²) units from the 335-µm net. Supplemental tables provide metadata for the cruises and stations.

openCC (other)Dec 2022View details →

Mississippi River spatial water chemistry Environmental Research Letters datasets

We mapped surface water chemistry along the entire length of the Upper Mississippi River (UMR) to understand spatial patterns in nitrate sources and processing. We used a sensor-based and boat-mounted sensing platform to continuously measure underway water chemistry. Measurements were linked with global positioning systems (GPS) to create maps of surface water chemistry. Here, we archive data associated with an Environmental Research Letters publication (Loken et al. 2018). Data include a single spatial survey of the entire length of the UMR (Minneapolis, Minnesota to Cairo, Illinois) in August 2015 and repeat surveys in Navigation Pool 8 (located near La Crosse, WI). Data have been provided in three formats (raw, hydraulic-corrected, and tau-corrected). Additionally, we archive laboratory chemistry data from water samples collected during the project. Sites include a range of main channel, backwaters, and tributaries. Water chemistry samples were analyzed at the North Temperate Lakes - Long Term Ecological Research facility and linked with underway sensor measurements.

openCC (other)Jan 2026View details →

Merged discrete water-column data from PAL LTER research cruises along the Western Antarctic Peninsula, from 1991 to 2024.

Water samples are collected throughout the water column along the Western Antarctic Peninsula at regular LTER grid stations where CTD casts are preformed and in surface waters at underway stations, where CTD casts are not done, using the ship's flow-through seawater system. This dataset is the compilation of water samples collected at these stations, merged from other PAL-LTER datasets. Data includes water column Chlorophyll and Phaeopigment concentrations; phytoplankton accessory pigments -including other chlorophyll's (e.g. chlorophyll b), xanthophylls, and carotenes; primary production rates; bacterial production; dissolved organic carbon; particulate organic carbon and nitrogen; dissolved inorganic nutrients; dissolved oxygen, and dissolved inorganic carbon and alkalinity. Measurements of phytoplankton Fv/Fm measured using a FIRe (Fluorescence Induction and Relaxation) fluorometer are also included, though caution is urged as FIRe data has not been corrected nor QCed. Conductivity, temperature, depth, and associated data from instruments on the sampling rosette (e.g. PAR, beam transmission) for each sampling depth are also included. Phytoplankton accessory pigment data is unavailable for the LMG10-01 cruise due to instrumentation problems and for the LMG12-01 cruise due to a freezer failure which resulted in the loss of samples. Dissolved oxygen measurements were discontinues after 2012, and thus no data is available from 2013 onwards. Dissolved Organic Carbon data is unavailable after 2012 due to instrumentation problems. There is no Particulate Organic Carbon data for cruise PD94-01.

opencc-by-4.0Aug 2020View details →

Data presented in Devenish and Cerminara, Journal of Geophysical Research Atmosphere, 2021. doi:10.1029/2020JD033699

The files contain the raw data of the atmospheric and concentration profiles respectively used and calculated by the LES and LSM simulations presented in Devenish and Cerminara (2020). The concentration data have been stored in two ASCII columns, the first being the elevation with respect to the vent level, and the second the concentration normalised by the initial concentration, where the initial concentration is the product of the source mass flux and the exit velocity. For the two cases of the intercomparison study, the initial mass flux is 1.5e6 kg/s and 1.5e9 kg/s for the weak and strong cases, respectively. The respective exit velocities are 135 m/s and 275 m/s. For the twenty cases with ambient wind, the initial mass flux and exit velocities can be extracted from the information given in the paper. Additional information can be found in Costa et al. (2016) and Aubry et al. (2019).

opencc-by-4.0Nov 2023View details →

JasonAlongTrack: A reformatted version of the Integrated Multi-Mission Ocean Altimeter Data for Climate Research Version 5.1

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.This is a reformatted version of Beckley et al.'s Integrated Multi-Mission Ocean Altimeter Data for Climate Research complete time series Version 5.1 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>.   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.

opencc-by-4.0Mar 2024View details →

Research in Svalbard international projects edgelist

This dataset lists all the country to country ties derived from the <a href="https://www.researchinsvalbard.no/">Research in Svalbard</a> (RIS) database using the country of origin of the organisations with joint research projects in Svalbard and the projects year. This edgelist is broken down into two time periods: 1972-2004 ; 2005-2022. Per each pair of countries, it gives the number of joint research projects registered in the RIS database per period of time. It can be used for network analysis purposes. It has been created and analysed using a core-periphery approach within the publication: Strouk, M. & Maisonobe, M. (2024). "Field science and scientific collaboration in the Svalbard Archipelago: beyond science diplomacy". Science and Public Policy. DOI: <a href="https://doi.org/10.1093/scipol/scae012">https://doi.org/10.1093/scipol/scae012/</a>

opencc-by-4.0Nov 2024View details →

Quantitative Assessment of Research Data Management Practices - 2023

This survey investigates Research Data Management (RDM) practices across five Swiss higher education institutions, including EPFL, ETH Zürich, Eawag, FHNW, and DaSCH, with the goal of gathering insights into how researchers manage data and code throughout the lifecycle of their projects, as well as using such findings to inform academic services related to RDM for researchers. Previous surveys, conducted at EPFL in 2017, 2019, and 2021, primarily focused on the planning and publishing stages of the research data lifecycle, such as data management planning and open data dissemination. The 2023 edition expanded to other institutes and places a stronger emphasis on Active Data Management, particularly during research projects, including a range of topics such as: <ul> <li>Storage and backup solutions</li> <li>Data and code sharing platforms</li> <li>Documentation and metadata usage</li> <li>Compliance with legal and ethical standards</li> <li>Long-term data preservation strategies</li> <li>Use of open formats and open-source software</li> <li>Adoption of Data Management Plans (DMPs)</li> </ul> This dataset was collected using the SurveyHero platform in compliance with GDPR and Swiss FADP regulations. enuvo GmbH acted as the data processor under a signed Data Processing Agreement. No personal identifiable information was purposefully collected, and data has been aggregated to further ensure respondents’ privacy. Included in this dataset: <ul> <li>A CSV and XLSX file with the aggregated, anonymized data from the survey.</li> <li>Two PDF files containing graphical representations of the survey results, automatically generated by the SurveyHero platform in portrait and landscape mode.</li> <li>A README file providing context.</li> </ul> This dataset is made openly available under the CC-BY 4.0 license. Users are encouraged to reuse it with appropriate attribution.

opencc-by-4.0Apr 2022View details →

Dataset Dental research data availability and quality according to FAIR principles

This dataset contains open access publications in EPMC dental journals from 2016 to 2021 and 500 non-open access dental publications. We evaluated the level of compliance with the FAIR principles. The original dataset and codebook are attached.

opencc-zeroDec 2020View details →

BIP! DB: A Dataset of Impact Measures for Research Products

<h2>Overview</h2> This dataset contains citation-based impact indicators (also referred as measures) for ~296M distinct persistent identifiers (PIDs) that correspond to various types of research products (publications, datasets, software, and other products). The calculated indicators are organized into categories based on the aspect of impact they capture.  <h3>Influence indicators</h3> Reflect the "total" impact of a research product; how established it is in general. <ul> <li>Citation Count: The total number of citations of the product, the most well-known influence indicator.</li> <li>PageRank score: 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> Capture the "current" impact of a research product; how popular it currently is. <ul> <li>RAM score: 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>AttRank score: 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> Measure the initial momentum that a research product received right after its publication. <ul> <li>Incubation Citation Count (3-year CC): 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> Capture the impact of a research product relative to the average performance in its field, accounting for differences in citation practices across disciplines. <ul> <li>Field-Weighted Citation Impact (FWCI): 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>3-year FWCI: 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> In our analysis, the expected number of citations for each research product is computed by grouping them by concept, publication year, and product type and then averaging the citations within each group.  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). <h2>Indicator calculation levels</h2> The impact indicators are calculated in two levels: <ul> <li>PID level:  assuming that each PID corresponds to a distinct research product. Currently PIDs are DOIs, PMCIDs, and PMIDs.</li> <li>OpenAIRE-id level: 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> Each researcj product is also assigned an impact class, reflecting its percentile rank among all products in the dataset:  <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;">Class</td> <td style="height: 19.5938px;">Percentile</td> <td style="height: 19.5938px;">Description</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> 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: <ul> <li> PID-level files: Each line follows the format: <code>identifier <tab> identifier_type <tab> score <tab> class</code> </li> <li> OpenAIRE-id-level files: These files contain the keyword "openaire_ids" in the filename. Each line follows the format: <code>identifier <tab> score <tab> class</code> </li> </ul> 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). <h3>Topic-related files</h3> In addition to the main indicator files, the dataset also includes topic-level outputs, providing field-weighted impact indicators as well percentile classes within the associated 2nd-level concepts from OpenAlex.  Specifically, we associated all research products with their 2nd level concepts from OpenAlex (using only their DOIs); 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. Since currently only the DOIs are used to associate concepts from OpenAlex to research products, all identifiers in these files refer to DOIs.  <ul> <li>Topic-specific impact classes file:  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> <code>identifier <tab> concept <tab> pagerank_class <tab> attrank_class <tab> 3-cc_class <tab> cc_class</code> <ul> <li>Field-weighted indicator files: Each line follows the format: <code>identifier <tab> concept <tab> score</code></li> </ul> 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. <h2>Data sources</h2> 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) OpenCitations' COCI & POCI dataset, (b) MAG (Sinha et al, 2015; Wang et al., 2019), and (c) Crossref. The union of all distinct citations that could be found in these sources have been considered.  Additionally, all topic-related computations are derived from OpenAlex concepts. <h2>Access and Use</h2> 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>.  Terms: These data are provided "as is", without any warranties of any kind. The data are provided under the CC0 license. <h2>Changelog</h2> v19.1 <ul> <li>[major update] Added field-weighted indicators: FWCI and 3-year FWCI.</li> </ul> v19.0 <ul> <li>Added PMCID as an additional type of PID.</li> </ul> v15.1 <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> v12.0 <ul> <li>Added PMIDs as an additional type of PID.</li> </ul> v10.0 <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> v9.0 <ul> <li>[Major update] Introduced topic-specific impact classes for PID-identified products based on OpenAlex 2nd-level concepts.</li> </ul> v7.0 <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> v5.1 <ul> <li>[Major update] Introduced dual-level score computation: PID level and OpenAIRE ID level.</li> </ul>

openCC (other)Nov 2021View details →

Herbivore dung and parasite counts, Ol Pejeta Conservancy and Mpala Research Centre, Kenya (2015-2018)

Data package contains two datasets of dung surveys, one dataset of parasite egg measurements, and two camera trap datasets collected from Mpala Research Centre and Ol Pejeta Conservancy, Laikipia County, Kenya from November 2015-September 2018. Datasets are provided as part of the publication `Watering sources aggregate parasites with increasing effects in more arid conditions`. Source data files for figures in the manuscript are also provided here.

openCC (other)Dec 2023View details →

Long-term (1935-2019) tree population data from remeasurements of a large network of permanent study plots in old-growth forest, Dukes Research Natural Area, Marquette Co., MI, USA

The Dukes Research Natural Area (Hiawatha National Forest, Marquette Co., MI) amounts to ca. 100 ha of minimally disturbed original forests, including a mix of mesic 'hemlock-northern hardwood' types and peaty wetlands dominated by several species of swamp conifers and black ash (Fraxinus nigra). The RNA hosts a regular grid of 250 0.2-acre (~0.08 ha) permanent monitoring (CFI) plots. This package includes tree censuses for subsets of CFI plots conducted in 1935, 1948, and 1974-1980, and repeated censuses with mapped stems from 1989 to 2019. This 84-year record constitutes one of the longest repeated-measurement, permanent-plot data-sets for old-growth temperate forest.

openCC (other)Oct 2025View details →

Leslie Holdridge arboretum tree census, La Selva Research Station, Organization for Tropical Studies, Sarapiquí, Heredia, Costa Rica, 1972-2017.

This database is a collection of dendrometric and structural measurements for all the trees in the arboretum, it was compiled through the assessment of 10 census from 1972 to 2017 by O. Vargas and E. Castro for the Organization for Tropical Studies. The 3.5-hectare Holdridge Arboretum is located at La Selva Research Station. Leslie R. Holdridge, the original owner of the property, created the arboretum in 1968. Initially, it was a small cacao grove with an exceptionally rich overstory of native shade trees. To facilitate research in the arboretum, staff later removed the cacao. In 1970, Gary Hartshorn continued to plant seedlings of many native tree species. OTS continues to plant, tag, and measure trees. OTS maintains the arboretum by regular mowing and pruning to facilitate safe access. Courses, natural history visitors, students, and researchers use the arboretum for a wide range of observational studies, manipulations, dendrological practices, and taxonomy classes.

openCC0Mar 2021View details →

Plant community data at water sources, Mpala Research Centre, Kenya (2015-2017)

Data package contains four datasets of plant measurements taken at Mpala Research Centre, Laikipia County, Kenya from November 2015-September 2017. Additional code for data analysis is also provided as part of the publication `The effects of herbivore aggregations at water sources on savanna plants differ across soil and climate gradients`.

openCC (other)Jan 2020View details →

Tussock watershed thaw depth survey summary for 1990 to present, Arctic Long-Term Ecological Research (LTER), Toolik Research Station, Alaska.

Thaw depth was measured since 1990 using a steel probe in the Tussock watershed just south of Toolik Lake, Alaska, on a gentle slope dominated by moist, non-acidic tussock tundra. At least two surveys are conducted each summer, on 2 July and on 11 August (plus or minus 1 day).

openCC (other)Dec 2015View details →

Chlorophyll a and primary productivity data for various lakes near Toolik Research Station, Arctic LTER. Summer 1983 to 1989.

Decadal file describing the chlorophyll a and primary production in various lakes near Toolik Research Station (68 38'N, 149 36'W) during summers from 1983 to 1989. Sample site descriptors include an assigned number (sortchem), site, date of analysis (incubation), time, depth and rates of primary production. The amount of chlorophyll a and pheophytin were also measured.