Find research datasets worth reusing

opencc-by-4.0Apr 2023View details →

The NGC Catalog, an exercise of data collection for UOC

This project aims to download the NGC object catalog using webcrapping techniques and save it as a CSV file. The accessed URL is <a href="https://in-the-sky.org/data/catalogue.php?cat=NGC&const=1&obj1Type=0&sort=0&view=1&page=x">https://in-the-sky.org/data/catalogue.php?cat=NGC&const=1&obj1Type=0&sort=0&view=1&page=x</a> with 1 to 79 pages. The downloaded contents for each item compund the catalog are: Name = Name in the catalog type = Object kind mag = Visual magnitude of the object dist = Distance from the earth (when available) constellation = Constellation where to find the object RA = RA coordinates of the object. DEC = DEC Coordinates of the object str_name = Other name from the object. object_image = The image of the object.

opencc-by-4.0Mar 2023View details →

WEFE data collection for a pilot smallholder farm in Costa Rica

In-situ and remote data collection of environmental and socioeconomic data for the planning of integrated water, energy, food, and environment systems for the case study of a smallholder farm in Costa Rica.

opencc-by-4.0Aug 2023View details →

Data of the INFORMS Journal on Computing paper: Routing replenishment workers: The prize collecting traveling salesman problem in scattered storage warehouses

In what follows, you will find data of the paper: "Routing replenishment workers: The prize collecting traveling salesman problem in scattered storage warehouses" published in INFORMS Journal on Computing List of files: - Computational_results_BB_NN_RW_CPLEX.xlsx: Excel file that gives all results - instance_gen.cc: Instance generator - instances.zip: compressed file of all instances that are sorted by Sections. It additionally includes the generator - Makefile: Makefile for compiling/debugging, i.e., "make all" or "make debug" do the jobs - MersenneTwister.h: needed by schedule_finder.cc - results_Section_5_1.zip: compressed file of all output files of Section 5.1 - results_Section_5_2.zip: compressed file of all output files of Section 5.2 - results_Section_5_3.zip: compressed file of all output files of Section 5.3 - schedule_finder.cc: Main program containing the B&B, the S-shape, and Nearest Neighbor procedure (see details for customizing the parameters at the top of this file) - valgrind_debug.txt: Only contains the used debug command instances/instance_gen.cc generates a problem instance in file problems.txt The structure of the these problem files is the following: /* NE                                              Total number of experiments given by the currently considered file -2                                              Separator EXPGRP                                          Index of the current experiment group the current experiment belong to N                                               Number of vacant positions in the warehouse M                                               Number of requests to be stored by the tour P                                               Number of pickers to be scheduled in the warehouse A                                               Number of vertical aisles B                                               Number of horizontal (cross) aisles L_A                                             Length of each vertical aisle L_B                                             Length of each cross aisle UF_VA                                           Up-factor of each vertical aisle (A values) DF_VA                                           Down- factor of each vertical aisle (A values) UF_CA                                           Up-factor of each cross aisle (B values) DF_CA                                           Down- factor of each cross aisle (B values) x_pos_vertical_aisle                            x-position of vertical aisle (A values) y_pos_cross_aisle                               y-position of cross aisle (B values) warehouse_graph values                          For each node of the warehouse graph all entries (15 each) are given (total_number_of_warehouse_graph_nodes*15)    FS << warehouse_graph[curr_node].free_position << " " << endl;    FS << warehouse_graph[curr_node].depot_node << " " << endl;    FS << warehouse_graph[curr_node].vertical_aisle << " " << endl;    FS << warehouse_graph[curr_node].cross_aisle << " " << endl;    FS << warehouse_graph[curr_node].pred_cross_aisle << " " << endl;    FS << warehouse_graph[curr_node].succ_cross_aisle << " " << endl;    FS << warehouse_graph[curr_node].pred_vertical_aisle << " " << endl;    FS << warehouse_graph[curr_node].succ_vertical_aisle << " " << endl;    FS << warehouse_graph[curr_node].pred_cross_aisle_dist << " " << endl;    FS << warehouse_graph[curr_node].succ_cross_aisle_dist << " " << endl;    FS << warehouse_graph[curr_node].pred_vertical_aisle_dist << " " << endl;    FS << warehouse_graph[curr_node].succ_vertical_aisle_dist << " " << endl;    FS << warehouse_graph[curr_node].region << " " << endl;    FS << warehouse_graph[curr_node].x_position << " " << endl;    FS << warehouse_graph[curr_node].y_position << " " << endl; shortest_path_distance                          For each combination of nodes (i.e., for total_number_of_warehouse_graph_nodes_square combinations) the distance shortest_path_length_including_start_and_end    For each combination of nodes (i.e., for total_number_of_warehouse_graph_nodes_square combinations) the number of visited nodes shortest_path_visited_nodes                     For each combination of nodes (i.e., for total_number_of_warehouse_graph_nodes_square combinations) the detailed path (length is respectively given by shortest_path_length_including_start_and_end) dd_free_position                                For each free position and the depot (here with index N) the due date is transferred (N+1 values) (only relevant for the extended problem, is ignored here) weight_of_free_position                         For each free position and the depot (here with index N) the weight is transferred (N+1 values) (only relevant for the extended problem, is ignored here) capacity_of_free_position                       For each free position the storage capacity transferred (N values) -2      Separator indicating the end of an instances -3      Separator indicating the end of all experiments (i.e., indicating the end of the file) */ output files (results_Section_5_1.zip/results_Section_5_2.zip/results_Section_5_3.zip): results_BB_NXXX_MYYY_A10_B05:  Output file of applying B&B results_RW_NXXX_MYYY_A10_B05:  Output file of applying s-shape random walk results_NN_NXXX_MYYY_A10_B05:  Output file of applying nearest neighbor In these files you find all outputs of schedule_finder.cc.   Among others, you will find the generated tour schedules (for Experiment with index I) in the output files by searching the phrase: "Experiment I completed with result=" or for the next Experiment " completed with result=" Example (results_BB_N030_M150_A10_B05.txt, experiment 0, the tardiness values are to be ignored, see comments in schedule_finder.cc) Pos 0 depot node with index 80 Number of stored items 0 CT 0  No tardiness Pos 1 position 27 Number of stored items 4 Current accumulated number of stored items 4 CT 163 DD 5629 No additional tardiness Pos 2 position 28 Number of stored items 5 Current accumulated number of stored items 9 CT 399 DD 2962 No additional tardiness Pos 3 position 29 Number of stored items 4 Current accumulated number of stored items 13 CT 670 DD 12631 No additional tardiness Pos 4 position 26 Number of stored items 5 Current accumulated number of stored items 18 CT 840 DD 10142 No additional tardiness Pos 5 position 23 Number of stored items 7 Current accumulated number of stored items 25 CT 1134 DD 6000 No additional tardiness Pos 6 position 22 Number of stored items 4 Current accumulated number of stored items 29 CT 1170 DD 6396 No additional tardiness Pos 7 position 16 Number of stored items 5 Current accumulated number of stored items 34 CT 1448 DD 8962 No additional tardiness Pos 8 position 11 Number of stored items 10 Current accumulated number of stored items 44 CT 1674 DD 6336 No additional tardiness Pos 9 position 0 Number of stored items 10 Current accumulated number of stored items 54 CT 2062 DD 1141 Additional tardiness 921 Pos 10 position 2 Number of stored items 9 Current accumulated number of stored items 63 CT 2201 DD 7742 No additional tardiness Pos 11 position 4 Number of stored items 5 Current accumulated number of stored items 68 CT 2407 DD 4846 No additional tardiness Pos 12 position 3 Number of stored items 5 Current accumulated number of stored items 73 CT 2717 DD 2316 Additional tardiness 401 Pos 13 position 1 Number of stored items 8 Current accumulated number of stored items 81 CT 2876 DD 9917 No additional tardiness Pos 14 position 6 Number of stored items 3 Current accumulated number of stored items 84 CT 3073 DD 7299 No additional tardiness Pos 15 position 5 Number of stored items 8 Current accumulated number of stored items 92 CT 3144 DD 6152 No additional tardiness Pos 16 position 8 Number of stored items 6 Current accumulated number of stored items 98 CT 3337 DD 3705 No additional tardiness Pos 17 position 12 Number of stored items 9 Current accumulated number of stored items 107 CT 3452 DD 7622 No additional tardiness Pos 18 position 13 Number of stored items 4 Current accumulated number of stored items 111 CT 3522 DD 7833 No additional tardiness Pos 19 position 14 Number of stored items 5 Current accumulated number of stored items 116 CT 3647 DD 9877 No additional tardiness Pos 20 position 19 Number of stored items 1 Current accumulated number of stored items 117 CT 3922 DD 2905 Additional tardiness 1017 Pos 21 position 20 Number of stored items 5 Current accumulated number of stored items 122 CT 3923 DD 2538 Additional tardiness 1385 Pos 22 position 21 Number of stored items 7 Current accumulated number of stored items 129 CT 3976 DD 2769 Additional tardiness 1207 Pos 23 position 18 Number of stored items 10 Current accumulated number of stored items 139 CT 4173 DD 3552 Additional tardiness 621 Pos 24 position 25 Number of stored items 4 Current accumulated number of stored items 143 CT 4482 DD 11710 No additional tardiness Pos 25 position 24 Number of stored items 7 Current accumulated number of stored items 150 CT 4509 DD 10599 No additional tardiness Pos 26 visiting the node with index 80 Number of stored items 0 CT 4710 DD 10893 No additional tardiness opt_makespan=4710 opt_total_tardiness=5552 TSP_procedure returned value 4710 Experiment 0 completed with result=3 BFS Branch&Bound report: Consumed time: 1 Copied from schedule_finder.cc: Note that the procedure used as a solution procedure in the paper is int TSP_procedure(struct bb_node *curr_bb_node, int version) It is called by BB_procedure() as a subroutine for computing a lower bound value of an extended problem (for instance, this extended problem additionally covers due dates. Therefore, due dates are also part of the problem instances, but can be ignored) Specifically, TSP_procedure(struct bb_node *curr_bb_node, int version) is called once by lb_computation()

opencc-by-4.0Aug 2023View details →

Data and scripts for collective intelligence research (arXiv:2204.13424)

This is the data and scripts for the study From Prediction Markets to Interpretable Collective Intelligence by Alexey V. Osipov and Nikolay N. Osipov (<a href="http://doi.org/10.48550/arXiv.2204.13424">arXiv:2204.13424</a> [cs.GT])

opencc-by-4.0Sep 2023View details →

MCU data in a Cypress 65 nm SRAM from heavy ions and protons collected at ground facilities

The dataset contains the raw MCU data collected at ground facilities under heavy ion and proton irradiation in the scope or RADSAGA and RADNEXT project. The device under consideration is the CY62167GE30-45ZXI, a 65 nm commercial SRAM available from Infineon (formerly Cypress). Note that the internal ECC has been disabled for this data collection. More information on data collection are available through this paper (<a href="https://doi.org/10.1109/REDW51883.2020.9325822">10.1109/REDW51883.2020.9325822</a>). The MCU were determined through the procedure explained in these two papers (<a href="https://doi.org/10.1109/TNS.2014.2313742">10.1109/TNS.2014.2313742</a> and <a href="https://doi.org/10.1109/TNS.2015.2496874">10.1109/TNS.2015.2496874</a>).

opencc-by-4.0Sep 2023View details →

Science, Technology & Society Eurobarometers 1993 - 2021: Trend Data Collection

These files contain structured collections of Eurobarometer (EB) survey data from 1993 to 2021, focusing on European citizen’s views on science and technology (S&T). The primary aim of these data collections is to facilitate research on trends over time regarding people’s knowledge, perception and attitudes towards S&T. The European Union has collected extensive survey data from the general public over the past 50 years through its official polling instrument, the Eurobarometer. Its general goal is monitoring the state of public opinion on diverse subjects and issues throughout Europe, one of which is S&T. The data collection files, provided in the folder ‘EB_data_csv’, include data from seven different Eurobarometer surveys. These surveys were selected because their raw datasets were openly accessible through Open EU Datasets. To ensure sufficient data points for plotting specific trends over time, we included only survey questions that appeared in at least three different EB surveys.

openCC0Aug 2022View details →

Data and code for EDI overview paper, data collection characteristics, FAIR evaluation, downloads, and citations

The Environmental Data Initiative (EDI) is a trustworthy, stable data repository and data management support organization for the environmental scientist. EDI provides tools and support that allow the environmental researcher to easily integrate data publishing into the research workflow. Almost ten years since going into production, these data and code were used to provide a general description of EDI’s collection of data and its data management philosophy and placement in the repository landscape. They show how comprehensive metadata and the repository infrastructure lead to highly findable, accessible, interoperable, and reusable (FAIR) data by evaluating compliance with specific community proposed FAIR criteria. Finally, they provide measures and patterns of data (re)use, assuring that EDI is fulfilling its stated premise.

Pre- and Post- Hurricane Maria Dry Soil Collection (GUAN, LAJA) (repackaging of occurrences published by the NEON Biorepository Data Portal)

These samples from Soil Collection (Distributed Periodic) (NEON-SOIC-DP) were collected at NEON GUAN and LAJA sites in Puerto Rico before (July and November 2017) and after (July 2018) Hurricane Maria, a Category 5 storm that affected the Carribean and occurred September 16, 2017 – October 2, 2017.

NEON Biorepository Aquatic Microalgae Collection (Chemical Preservation) (repackaging of occurrences published by the NEON Biorepository Data Portal)

This collection contains subsamples of aquatic microalgae preserved in either glutaraldehyde or a high-iodine Lugol's solution (NEON sample class: ptx_taxonomy_in.preserved). Periphyton and phytoplankton samples are collected three times per year at wadeable stream, river, and lake sites during aquatic biology bout windows, roughly in spring, summer, and fall. Benthic samples are collected using the most appropriate sampler for the habitat and substratum type, including rock scrubs, grab samples, and epiphyton. In wadeable streams, periphyton samples are collected in the two most dominant benthic habitat types (e.g. riffles, runs, pools, step pools), and seston samples were collected from the water column near the S2 sensor (seston samples were discontinued in 2018). In lakes, water-column phytoplankton samples are collected near the buoy and littoral sensors using a Kemmerer sampler, and in littoral areas using the best benthic sampling method for the dominant substratum type. In rivers, phytoplankton samples are collected near the buoy and two other deep-water locations using a Kemmerer or Van Dorn sampler, and in littoral areas using the best benthic sampling method for the dominant substratum type. All field-collected samples are split into subsamples in the domain support facility, preserved, and shipped to a contracting taxonomy laboratory where samples are further subsampled for analysis and archiving. All samples are archived in 20 mL glass scintillation vials and stored in a temperature (17-18°C) and humidity controlled environment. Phytoplankton and seston samples are preserved in a 2% high-iodine Lugol's solution from 2014-2020 and 0.5% glutaraldehyde starting in 2021. Periphyton samples are preserved in 0.5% glutaraldehyde. See related links below for protocols and NEON related data products.

NEON Biorepository Aquatic Microalgae Collection (Freeze-dried) (repackaging of occurrences published by the NEON Biorepository Data Portal)

This collection contains freeze-dried subsamples of aquatic microalgae (NEON sample class: ptx_taxonomy_in.freezeDried). Periphyton and phytoplankton samples are collected three times per year at wadeable stream, river, and lake sites during aquatic biology bout windows, roughly in spring, summer, and fall. Benthic samples are collected using the most appropriate sampler for the habitat and substratum type, including rock scrubs, grab samples, and epiphyton. In wadeable streams, periphyton samples are collected in the two most dominant benthic habitat types (e.g. riffles, runs, pools, step pools), and seston samples were collected from the water column near the S2 sensor (seston samples were discontinued in 2018). In lakes, water-column phytoplankton samples are collected near the buoy and littoral sensors using a Kemmerer sampler, and in littoral areas using the best benthic sampling method for the dominant substratum type. In rivers, phytoplankton samples are collected near the buoy and two other deep-water locations using a Kemmerer or Van Dorn sampler, and in littoral areas using the best benthic sampling method for the dominant substratum type. All field-collected samples are split into subsamples in the domain support facility, preserved, and shipped to a contracting taxonomy laboratory where samples are further subsampled for analysis and archiving. Freeze dried subsamples contained cleaned, freeze dried diatoms. Samples are archived in 20 mL glass scintillation vials and stored at room temperature. See related links below for protocols and NEON related data products.

NEON Biorepository Aquatic Microalgae Collection (Microscope Slides) (repackaging of occurrences published by the NEON Biorepository Data Portal)

This collection contains slide-mounted subsamples of aquatic microalgae (NEON sample class: ptx_taxonomy_in.slideID). Periphyton and phytoplankton samples are collected three times per year at wadeable stream, river, and lake sites during aquatic biology bout windows, roughly in spring, summer, and fall. Benthic samples are collected using the most appropriate sampler for the habitat and substratum type, including rock scrubs, grab samples, and epiphyton. In wadeable streams, periphyton samples are collected in the two most dominant benthic habitat types (e.g. riffles, runs, pools, step pools), and seston samples were collected from the water column near the S2 sensor (seston samples were discontinued in 2018). In lakes, water-column phytoplankton samples are collected near the buoy and littoral sensors using a Kemmerer sampler, and in littoral areas using the best benthic sampling method for the dominant substratum type. In rivers, phytoplankton samples are collected near the buoy and two other deep-water locations using a Kemmerer or Van Dorn sampler, and in littoral areas using the best benthic sampling method for the dominant substratum type. All field-collected samples are split into subsamples in the domain support facility, preserved, and shipped to a contracting taxonomy laboratory where samples are further subsampled for analysis and archiving. Algae specimens in this collection contain cleaned diatom subsamples that have been mounted on glass microscope slides and are archived at room temperature. See related links below for protocols and NEON related data products.

NEON Biorepository Benthic Microbe Collection (Sterivex Filters) (repackaging of occurrences published by the NEON Biorepository Data Portal)

This collection contains benthic biofilm samples collected on 67 mm long, 1.7 cm diameter, 0.22 um Sterivex capsule filters (NEON sample class: amb_fieldParent_in.archiveID). Benthic biofilm samples are collected 3 times per year at the same time and location as periphyton (microalgae) samples and microbe samples sent for sequencing analysis, three times per year in wadeable streams during aquatic biology bout windows, roughly in spring, summer, and fall. Benthic biofilms are not collected in lakes and rivers. Samples are collected from rock and wood scrubs using field-sterile methods, and filtered through a 0.22 um Sterivex SVGP capsule filters. In wadeable streams, periphyton samples are collected in the two most dominant benthic habitat types (e.g. riffles, runs, pools, step pools). Sterivex filters are capped and flash-frozen in the field and then shipped to the Biorepository to be archived at -80 degrees Celsius. See related links below for protocols and NEON related data products.

NEON Biorepository Particulate Mass Filter Collection (repackaging of occurrences published by the NEON Biorepository Data Portal)

This collection contains quartz microfiber particulate mass filters (NEON sample class: dpm_fieldData_in.sampleID and dpm_filterBlank_in.sampleID). Particulate mass sampling is executed at six of NEON's terrestrial sites, located in Domains 10, 13, and 15. The subset of sites included for sampling are those in the Basin and Range, Eastern and Western slopes of the Rocky Mountains, and the Eastern plains of Colorado. This selection of sites enables focus on transportation of particulate matter from the Great Basin and the Colorado Plateau by prevailing westerly winds over the Colorado Rocky Mountains, to receptor sites in the Rockies and Great Plains. Samples are collected by an automated assembly that pulls air through a quartz microfiber filter with a porosity of 10 micrometers, to collect PM10. Filters are weighed at high precision pre- and post-deployment at the Colorado Department of Public Health and Environment Air Resources Laboratory to determine dust deposition mass. Filters are then shipped to the Biorepository to be archived at 4 degrees Celsius in air-tight plastic sleeves. Subsamples of the filters are available to the science community upon request to enable the assessment of chemical and nutrient inputs in the region. Additionally, 5 filter blanks from each box of filters are archived and available upon request. See related links below for protocols and NEON related data products.

NEON Biorepository Soil Collection (Distributed Periodic) (repackaging of occurrences published by the NEON Biorepository Data Portal)

This collection contains air-dried soil samples collected during periodic soil sampling at NEON terrestrial sites (NEON sample class: sls_bgcSubsampling_in.bgcArchiveID). Soil biogeochemical samples are collected once every 5 years, with three unique sampling locations per plot and ten plots per site. Soil sampling is conducted to a maximum depth of 30 ± 1 cm where possible. When organic (O) and mineral (M) horizons are present within a single profile they are separated prior to analysis and archiving. However, other sub-horizons are not separated. Soil from the O horizon is homogenized and non-soil material is removed by hand (no sieving), whereas soil from the M horizon is homogenized and sieved to 2 mm. Prior to archiving, all soil samples are air-dried, then placed into glass jars and stored at room temperature. See links below for NEON data products that provide various physical, chemical, and biological measurements (pH, moisture, carbon and nitrogen content and stable isotopes, inorganic nitrogen pools and net transformation rates, microbial community composition and biomass) for these same soils. In addition, a more detailed characterization of the dominant soil types at each site, including taxonomy, texture, bulk density, and geochemical properties, occurred during the construction period of NEON through two projects. These data are available in NEON data products Soil physical and chemical properties, distributed initial characterization (DP1.10047.001) and Soil physical and chemical properties, Megapit (DP1.00096.001).

NEON Biorepository Soil Microbe Collection (Bulk Subsamples) (repackaging of occurrences published by the NEON Biorepository Data Portal)

This collection contains samples collected during periodic soil sampling and frozen at ultra-low temperatures in order to provide material for microbial sequencing or other microbial analyses (NEON sample classes: sls_soilCoreCollection_in.geneticArchiveSample1ID, sls_soilCoreCollection_in.geneticArchiveSample2ID, sls_soilCoreCollection_in.geneticArchiveSample3ID, sls_soilCoreCollection_in.geneticArchiveSample4ID, sls_soilCoreCollection_in.geneticArchiveSample5ID,sls_metagenomicsPooling_in.compositeSampleID). Archive samples are collected during each soil sampling bout and are promptly frozen. Three unique locations are sampled per plot, with ten plots per site. Bouts occur three times per year in order to capture the prevailing conditions at the site during different seasons, except in Alaska where only 1 bout is possible. Soil sampling is conducted to a maximum depth of 30 ± 1 cm, and when organic (O) and mineral (M) horizons are present within a single profile, they are separated prior to analysis and archiving. However, other sub-horizons are not separated. During the majority of bouts, only the top horizon (O if present, else M) is collected and archived. Soils are homogenized and non-soil material is removed by hand in the field, then subsamples are immediately frozen on dry ice. They are maintained in ultra-low temperature freezers until shipment to the Biorepository. See links below for NEON data products that provide physical, chemical, and biological measurements for these same soils (soil pH and moisture are always measured; chemical properties as well as microbial community composition and biomass are determined only for a subset of collection bouts). In addition, a more detailed characterization of the dominant soil types at each site, including taxonomy, texture, bulk density, and geochemical properties, occurred during the construction period of NEON through two projects. These data are available in NEON data products Soil physical and chemical

NEON Biorepository Soil Microbe Collection (DNA Extracts ) (repackaging of occurrences published by the NEON Biorepository Data Portal)

This collection contains genetic extracts from soil microbes collected during periodic soil sampling at NEON terrestrial sites (NEON sample class: mic_dnaExtraction_in.soilDnaSampleID). Three unique locations are sampled per plot with ten soil plots per site. Bouts occur three times per year in order to capture the prevailing conditions at the site during different seasons, except in Alaska where there only 1 bout is possible. However, the frequency of genetic analysis and thus DNA archiving varies by site type. Soil sampling is conducted to a maximum depth of 30 ± 1 cm, and when organic (O) and mineral (M) horizons are present within a single profile, they are separated prior to analysis and archiving. However, other sub-horizons are not separated. During the majority of bouts, only the top horizon (O if present, else M) is analyzed for genetic content. Soils are homogenized and non-soil material is removed by hand in the field, then subsamples are immediately frozen on dry ice. They are maintained in ultra-low temperature freezers until they are shipped to an analytical facility for DNA extraction, sample preparation and sequencing. During peak greenness bouts, subsamples from each of the 3 sampling locations per plot are combined to form a plot-level composite that is used for metagenomics analysis. Laboratory metadata are delivered to NEON for QC testing and acceptance, and then formatted for upload to public sequence repositories. Genetic extracts are shipped from the analytical facility in 96-well plates to the NEON Biorepository to be archived at -80 degrees Celsius. See links below for NEON data products that provide physical, chemical, and biological measurements for these same soils (soil pH, moisture, and microbial properties are always measured; chemical properties are determined only for a subset of collection bouts). The metabarcoding protocol used by Battelle Applied Genomics is available in the NEON document library (https://data.neonscience.org/docu

NEON Biorepository Surface Water Microbe Collection (Sterivex Filters) (repackaging of occurrences published by the NEON Biorepository Data Portal)

This collection contains surface water microbe samples collected on 67 mm long, 1.7 cm diameter, 0.22 um Sterivex capsule filters (NEON sample class: amc_fieldCellCounts_in.archiveID). Surface water microbe samples are collected at the same time and location as surface water cell count samples and surface water chemistry samples once per month in wadeable streams (12 times per year) and every-other month in lakes and rivers (6 times per year). Details on sampling locations and timing are provided in the NEON document titled Surface Water Chemistry Sampling in Aquatic Habitats (https://data.neonscience.org/documents). In wadeable streams, surface water microbe samples are collected near the downstream S2 sensor location. In lakes, microbial samples are collected near the the 'buoy', 'littoral 1', and 'littoral 2' sensors, and sampling depth(s) is dependent on lake stratification. In rivers, microbial samples are collected near the buoy sensor. Water samples are filtered on 0.22 um Sterivex capsule filters, capped and flash-frozen in the field. Sterivex filters are archived at the NEON Biorepository at -80 degrees Celsius. See related links below for protocol.

NEON Biorepository Wet Deposition Collection (repackaging of occurrences published by the NEON Biorepository Data Portal)

This collection contains wet deposition samples collected during precipitation events at NEON terrestrial and aquatic sites (NEON sample class: wdp_collection_in.chemSubsampleID). Samples are collected in a climate controlled wet deposition collector located at the tower top of terrestrial sites, and at the meteorologic tower of select aquatic sites. The automated assembly detects precipitation with an optical sensor and opens to collect wet deposition during all rain events. This allows for all types of precipitation to enter the glass collection bottles located within the enclosure. Once precipitation has ceased (as detected by the optical precipitation detector), the retractable lid closes until the next precipitation event is detected. Every two weeks samples are retrieved. A portion of the sample is filtered and sent to an analytical facility for analysis of major ions, pH, and conductivity. The remaining (unused) portion of the sample is not filtered and archived at 4 degrees Celsius for five years. Samples are stored in plastic Nalgene bottles, either PP or HDPE. See related links below for protocols and NEON related data products. Please note that associated datasets include important remarks and notes from the analysis laboratory about the condition of each sample (for example, if debris or contaminants were observed in the sample).

openCC (other)Jun 2023View details →

Decomposition of Microstegium vimineum litter, plants grew through the Big Oaks National Wildlife Refuge in 2019. Litter used in this experiment naturally senesced in the fall 2019, decomposition data collected through 2020. Plants were infected or not-infected with the foliar fungal pathogen Bipolaris gigantea during the 2019 growing season.

Decomposition of plant litter, facilitated primarily by microbial decomposers, plays a critical role in biogeochemical cycling and ecosystem function. Emerging pathogens have the potential to impact litter decomposition by altering the chemical composition and associated microbial community of host tissue. Here, we compared litter decomposition of the invasive grass Microstegium vimineum collected from sites with Bipolaris leaf spot symptoms and sites with no apparent disease symptoms in a common garden experiment. Our results revealed that leaf tissue from litter from non-infected sites decomposed more rapidly through the spring than litter from infected sites. Differences in fungal composition between infected and non-infected litter at the start of the experiment largely persisted through the summer. Our work demonstrates that pathogen colonization may facilitate the persistence of infected host litter, potentially slowing the return of nutrients to the environmental pool while also promoting the survival and dispersal of primary inoculum the following season.