Find research datasets worth reusing

openOpenAug 2011View details →

Alaskan Peatland Experiment: Community structure and productivity data for 2007-2010 X - Leaf Area

This dataset contains surface area measurements for all vascular species present within a plot with an abundance greater than 5%. Surface area measurements at the fen include 2008-2010, while at the bog are for 2009-2010. Within the fen site a water table manipulation has been ongiong since 2005, with control, lowered and raised water table treatment plots. Samples at the bog were collected in a plot established within the lowland black spruce permafrost plateau (permafrost), and two plots established within collapse scars embedded within the plateau. One collapse scar formed ~ 45 years ago (old collapse) and the other formed ~ 25 years ago based upon aerial photography provided by the BCEF LTER. The data provided in this data set can be sorted by site and plot.

openCC0Mar 2024View details →

Plant aboveground biomass data: The influence of natural enemies on plant community composition and productivity

The purpose of this experiment is to determine the influences of natural enemies, including plant pathogenic fungi and insect pests, influence plant community composition, productivity, and diversity over time. The experiment is being conducted in an old field that is burned every other year. Within the old field, there are 8 blocks, and within each block there are 6 treatments: foliar fungicide, soil drench fungicide, foliar insecticide, mammal exclosure, the combination of all enemy suppression tactics (pesticides and mammal exclosure), and a nontreated control. The pesticides are applied repeatedly throughout the growing season. Within the plots, community productivity, species composition, percent cover, and pest damage are being quantified over time.

openCC0Mar 2024View details →

Plant aboveground biomass data: The influence of natural enemies on plant community composition and productivity

openCC0Aug 2022View details →

Instantenous rates of ecosystem carbon fluxes: The influence of natural enemies on plant community composition and productivity

opencc-by-4.0Feb 2021View details →

Net community production, nutrients, and hydrographic parameters in the South China Sea in October 2014 and June 2015

Net community production (NCP) corresponds to the difference between photosynthesis and respiration and can be estimated based on the dissolved oxygen to argon ratio (O2/Ar) in the mixed layer. NCP is also an important proxy for the biological pump in the ocean. In order to figure out the influencing factors on NCP in the northern slope region of the South China Sea (SCS), we conducted high-resolution underway measurements of O2/Ar and hydrographic parameters using membrane inlet mass spectrometry (MIMS) and multi-parameter water quality logger (RBR Maestro) in the northern slope region of the SCS in October 2014 and June 2015, assisted by nutrients measurements. NCP in the mixed layer was estimated using the biological supersaturation of O2/Ar, Δ (O2/Ar), and gas transfer velocity (k). All the underway data were compiled into the 5-min interval. Surface water samples for the nutrients analysis were collected from Niskin bottles mounted on the conductivity–temperature–depth (CTD) rosette at sampling stations, the nutrients were then photometrically determined by an auto-analyzer. The mixed layer depth (MLD) and the euphotic depth (Zeu) were calculated at stations where CTD casts were made based on potential density and chlorophyll fluorescence profile, respectively.

opencc-by-4.0Aug 2021View details →

Plankton community composition and productivity near the Subantarctic Prince Edward Islands archipelago in autumn

This data set, shows hydrographic (CTD) and biogeochemical (Chl-a, nitrate+nitrite, nitrite, ammonium and silicic acid concentrations) parameters, nitrate uptake rates and net primary production, egg production rates (Calanus simillimus), Pheophytin-a concentrations in zooplankton guts, and phytoplankton and zooplankton abundances and biomass from the Prince Edward Islands archipelago in the Indian Sector of the Subantarctic Ocean, during April-May 2017.

opencc-by-4.0Feb 2022View details →

Viroplant Project - Microcosm studies on the effect of bacteriophages used as plant protection products on soil microbial communities

This file contains the description, data and DNA analyses on the effect of bacteriophages with a potential to be used as plant protecction products on the structure and function of soil microbial communities. The objective was to evaluate two different microcsom incubation systems with phages and microbial cells from soil, or soil itself and to analyses in a time dependent manner how the phages affect the natural soil microbiomes. The microbial communities were quantified with qPCR and their diversity analyzed with PCR amplified 16S rRNA gene sequences. Bioinformatic analyses were used to evaluate microbial community responses

opencc-by-4.0Feb 2022View details →

Dataset for Anomaly Detection in a Production Wireless Mesh Community Network

CSV dataset generated gathering data from a production wireless mesh community network. Data is gathered every 5 minutes during the interval 2021-04-13 00:00:00 to 2021-04-16 00:00:00. During the interval 2021-04-14 02:00:00 2021-04-14 17:50:00 (both included) there is the failure of a gateway in the mesh (nodeid 24).  Live mesh network monitoring link: <a href="http://dsg.ac.upc.edu/qmpsu">http://dsg.ac.upc.edu/qmpsu</a> The dataset consists of single gzip compressed CSV file. The first line of the file is a header describing the features. The first column is a GMT timestamp of the sample in the format as "2021-03-16 00:00:00".  The rest of the columns provide the comma-separated values of the features collected from each node in the corresponding capture. A suffix with the nodeid is added to each feature. For instance, the feature having the number of processes of node with nodeid 24 is named as "processes-24". In total, 63 different nodes showed up during the samples, each being assigned a different nodeid. Features are of two types: (i) absolute values, for instance, the CPU 1-minute load average, and (ii) counters that are monotonically increased, for instance the number of transmitted packets. We have converted counter-type kernel variables to rates, by dividing the difference between two consecutive samples, over the difference of the corresponding timestamps in seconds, as shown in the following pseudo-code:   feature.rate are columns computed from feature as     feature.rate <- (feature[2:n]-feature[1:(n-1)])/(epoch[2:n]-epoch[1:(n-1)])     feature.rate <- feature.rate[feature.rate >= 0] # discard samples where the counter is restarted    where n is the number of samples features - processes     number of processes - loadavg.m1     1 minute load average - softirq.rate     servicing softirqs - iowait.rate     waiting for I/O to complete - intr.rate       - system.rate     processes executing in kernel mode - idle.rate     twiddling thumbs - user.rate     normal processes executing in user mode - irq.rate     servicing interrupts - ctxt.rate     total number of context switches across all CPUs - nice.rate     niced processes executing in user mode - nr_slab_unreclaimable     The part of the Slab that can't be reclaimed under memory pressure - nr_anon_pages     anonymous memory pages - swap_cache     Memory that once was swapped out, is swapped back in but still also is in the swapfile - page_tables     Memory used to map between virtual and physical memory addresses - swap       - eth.txe.rate     tx errors over all ethernet interfaces - eth.rxe.rate     rx errors over all ethernet interfaces - eth.txb.rate     tx bytes over all ethernet interfaces - eth.rxb.rate     rx bytes over all ethernet interfaces - eth.txp.rate     tx packets over all ethernet interfaces - eth.rxp.rate     rx packets over all ethernet interfaces - wifi.txe.rate     tx errors over all wireless interfaces - wifi.rxe.rate     rx errors over all wireless interfaces - wifi.txb.rate     tx bytes over all wireless interfaces - wifi.rxb.rate     rx bytes over all wireless interfaces - wifi.txp.rate     tx packets over all wireless interfaces - wifi.rxp.rate     rx packets over all wireless interfaces - txb.rate        tx bytes over all ethernet and wifi interfaces - txp.rate        tx packets over all ethernet and wifi interfaces - rxb.rate        rx bytes over all ethernet and wifi interfaces - rxp.rate        rx packets over all ethernet and wifi interfaces - sum.xb.rate    tx+rx bytes over all ethernet and wifi interfaces - sum.xp.rate    tx+rx packets over all ethernet and wifi interfaces - diff.xb.rate      tx-rx bytes over all ethernet and wifi interfaces - diff.xp.rate      tx-rx packets over all ethernet and wifi interfaces

opencc-zeroMay 2024View details →

Data from: Species richness and evenness of European bird communities show differentiated responses to measures of productivity

Understanding patterns of species diversity is crucial for ecological research and conservation, and this understanding may be improved by studying patterns in the two components of species diversity, species richness and evenness of abundance of species. Variation in species richness and evenness has previously been linked to variation in total abundance of communities as well as productivity gradients. Exploring both components of species diversity is essential because these components could be unrelated or driven by different mechanisms. The aim of this study was to investigate the relationship between species richness and evenness in European bird communities along an extensive latitudinal gradient. We examined their relationships with latitude and Net Primary Productivity, which determines energy and matter availability for heterotrophs, as well as their responses to territory densities (i.e., the number of territories per area) and community biomass (i.e., the bird biomass per area). We applied a multivariate Poisson log-normal distribution to unique long-term, high-quality time-series data, allowing us to estimate species richness of the community as well as the variance of this distribution, which acts as an inverse measure of evenness. Evenness in the distribution of abundance of species in the community was independent of species richness. Species richness increased with increasing community biomass, as well as with increasing density. Since both measures of abundance were explained by NPP, species richness was partially explained by energy-diversity theory (i.e., the more energy, the more species sustained by the ecosystem). However, species richness did not increase linearly with NPP but rather showed a unimodal relationship. Evenness was not explained either by productivity nor by any of the aspects of community abundance. This study highlights the importance of considering both richness and evenness to gain a better understanding of variation in species diversity. We encourage the study of both components of species diversity in future studies, as well as use of simulation studies to verify observed patterns between richness and evenness.

opencc-by-4.0Jul 2024View details →

Biomass production of experimental grassland communities in urban context within LandKlif project

Mixtures of grassland communities (three different compositions) were sown in urban experimental areas to compare in biodiversity and functioning to standard urban lawns (56 plots, 2 x 4 m each) in Munich and Weihenstephan in 2020. Two samples (20 x 20 cm) per experimental plot were clipped in August 2021, sorted by functional type, oven-dried and weighed. For integrity of the database, all field experimental units of this study are associated to Plot ID 7835_1_U, but the variable PlotID is deprecated. LandKlif is funded by the Bavarian State Ministry of Science and the Arts within the Bavarian Climate Research Network (bayklif).  Within the five year funding period of bayklif, five interdisciplinary senior research associations and five junior research groups are be financed with a total sum of 18 million Euro. LandKliF, as one of the five interdisciplinary senior research associations, addresses the effects of climate change on biodiversity and ecosystem services in semi-natural, agricultural and urban landscapes.

opencc-by-4.0Aug 2019View details →

Interspecific social interactions shape public goods production in natural microbial communities

The R code (Hesse_etal.R) descibes step by step how metal polution affects the ecology and evolution of a community-wide public good – the production of metal-detoxifying siderophores. This code accompanies the manuscript "Interspecific interactions shape public goods production in natural microbial communities" (https://www.biorxiv.org/content/10.1101/710715v1). The R code is divided into different sections per figure (1-5 and supplementary Figure S1). Each section first starts with reading in the appropriate data files (cvs files) and continues with statistics and plotting of figures.

opencc-by-4.0Jul 2020View details →

Figure 3 in Comparison of soil invertebrate communities in organic and conventional production systems in Southern Brazil

Figure 3. Non-metric multidimensional scaling (NMDS) plot showing the relationship between macrofauna taxa (black text) and soil chemical and physical properties (red text) of samples taken in four land-use system in Quitandinha, Brazil. NF = Native forest, OH = Organic horticulture, RT = Reduced tillage, CH = Conventional horticulture.

opencc-by-4.0Oct 2024View details →

Data for "Temporal and vertical variability in phytoplankton primary production and microbial community respiration in the North Pacific Subtropical Gyre"

This ALOHA_GOP&R.xslx data set provides measurements of biological rates conducted between April 2015 and July 2020 at different depths in the euphotic zone at or in the vicinity of Station ALOHA (22° 45' N, 158° W), the long-term sampling site of the Hawaii Ocean Time-series (HOT) program, within the North Pacific Subtropical Gyre.  The file ALOHA_GOP&R.xslx contains incubation-based measurements of gross oxygen production and community respiration that were measured in the same incubation bottles by applying the 18O-water method and tracking net changes in oxygen to argon ratios during dawn to dusk in situ incubations, following Ferrón et al. (2016). The samples were measured using membrane inlet mass spectrometry. Rates were measured at 6 depths within the euphotic zone: 5, 25, 45, 75, 100, 125 m, except in a few occasions in which there were no measurements made at 125 m. Data description <table> <tbody> <tr> <td> Variable </td> <td> Description </td> <td> Units </td> </tr> <tr> <td> Date  </td> <td> Date of sampling and start of incubation (UTC -10 hours) </td> <td>   </td> </tr> <tr> <td> Cruise ID </td> <td> Cruise identification </td> <td> # </td> </tr> <tr> <td> Latitude </td> <td> Latitude </td> <td> degrees N </td> </tr> <tr> <td> Longitude </td> <td> Longitude </td> <td> degrees E </td> </tr> <tr> <td>Stn ALOHA </td> <td>Whether the data are from Station ALOHA (yes/no)</td> <td> </td> </tr> <tr> <td>IncT</td> <td> Incubation time </td> <td> hours </td> </tr> <tr> <td> Depth </td> <td>Nominal depth of sampling and incubation </td> <td> meters </td> </tr> <tr> <td>GOP</td> <td>Gross oxygen production </td> <td>mmol O2 m-3 d-1</td> </tr> <tr> <td>CR</td> <td>Estimate of community respiration </td> <td>mmol O2 m-3 d-1</td> </tr> <tr> <td> Flag GOP </td> <td>Flag identification for gross oxygen production (good=1,questionable=2)</td> <td> # </td> </tr> <tr> <td> Flag CR </td> <td> Flag identification for community respiration (good=1,questionable=2) </td> <td> # </td> </tr> </tbody> </table>   The Light-dark_ALOHA_rates.xlsx file contains metabolic rates measured by the ligth-dark oxygen method between June 2005 and June 2007 at different depths in the euphotic zone at Station ALOHA (22° 45' N, 158° W). Rates of net community production, communnity respiration, and gross oxygen production were measured at 6 depths within the euphotic zone (5, 25, 45, 75, 100, 125 m)  in dawn to dawn incubations, following Williams et al. (2004).  Data description <table> <tbody> <tr> <td> Variable </td> <td> Description </td> <td> Units </td> </tr> <tr> <td> HOT </td> <td>HOT cruise number</td> <td> # </td> </tr> <tr> <td> Date </td> <td>Date of sampling and start of incubation (UTC -10)</td> <td>   </td> </tr> <tr> <td> Depth </td> <td>Nominal depth of sampling and incubation </td> <td> meters </td> </tr> <tr> <td>GOP</td> <td>Gross oxygen production, average of 8 replicates</td> <td>mmol O2 m-3 d-1</td> </tr> <tr> <td>GOP SE</td> <td>Gross oxygen production standard error </td> <td>mmol O2 m-3 d-1</td> </tr> <tr> <td>CR</td> <td> Dark community respiration, average of 8 replicates </td> <td> mmol O2 m-3 d-1 </td> </tr> <tr> <td> CR SE </td> <td>Dark community respiration standard error</td> <td> meters </td> </tr> <tr> <td>NCP</td> <td>Net community production,average of 8 replicates </td> <td>mmol O2 m-3 d-1</td> </tr> <tr> <td>NCP SE</td> <td>Net community production standard error </td> <td>mmol O2 m-3 d-1</td> </tr> </tbody> </table>

opencc-by-4.0Dec 2022View details →

Contribution of tree community structure to forest productivity across a thermal gradient in eastern Asia

These CSV and R script files are the dataset and codes used for the analysis in the following <a href="https://www.nature.com/articles/s41467-023-36671-1">journal paper</a>: Kohyama, T.I., Sheil, D., Sun, IF. et al. Contribution of tree community structure to forest productivity across a thermal gradient in eastern Asia. Nat Commun 14, 1113 (2023). https://doi.org/10.1038/s41467-023-36671-1   Contents <ul> <li>d0.csv — Individual tree-stem size data obtained by two censuses in 60 forest plots in eastern Asia <ul> <li><code>plot_id</code> — Plot ID</li> <li><code>species</code> — Scientific name</li> <li><code>t1</code> — Year of the first census</li> <li><code>t2</code> — Year of the second census</li> <li><code>dbh1</code> — Stem diameter (cm) at the first census*1</li> <li><code>dbh2</code> — Stem diameter (cm) at the second census*1</li> <li><code>w1</code> — Estimated above ground biomass (Mg C ha−1) at the first census</li> <li><code>w2</code> — Estimated above ground biomass (Mg C ha−1) at the first census</li> <li><code>wl1</code> — Estimated leaf biomass (Mg C ha−1) at the first census</li> <li><code>wl2</code> — Estimated leaf biomass (Mg C ha−1) at the second census</li> </ul> </li> </ul>   <ul> <li>d1.csv — Species-level biomass, productivity and other turnover rates in each of 60 forest plots in eastern Asia <ul> <li><code>plot_id</code> — Plot ID</li> <li><code>t1</code> — Year of the first census</li> <li><code>t2</code> — Year of the second census</li> <li><code>species</code> — Scientific name</li> <li><code>N</code> — Period mean number of stems (ha−1)</li> <li><code>B</code> — Period mean above ground biomass (Mg C ha−1)</li> <li><code>Bl</code> — Period mean leaf biomass (Mg C ha−1)</li> <li><code>p</code> — Relative above ground biomass productivity rate (year−1)</li> <li><code>l</code> — Relative above ground biomass loss rate (year−1)</li> <li><code>P</code> — Absolute above ground biomass productivity rate (Mg C ha−1 year−1)</li> <li><code>L</code> — Absolute above ground biomass loss rate (Mg C ha−1 year−1)</li> <li><code>pl</code> — Relative leaf biomass productivity rate (year−1)</li> <li><code>ll</code> — Relative leaf biomass loss rate (year−1)</li> <li><code>Pl</code> — Absolute leaf biomass productivity rate (Mg C ha−1 year−1)</li> <li><code>Ll</code> — Absolute leaf biomass loss rate (Mg C ha−1 year−1)</li> <li><code>w_max</code> — Period mean above ground biomass of the largest tree (Mg C ha−1)</li> <li><code>w_99</code> — The 99-th percentaile of tree above ground biomass (Mg C ha−1)</li> <li><code>rgr_max</code> — Relative growth rate of the largest tree (year−1)</li> </ul> </li> </ul>   <ul> <li>plot_metadata.csv — Metadata (e.g. location and climate variables) for 60 forest plots in eastern Asia <ul> <li><code>plot_id</code> — Plot ID</li> <li><code>latitude</code> — Latitude in decimal degrees (°)</li> <li><code>longitude</code> — Longitude in decimal degrees (°)</li> <li><code>elevation</code> — Elevation (m)</li> <li><code>area</code> — Plot area (ha)</li> <li><code>MAT</code> — Mean annual temperature (°C)*2</li> <li><code>AP</code> — Annual precipitation (mm year−1)*2</li> <li><code>PET</code> — Potential evapotranspiration (mm year−1)*2</li> </ul> </li> </ul>   <ul> <li>annual_litterfall.csv — Annual fine litterfall (i.e. canopy productivity) obtained by monthly litterfall records collected by litter traps during same census period in 22 forest plots <ul> <li><code>plot_id</code> — Plot ID</li> <li><code>Plitter</code> — Annual litterfall production (Mg C ha−1 year−1)</li> </ul> </li> </ul>   <ul> <li>max_tree_height.csv — Tallest tree height for 388 species in 11 forest plots <ul> <li><code>plot_id</code> — Plot ID</li> <li><code>species</code> — Scientific name</li> <li><code>H_max</code> — tallest tree height (m)</li> </ul> </li> </ul>   <ul> <li>productivity.r — R script for estimating forest-level aboveground net productivity</li> </ul>   *1 No-record diameters due to death in the second census and pre-recruitment in the first census were set to zero. *2 Climate data for the period 1981–2010 were obtained from CHELSA version 2.1 (Krager et al. 2021 EnviDat, https://doi.org/10.16904/envidat.228.v2.1)

opencc-by-4.0Feb 2023View details →

An archive of net community production estimates derived from autonomous profiler observations at Ocean Station Papa

This dataset contains an archive of marine net community production estimates for Ocean Station Papa, derived using autonomous profilers, including BGC-Argo floats and sea gliders. The archive contains data from the following original publications: <ol> <li>Haskell, W. Z., Fassbender, A. J., Long, J. S., and Plant, J. N.: Annual Net Community Production of Particulate and Dissolved Organic Carbon From a Decade of Biogeochemical Profiling Float Observations in the Northeast Pacific, Global Biogeochemical Cycles, 34, 1–22, <a href="https://doi.org/10.1029/2020GB006599">https://doi.org/10.1029/2020GB006599</a>, 2020.</li> <li>Huang, Y., Fassbender, A. J., Long, J. S., Johannessen, S., and Bernardi Bif, M.: Partitioning the Export of Distinct Biogenic Carbon Pools in the Northeast Pacific Ocean Using a Biogeochemical Profiling Float, Global Biogeochemical Cycles, 36, <a href="https://doi.org/10.1029/2021gb007178">https://doi.org/10.1029/2021gb007178</a>, 2022.</li> <li>Pelland, N. A., Eriksen, C. C., Emerson, S. R., and Cronin, M. F.: Seaglider Surveys at Ocean Station Papa: Oxygen Kinematics and Upper-Ocean Metabolism, Journal of Geophysical Research: Oceans, 123, 6408–6427, <a href="https://doi.org/10.1029/2018JC014091">https://doi.org/10.1029/2018JC014091</a>, 2018.</li> <li>Plant, J. N., Johnson, K. S., Sakamoto, C. M., Jannasch, H. W., Coletti, L. J., Riser, S. C., and Swift, D. D.: Net community production at Ocean Station Papa observed with nitrate and oxygen sensors on profiling floats, Global Biogeochemical Cycles, 30, 859–879, <a href="https://doi.org/10.1002/2015GB005349">https://doi.org/10.1002/2015GB005349</a>, 2016.</li> <li>Yang, B., Emerson, S. R., and Bushinsky, S. M.: Annual net community production in the subtropical Pacific Ocean from in-situ oxygen measurements on profiling floats, Global Biogeochemical Cycles, 31, 728–744, <a href="https://doi.org/10.1002/2016GB005545">https://doi.org/10.1002/2016GB005545</a>, 2017.</li> </ol>   Dataset description: Separate Matlab (.mat) and NetCDF (.nc) files are provided for each original publication dataset. At minimum, each contains time (recorded as absolute days since 01 Jan 2000, 00:00:00), and time-explicit depth-integrated NCP. Additional fields include depth-resolved NCP, and water column depth (Plant et al., 2016; Pelland et al., 2018). Note that the Haskell et al. (2020) dataset (haskell20.mat, haskell20.nc) contains climatological NCP estimates. Accordingly, the time field represents the Year-day only.  All NCP units are recorded in units of mmol O2/m2/d (depth-integrated) or mmol O2/m3/d (depth-resolved). Additional information on each dataset is contained within the data files. Additional processing information is contained in Izett et al. (under review).   The data were compiled in:  Izett, R. W., Fennel, K., Stoer, A. C., and Nicholson, D. P. Reviews and syntheses: Expanding the global coverage of gross primary production and net community production measurements using BGC-Argo floats, Submitted to Biogeosciences. 2023.   Please cite as: Izett, R., Haskell, W., Huang, Y., Pelland, N., Plant, J., and Yang, B. 2023. An archive of net community production estimates derived from autonomous profiler observations at Ocean Station Papa. Database. Zenodo. doi: 10.5281/zenodo.7667521. Accessed on DATE.

opencc-by-4.0Jul 2023View details →

Primary data for: "Remotely sensed localised primary production anomalies predict the burden and community structure of infection in long-term rodent datasets"

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publicMay 2024View details →

Data from: Species richness and evenness of European bird communities show differentiated responses to measures of productivity

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publicNov 2024View details →

Infauna shift trait-productivity relationships in submerged aquatic vegetation communities

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