Find research datasets worth reusing

opencc-by-4.0Mar 2024View details →

Database to: Cover crops affect pool specific soil organic carbon in cropland – A meta‐analysis

Database to a meta-analysis studying the effects of cover crops on the mineral-associated organic carbon pool (MAOC), the particulate organic carbon pool (POC) and the microbial biomass carbon pool (MBC). Consists of: 1. information on the database 2. legend 3. list of included studies, all extracted data necessary for response ratio calculation and moderator analysis, and additional information

opencc-by-4.0Dec 2020View details →

Dataset: An Empirical Analysis of Pool Hopping Behavior in the Bitcoin Blockchain

We provide the first empirical analysis of pool hopping behavior among 15 mining pools throughout Bitcoin's history. Bitcoin mining is a critical activity that keeps the Bitcoin system secure, valid, and stable. Mining pools have emerged as major players that ensure that the Bitcoin system stays secure, valid, and stable. Individual miners join mining pools to benefit from a more stable and predictable income. Many questions remain open regarding how mining pools have evolved throughout Bitcoin's history and when and why miners join or leave mining pools. We propose a heuristic algorithm to extract the payout flow from mining pools and detect the pools' migration of miners. Our results showed that reward rules and pool fees influence miners' decisions to join, change, or exit from a mining pool, thus affecting the dynamics of mining pool market shares. Our analysis provides evidence that mining activity becomes an industry as miners' decisions follow classical economic rationale.

opencc-by-4.0Jul 2022View details →

Southern African Power Pool GridPath Model Input Data

This data repository holds GridPath model input data for the paper Chowdhury, A.K., Deshmukh, R., Wu, G., Uppal, A., Mileva, A., Curry, T., Armstrong, L., Galelli, S., and Kudakwashe, N. (2022) “Enabling a low-carbon electricity system for Southern Africa”, Joule. See Readme for more details.

opencc-by-4.0Oct 2023View details →

A Translation Table to Convert ERA5 Parameter IDs used in DKRZ's data pool into GRIB Codes

In DKRZ's data pool on the HPC Levante, ERA5 parameters are referred to as 3-digit parameter ID. This 3-digit parameter ID ("PARAM") is derived from the WMO GRIB parameter table. PARAM <=256 : 3-digit parameter ID of GRIB table 128 256 < PARAM <= 512 : 256 + last 3-digits of parameter ID of GRIB table 228 For example, PARAM=259 corresponds to "friction velocity" which has the parameter ID 003 in GRIB table 228. In the Excel table ERA5ParamsDKRZ_YYYYMMDD.xlsx, all ERA5 parameters are listed and explained.

openmit-licenseMar 2018View details →

Data and Statistical analysis for: "Predator in the pool? A quantitative evaluation of non-indexed open access journals in aquaculture research"

Data and Statistical analysis for: "Predator in the pool? A quantitative evaluation of non-indexed open access journals in aquaculture research" published in Frontiers in Marine Science

opencc-by-4.0Nov 2018View details →

Catalogue of Stepped Pools

This dataset lists, describes, and provides relevant bibliography to all known stepped pools in Galilee from the Hellenistic to Byzantine periods that have been exposed through archaeological excavations or field surveys. It forms part of the dataset used in the book Being Jewish in Galilee, 100–200 CE: An Archaeological Study (Brepols). The dataset is available in both PDF and CSV format. The PDF file provides a detailed description of and bibliography for each stepped pool. The CSV file contains the raw data that can be easily imported into spreadsheets and databases.

opencc-by-4.0Aug 2024View details →

Example Dataset for npstat: Population genetics from Pooled NGS data NPStat v1: User guide

Example Dataset for npstat to test the program and the different options. The example dataset contains a pileup file with sequences of of the 2L chromosome from fifteen pooled inbreed individuals of Drosophila melanogaster (doi: 10.1038/nature10811). The dataset also contains the sequence reference of the 2L chromosome  in fasta format, an outgroup sequence in fasta format of D. yakuba (SRR26246471), a GFF3 annotation file and a file with a brief list of selected SNPs to be analyzed.

opencc-by-4.0Sep 2024View details →

Seefeld Cold-Air Pool Experiment (SEECAP): WRF Simulation Output with snow cover January 16 2020 0000 UTC to January 17 2020 1200 UTC

The Seefeld Cold-Air Pool Experiment (SEECAP) focused on the cross-country skiing area Olympiaregion Seefeld and in particular the topographic setting in the Nordic ski arena which favors the formation of cold-air pools and took place between December 2019 and March 2020. The measurement data are described in Rudolph (2022) and Rauchöcker et al. (2024d) and meteorological measurement data associated with SEECAP are published in Rauchöcker et al. (2024c). This upload contains WRF simulation output data for the night between January 16 and January 17 2020 with snow cover and the plotting routines to reproduce the figures in Rauchöcker et al. (2024d). The night between January 16 and January 17 2020 initially featured ideal condition for cold-air pool formation followed by a disturbance around midnight. There is also an upload with simulation output for the same night, but without snow cover (Rauchöcker et al., 2024a). Also available in a different dataset are data from a simulation with snow cover for the night between January 12 and January 13 2020 (Rauchöcker et al., 2024b), which featured an undisturbed cold-air pool for almost the entire night. This case was considered to feature in Rauchöcker et al. (2024d), but a different case was chosen because some measurement data was not available during this period. <h3>WRF Simulation Output</h3> This Dataset includes data generated with WRFlux v1.4.1 (Göbel et al.,  2022), a fork of the Weather Research and Forecasting model WRF (Skamarock et al. 2021).  WRFlux allows to calculate the contribution of different processes to the potential temperature tendency at each grid point. The data published here is from the innermost simulation domain with 40m horizontal resolution and 10m vertical resolution close to the surface. The simulations were initialized at 00:00 UTC January 16 2020 and run until 12:00 UTC January 17 2020. Three different simulations were performed: two simulations with modified snow cover as described in Rauchöcker (2022), one each with the MYNN 2.5-order and the SMS-3DTKE PBL parameterizations (a scheme that blends a PBL scheme and a LES subgrid parameteriztion in the greyzone of turbulence), and one without snow cover with the MYNN 2.5-order PBL parameterization. Otherwise the simulations were identical. This dataset includes the two simulation with snow cover, where  jan126_sms.zip contains the files relating to the simulations with the SMS-3DTKE scheme and jan16.zip those for the simulation with the MYNN 2.5-order PBL parameterization. A detailed description of the model setup can be found in Rauchöcker et al (2024d) and in the files namelist.input and namelist_sms.input that were used for the simulations.  Standard WRF output can be found in wrfout_40m_jan16 and wrfout_40m_jan16_sms. The mean wind speed components, which were necessary to rotate the tendencies in a coordinate system that is aligned with the valley orientation, are contained in windout_40m_jan16 and windout_40m_jan16_sms. These variables were contained in the  unprocessed output files produced by WRFlux; the full files were unfortunately too large to be included here. The postprocessed tendencies are stored in tend_40m_jan16.nc and tend_40m_jan16_sms.nc. <h3>Plotting routines</h3> Python scripts and environment files to reproduce most figures in Rauchoecker et al. (2024d) are included in code.zip. To reproduce plots involving measurement data, which is available in Rauchöcker et al. (2024c), is also needed. Due to conflicts between some packages, two different environment were needed. To reproduce Figure 2, install the orthoplot environment by running "conda env create orthoplot.yml" in a terminal window, activate it  ("conda activate orthoplot") and then run ortho_plot.py. All other plots require the wrfstuff environent (installed by running "conda env create wrfstuff.yml" and activated by "conda activate wrfstuff") and are produced by paper_plots.py. Functions used to load data and plot the figures are included in dataload.py and plotting_routines.py, respectively. Two variables decide which figures are plotted for which dataset: dataname and doplot. The variable dataname defines the path to the dataset that should be used to produce the figures, while the value doplot defines which figure to reproduce. By setting doplot="fig1", Figure 1 is reproduced, while doplot="fig7" and doplot="fig9" reproduce Figures 7 and 9, respectively. For all other values for doplot, Figures 4, 5, 6, 8 and 11 are reproduced. We decided not to include a script to plot Figure 3 because the data the climatology is based on is owned by GeoSphere Austria - the agency operating the permanent weather station. Further, no script for reproducing Figure 10 is included because it was not created within the framework of Python. <h3>Geofiles</h3> The files included in geofiles.zip are needed to plot Figure 1 and Figure 2, although not of particular interest on their own. Included are output files from geogrid.exe, whicih are necessary to plot the domains overview (Figure 1), as well as an orthophoto (orthophoto.tif) and high-resolution digital elevation model (topo_hr.tif) which are both based on data from Land Tirol.

opencc-by-4.0Feb 2023View details →

Dataset for the "a parameterization for cloud organization and propagation by evaporation-driven cold pools edges"

When the negatively buoyant air in the cloud downdrafts reaches the surface, it spreads out horizontally, producing cold pools. A cold pool can trigger new convective cells. However, when combined with the ambient vertical wind shear, it can also connect and upscale them into large mesoscale convective systems (MCS). Given the broad spectrum of scales of the atmospheric phenomenon involving the interaction between cold pools and the MCS, a parameterization was designed here. Then, it is coupled with a classical convection parameterization to be applied in an atmospheric model with an insufficient spatial resolution to explicitly resolve convection and the sub-cloud layer. A new scalar quantity related to the deficit of moist static energy detrained by the downdrafts mass flux is proposed. This quantity is subject to grid-scale advection, mixing, and a sink term representing dissipation processes. The model is then applied to simulate moist convection development over a large portion of tropical land in the Amazon Basin in a wet and dry-to-wet 10-days period. Our results show that the cold pool edge parameterization improves the organization, longevity, propagation, and severity of simulated MCS over the Amazon and other different continental areas.

opencc-by-4.0Mar 2023View details →

Data from: Is a community state reachable, and why?, and Coexistence and collapse: an experimental investigation of the persistent communities of a protist species pool

Deterministic models have difficulties to take into account stochasticity during community assembly. As a tool to circumvent this problem, we present a qualitative discreteevent model, where consequences of interspecific interactions are described as rules. This model provides a map of all possible future dynamics for a given system, which allows to exhaustively describe the possible pathways during an assembly process. Such a description does not rely on species traits details and is insensitive to stochastic effects. This allows to show that subsets of species are sometimes impossible to reach starting from larger sets of species, and therefore to question the reachability of community states during the system’s dynamics. Applying the model to an experimental dataset studying the collapse of protist communities, we obtain a very good theory-experiment agreement. We finally discuss what the notion of reachability can bring to community assembly.

openCC0Apr 2022View details →

Dataset from University of Idaho 2004, master's thesis [Littoral ecology of epilithic algae in the Rocky Reach Pool, Mid-Columbia River (Washington State) - The effects of reservoir fluctuations.]

(Abstract from thesis) Epilithic algae, water column physical/chemical properties, and sediments were examined in the impounded Mid-Columbia River including the Rocky Reach Reservoir. Primary objectives included determination of the effects reservoir drawdown has on epilithic algae and potential nutrient enrichment via sediment. Epilithic algae were analyzed by pigment concentration, gravimetrically, and species composition. Reservoir elevation fluctuated at higher rates at tailrace sites (0.41-0.25 m/hr) compared to the forebay site (0.06-0.08 m/hr). Littoral exposure times were also greater at tailrace sites (mean of 8 hrs compared to 0 hr at the forebay site). Mean epilithic algae monochromatic chlorophyll a over all sampling periods at mainstem sites was 76.7 ± 4.8 mg/m2 (95 % C.I.). Epilithic algae monochromatic chlorophyll a in the zone of water fluctuation (0-1 m) was less at Wells tailrace (38.8 mg/m2) compared to Rocky Reach forebay (141.3 mg/m2) during summer, 2000 and 2001. Mean epilithic biofilm ash-free oven-dry weight over all sampling periods at mainstem sites was 25.6 ± 1.5 g/m2 (95 % C.I.). Mean autotrophic index across all mainstem locations was 439 indicating a large heterotrophic component within the epilithic biofilms. Epilithic algae communities were dominated by diatoms (50.2 %) and cyanobacteria (35.9 %), with some green algae (13.8 %). Canonical correlation analysis indicated that temperature, depth, site, and the water elevation change rate were important controllers of epilithic algae chlorophyll pigments. Mean textural characteristics of dredged sediment were 51.1 % sand, 43.2 % silt, and 5.7 % clay. Mean organic matter content in this sediment was 4.1 %. The mean seston sedimentation rate across mainstem locations was 11.3 g m-2 d-1 and organic matter comprised 14.7 % of the material collected from the water column.

openCC (other)Dec 2023View details →

Adirondack Public Good Events Database: Natural Resource, Environmental, Economic and Recreation Policy and Common Pool Resources for a Social-Ecological System in Adirondack Park, New York, USA, 1760-2020.

I assembled this dataset from various published sources to evaluate how the social-ecological system (SES) in Adirondack Park, New York changed through time and the interplay of public goods (Common Pool Resources, CPRs), public land rules and private land rights, and related concepts over 260 years (1760-2020). The database was the basis for a doctoral dissertation titled "Blue Lining: Assessing the Resilience of Adirondack Park, New York Using Polycentricity and Panarchy Frameworks." The goal of the dissertation was to assess patterns and changes in institutional rules, actors and arrangements before and after establishment of the public Adirondack Forest Preserve in 1885 and Adirondack Park in 1892 as those actors and rules were modified and as both internal and external events influenced the SES as it moved through different phases of the adaptive cycle through space and time (see panarchy). Using the database, I identified which organizations and events contributed to natural resource and CPR policy. The dissertation can be downloaded here: https://experts.esf.edu/esploro/outputs/99917370604826.

openCC (other)Mar 2024View details →

Cold-air pooling characterization and forest composition, New England, USA

This dataset corresponds to a project investigating whether cold-air pooling influences forest composition and function. The data include hourly sub-canopy air temperatures (measured continuously via ibuttons) and forest forest composition data for 48 plots along 9 transects in 3 sites across New England, USA. The temperature data also include surface lapse rates and temperature gradients across transects, as well as a designation indicating the presence or absence of a temperature inversion. We found that sites with the most frequent temperature inversions also displayed vegetation inversions across slopes, with more cold-adapted species at low instead of high elevations.

openCC0Jul 2021View details →

Mohonk Preserve Amphibian and Water Quality Monitoring Dataset at 11 Vernal Pools from 1931-Present

"The Mohonk Preserve's Daniel Smiley Research Center has been monitoring species occupancy, reproductive success, and water quality of 11 vernal pools (Ski Loop, Bonticou, Terrace, Long Woodland Pool, Long Woodland Swamp, Oakwood, Sleepy Hollow, Hermits, North Mud Pond, Canaan, and Talus) on the Preserve each spring from April 1931 to May 2019 (present). This project aims to document changes in the reproductive behavior and phenology of amphibians and allow research access to historical, longitudinal records. The dataset is a paired record of amphibian occurence with environmental indicators spanning nearly 90 years of data collection. The dataset includes environmental conditions for the 730 sampling events associated with the species occurences with complete coverage air temperature and precipitation records and partial coverage for a variety of other weather and water quality measures. Species occurence data collection has included species identification and counts of live and dead adults, mated pairs, spermatophores, egg masses, juveniles, and tadpoles counts as well as a record of the level of frog calling. Weather conditions including precipitation, sky and wind codes; and water quality measurements including water temperature, pH, and depth. Collection of data was sporadic from 1931 - 1991 but has been collected consistently from 1991 to present. We also began monitoring dissolved oxygen, nitrate concentrations, and conductivity of the vernal pools using a YSI Sonde Professional Plus Instrument and turbidity using a turbidity tube in February 2018. The data collection is ongoing, as are digitization efforts, and the data package will receive periodic updates."

openOpenOct 2015View details →

Effects of permafrost thaw on nitrogen availability and plant-soil interactions in a boreal Alaskan lowland: VI - Soil Nitrogen Pools

This dataset examines shifts in soil N and extractable soil pore water chemistry along a lowland boreal permafrost thaw chronosequence. Data was collected in 2013 at the APEX Beta and forested study plots, located adjacent to the BNZ experimental forest. Measured parameters include: dissolved inorganic N, dissolved organic N, free amino acids, total dissolved N, C:N of soil material, soil bulk density, soil N concentration, soil C concentrations, volumetric soil moisture. Soil pore water samples were collected every three to four weeks from late June to late September, 2013 for a total of five sampling events, while soil cores were collected in late July 2013 for physical characterstics and soil C and N concentrations.

openCC0May 2023View details →

PCN01 Plant and soil carbon and nitrogen pool data from the Belowground Plot Experiment at Konza Prairie

Data describe the carbon and nitrogen pools in combustible aboveground litter, and in shoots, roots, litter, and soil at the end of the growing season at the Belowground Plot Experiment in 2021.

CAT 4.6 taxonomic binning of the CAMI 2 Mouse Gut Toy data set, gold standard pooled assembly

Taxonomic binning of the gold standard pooled assembly Software: CAT SoftwareVersion: 4.6 DataURL: https://data.cami-challenge.org/participate SoftwareURL: https://github.com/dutilh/CAT ReferenceDatabase: prebuilt 2018-12-12 Taxonomy: NCBI 2018-12-12 ShortReadsUsed: False LongReadsUsed: False CommandUsed: CAT contigs -c anonymous_gsa_pooled.fasta -d CAT_prepare_20181212/2018-12-12_CAT_database/ -t CAT_prepare_20181212/2018-12-12_taxonomy/ --tmpdir tmp --nproc 16

PhyloPythiaS+ 1.4 taxonomic binning of the CAMI 2 Mouse Gut Toy data set, gold standard pooled assembly

Taxonomic binning of the gold standard pooled assembly Software: PhyloPythiaS+ SoftwareVersion: 1.4 DataURL: https://data.cami-challenge.org/participate SoftwareURL: https://github.com/algbioi/ppsp DockerImage: cami/ppsp:1.4 IsBiobox: False ReferenceDatabase: RefSeq 93, SILVA 132 Taxonomy: NCBI 2018-02-26 ShortReadsUsed: False LongReadsUsed: False CommandUsed: run_ppsp.py --pipelineDir ppsp_pipepline --inputFastaFile anonymous_gsa_pooled.fasta --databaseFile ncbi_taxonomy --refSeq refseq93 --s16Database SILVA_132 --mgDatabase reference_NCBI201502/mg5

MetaBAT 2.12.1 genome binning of the CAMI 2 Mouse Gut Toy data set, samples 0-63, gold standard pooled assembly

Genome binning of the gold standard pooled assembly Software: MetaBAT SoftwareVersion: 2.12.1 DataURL: https://data.cami-challenge.org/participate SoftwareURL: https://bitbucket.org/berkeleylab/metabat ShortReadsUsed: True LongReadsUsed: False CommandUsed: bowtie2-build anonymous_gsa_pooled.fasta anonymous_gsa_pooled.fasta for i in {0..63}; do bowtie2 -q --threads 30 --fr -x anonymous_gsa_pooled.fasta --interleaved sample_${i}/anonymous_reads.fq -S anonymous_reads_sample_${i}.sam ; done for i in {0..63}; do samtools view -b sample_${i}.sam -o anonymous_reads_sample_${i}.bam & done for i in {0..63}; do samtools sort anonymous_reads_sample_${i}.bam -o anonymous_reads_sample_${i}.sorted.bam ; done for i in {0..63}; do samtools index anonymous_reads_sample_${i}.sorted.bam ; done runMetaBat.sh -l anonymous_gsa_pooled.fasta anonymous_reads_sample_*.sorted.bam

Kraken 2.0.8 beta taxonomic binning of the CAMI 2 Mouse Gut Toy data set, gold standard pooled assembly

Taxonomic binning of the gold standard pooled assembly Software: Kraken SoftwareVersion: 2.0.8 beta DataURL: https://data.cami-challenge.org/participate SoftwareURL: https://ccb.jhu.edu/software/kraken2/ ReferenceDatabase: built 2019-05-22 Taxonomy: NCBI 2019-05-22 ShortReadsUsed: False LongReadsUsed: False CommandUsed: kraken2-build --standard --db kraken2db_std --use-ftp kraken2 --db kraken2db_std --threads 16 --output 19122017_mousegut_scaffolds.kraken --report 19122017_mousegut_scaffolds.kreport anonymous_gsa_pooled.fasta cat 19122017_mousegut_scaffolds | awk '{print $2 "\t" $3}' > 19122017_mousegut_scaffolds.cami