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zenodo44/100

Metagenome-assembled genomes from Stordalen Mire, Sweden (2019) (MAGs from long-read, short-read, & hybrid assemblies)

METHODS: Soil samples (6 total) were collected at the Stordalen Mire site in 2019 from two depths (1-5 & 20-24 cm below ground) across three habitats (Palsa, Bog, and Fen). DNA was extracted based on the protocol described by <a href="http://dx.doi.org/10.17504/protocols.io.yxmvm244bg3p/v1">Li et al. (2024)</a>. For short reads, libraries were prepared at the Joint Genome Institute (JGI) with the KAPA Hyperprep kit, and sequenced with Illumina NovaSeq 6000. For long reads, libraries were prepared with the SMRTbell Express Template Prep Kit 2.0 (PacBio), then sequenced using PacBio Sequel IIe at JGI. PacBio data was processed at JGI to form filtered CCS (Circular Consensus Sequencing) reads.  Assemblies were generated with short-only, long-only, and hybrid read sources: Short-only was assembled with <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5411777/">metaSPAdes</a> (v3.15.4) using <a href="https://zenodo.org/records/10806928">Aviary</a> (v0.5.3) with default parameters. Long-only was assembled with <a href="https://www.nature.com/articles/s41592-020-00971-x">metaFlye</a> (v2.9-b1768) using <a href="https://zenodo.org/records/10806928">Aviary</a> (v0.5.3) with default parameters. Hybrid assembly was performed using <a href="https://zenodo.org/records/10806928">Aviary</a> v0.5.3 with default parameters. This involved a step-down procedure with long-read assembly through <a href="https://www.nature.com/articles/s41592-020-00971-x">metaFlye</a> (v2.9-b1768), followed by short-read polishing by <a href="https://genome.cshlp.org/content/27/5/737">Racon</a> (v1.4.3), <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0112963">Pilon</a> (v1.24) and then Racon again. Next, reads that didn't map to high-quality metaFlye contigs were hybrid assembled with <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5411777/">SPAdes (--meta option)</a> and binned out with <a href="https://peerj.com/articles/7359/">MetaBAT2</a> (v2.1.5). For each bin, the reads within the bin were hybrid assembled using <a href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005595">Unicycler</a> (v0.4.8). The high-coverage metaFlye contigs and Unicycler contigs were then combined to form the assembly fasta file. Genome recovery was performed using <a href="https://zenodo.org/records/10806928">Aviary</a> v0.5.3 with samples chosen for differential abundance binning by <a href="https://zenodo.org/records/10939393">Bin Chicken</a> (v0.4.2) using <a href="https://zenodo.org/records/7130825">SingleM metapackage S3.0.5</a>. This involved initial read mapping through <a href="https://zenodo.org/records/10531254">CoverM</a> (v0.6.1) using <a href="https://academic.oup.com/bioinformatics/article/34/18/3094/4994778">minimap2</a> (v2.18) and binning by <a href="https://peerj.com/articles/1165/">MetaBAT</a>, <a href="https://peerj.com/articles/7359/">MetaBAT2</a> (v2.1.5), <a href="https://www.nature.com/articles/s41587-020-00777-4">VAMB</a> (v3.0.2), <a href="http://doi.org/10.1038/s41467-022-29843-y">SemiBin</a> (v1.3.1), <a href="https://zenodo.org/records/10460259">Rosella</a> (v0.4.2), <a href="https://www.nature.com/articles/nmeth.3103">CONCOCT</a> (v1.1.0) and <a href="https://academic.oup.com/bioinformatics/article/32/4/605/1744462">MaxBin2</a> (v2.2.7). Genomes were analyzed using <a href="https://www.nature.com/articles/s41592-023-01940-w">CheckM2</a> (v1.0.2) and clustered at 95% ANI using <a href="https://zenodo.org/records/10526086">Galah</a> (v0.4.0).   FILES: <ul> <li>EMERGE_MAGs_2019_long-short-hybrid.tar.gz - Archive containing the MAG files (.fna).</li> <li>metadata_MAGs_2019_EMERGE.tsv - Table containing source sample names and accessions, GTDB classifications, CheckM2 quality information, NCBI GenomeBatch- and MIMAG(6.0)-formatted attributes, and other metadata for the MAGs.</li> </ul>   FUNDING: This research is a contribution of the EMERGE Biology Integration Institute (<a href="https://emerge-bii.github.io/">https://emerge-bii.github.io/</a>), funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070. This study was also funded by the Genomic Science Program of the United States Department of Energy Office of Biological and Environmental Research, grant #s DE-SC0004632. DE-SC0010580. and DE-SC0016440. We thank the Swedish Polar Research Secretariat and SITES for the support of the work done at the Abisko Scientific Research Station. SITES is supported by the Swedish Research Council's grant 4.3-2021-00164. Data from the Joint Genome Institute (JGI) was collected under BER Support Science Proposal 503530 (DOI: <a href="https://doi.org/10.46936/10.25585/60001148">10.46936/10.25585/60001148</a>), conducted by the U.S. Department of Energy Joint Genome Institute (<a href="https://ror.org/04xm1d337">https://ror.org/04xm1d337</a>), a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.

opencc-by-4.0Nov 2024View details →

zenodo44/100

Nanoparticle clustering in supraparticles to control magnetic long-range interactions

This data publication is based on the metadata and datasets underlying the manuscript: Nanoparticle clustering in supraparticles to control magnetic long-range interactions To tailor superparamagnetic iron oxide nanoparticles (SPIONs) to the specific needs of diverse application fields, it is essential to understand not only their intrinsic properties but also their interactions with each other. Theoretical models predicting/explaining the magnetization behavior of macroscopic samples containing millions of SPIONs are intricate due to the complexity of the underlying relaxation mechanisms in alternating fields. This study introduces supraparticles (SPs) as model architectures to empirically investigate magnetic interactions within and between large SPION clusters (> 100 nanoparticles). For this purpose, nanoparticle dispersions containing SPIONs and silica nanoparticles (SiO2 NPs) as non‐magnetic building blocks are spray‐dried to form binary SPs. Selective salt‐induced agglomeration of the two building block types before spray‐drying is utilized to tailor SP architectures, including control over SPION cluster size, shape, and proximity. Magnetic particle spectroscopy (MPS), operating under ambient conditions, reveals altered magnetization behavior for different cluster structures. Not only the nearest SPION neighbors, but the whole cluster structure up to several micrometers is decisive for the magnetization behavior. This highlights the importance of long‐range magnetic interactions. This work presents a versatile approach for designing model architectures to advance empirical interaction studies between SPIONs in macroscopic samples.

opencc-by-4.0Jul 2024View details →

zenodo44/100

Dataset for the paper "Combining near-term benefits of climate adaptation with long-term benefits of emissions abatement"

This repo archives all data used in Duan et al. (2024), including model codes, raw model outputs, and post-processing scripts.  A Readme file describes the data and structure included here. If you have any questions, please contact the lead author (Lei Duan: leiduan@carnegiescience.edu).  We have updated the post-process codes to reflect changes in the revised manuscript == Paper associated with this dataset can be found at: https://www.nature.com/articles/s43247-024-01976-6#:~:text=Adaptation%20deployed%20in%20conjunction%20with,adaptation%20reducing%20near%2Dterm%20damage.

opencc-by-4.0Nov 2024View details →

zenodo44/100

Century Long Reconstruction of Gridded Phosphorus Surplus Across Europe (1850-2019)

<h4>Publication</h4> Please cite this publication if you use the dataset: Batool, M., Sarrazin, F. J. and  Kumar, R. Century Long Reconstruction of Gridded Phosphorus Surplus Across Europe (1850-2019), submitted to Earth System Science Data. Please also refer to the above publication for methodological details. <h4>License</h4> The "Century Long Reconstruction of Gridded Phosphorus Surplus Across Europe (1850-2019)" is freely available under an Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0, https://creativecommons.org/licenses/by-nc-sa/4.0), in compliance with the terms of use of the Food and Agriculture Organization of the United Nations (FAO). Data description (v1): This dataset consists of annual long-term reconstruction of total P surplus (both agricultural and non-agricultural soils) across Europe at a 5 arcmin spatial resolution for the period 1850 to 2019. The dataset consists of 48 P surplus estimates that account for the uncertainties resulting from methodological choices and coefficients in major components of the P surplus. This dataset offers the flexibility of aggregating the P surplus at any spatial scale of relevance to support water and land management strategies. Notably, our P surplus dataset has been developed consistently with our N surplus dataset (Batool et al. 2022), enabling joint analysis of N and P budgets across Europe, thereby facilitating holistic nutrient management studies.   1. Gridded P surplus data (NetCDF format): 48 files, each of them containing 170 years (1850-2019) of gridded data P surplus 2. Aggregated P surplus at European NUTS level (csv format) : 3 files (NUTS 1, NUTS 2, NUTS 3), each of them containing 170 years (1850-2019) of P surplus (mean and standard deviation of 48 estimates). Additionally, a readme file is provided for the NUTS ID's. 3. Aggregated P surplus at European river basins (CSV format): 1 file, each of them containing 170 years (1850-2019) of P surplus (mean and standard deviation of 48 estimates). Additionally, a readme file is provided for the river basin ID's. Unit: kg/ha/yr (ha = physical area of grid cell/NUTS/river basins) Time period: 1850-2019 <h4>Data description (v2):</h4> We have updated the dataset (v1) and creared v2, which includes  improvements and additional components for a more comprehensive phosphorus surplus analysis across Europe. The updates are as follows: <ul> <li>Refined P input estimates from mineral fertilizers (1850–1960): We have revised our methodology for historical P inputs from mineral fertilizers. In the revised estimates, instead of relying on nitrogen (N) fertilizer trends as a proxy for changes in P fertilizer, we have now incorporated a global dataset that traces the historical sources of phosphorus fertilizers from phosphate rock (1800–2000). This dataset provides a more reliable temporal trend for P fertilizer use. </li> <li>Exclusion of chemical weathering inputs to urban areas: This adjustment better reflects phosphorus dynamics in urban regions.</li> <li>Expanded data components: In addition to P surplus, the dataset now includes detailed estimates of P inputs (e.g., mineral fertilizers, manure) and P outputs , offering a more granular view of phosphorus flows.</li> </ul> <ol> <li>Gridded datasets <ol> <li>Total P surplus data (NetCDF format): 48 files, each of them containing 170 years (1850-2019) of gridded P surplus data</li> <li>Total P inputs data (NetCDF format): 1 file, containing 170 years (1850-2019) of gridded P inputs data</li> <li>Total P output data (NetCDF format): 1 file, containing 170 years (1850-2019) of gridded P outputs data</li> <li>P fertilizer (NetCDF format): 2 files, each of them containing 170 years (1850-2019) of gridded  P inputs from mineral fertilizer data</li> <li>P animal manure (NetCDF format): 6 files, each of them containing 170 years (1850-2019) of gridded P inputs from animal manure data</li> </ol> </li> <li> Aggregated P surplus at European NUTS level (csv format) : 3 files (NUTS 1, NUTS 2, NUTS 3), each of them containing 170 years (1850-2019) of P surplus (mean and standard deviation of 48 estimates). Additionally, a readme file is provided for the NUTS ID's. </li> <li> Aggregated P surplus at European river basins (CSV format): 1 file, each of them containing 170 years (1850-2019) of P surplus (mean and standard deviation of 48 estimates). Additionally, a readme file is provided for the river basin ID's. </li> </ol> Unit: kg/ha/yr (ha = physical area of grid cell/NUTS/river basins) Time period: 1850-2019 <h4>Acknowledgments and underlying datasets</h4> Partial support for this work was provided by the Global Water Quality Analysis and Service Platform (GlobeWQ) project financed by the German Ministry for Education and Research (grant number 02WGR1527A) and the Development Bank of Saxony, Research Project Funding on Resilient Zero-Pollution Wastewater Systems in Climate Change – Case Study Saxony (Project No. 100669418). We are also thankful to UFZ for providing computing power and technical support to the EVE supercomputing facility. We would like to thank people from various organizations and projects for kindly providing us with the data that were used in this study, which includes among others: FAO, Eurostat, HYDE, and IFA. <h4>Contact</h4> Further queries regarding these datasets can be directed to Masooma Batool (masooma.batool@ufz.de) and Rohini Kumar (rohini.kumar@ufz.de).

opencc-by-4.0Jul 2024View details →

zenodo44/100

Long-read sequencing and structural variant characterization in 1,019 samples from the 1000 Genomes Project

SV analysis of the long-read sequencing data of 1,019 samples from the 1000 Genomes Project. The data is hosted at the International Genome Sample Resource (IGSR) in the <a href="https://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/1KG_ONT_VIENNA/">1KG_ONT_VIENNA</a> directory. Please see the <a href="https://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/1KG_ONT_VIENNA/README_1KG_ONT_VIENNA.md">README</a> and <a href="https://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/1KG_ONT_VIENNA/README_1KG_ONT_VIENNA_datareuse_statement.md">data reuse statement</a> for further information about this dataset.

openmit-licenseApr 2024View details →

zenodo44/100

Long-term live imaging, cell identification and cell tracking in regenerating crustacean legs

Supplementary data and videos for the manuscript 'Long-term live imaging, cell identification and cell tracking in regenerating crustacean legs', by Çevrim, Laplace-Builhé, Sugawara, Rusciano, Labert, Brocard, Almazán and Averof. The supplementary data include: Supplementary Data 1 (.csv file);  Live imaging of regenerating Parhyale legs: image acquisition settings Table with information on the 22 time lapse recordings presented in Figure 3, including image acquisition settings, temperature and duration of the recordings. Supplementary Data 2 (.zip file);  Live imaging of regenerated Parhyale legs: maximum projections Compressed folder including maximum projections for each of the 22 time lapse recordings presented in Figure 3. These files were generated by projecting all or a subset of the z slices acquired at each time point. A 20 micron scale bar was added on the first time point. These files serve as a quick way to examine the 22 time lapse recordings. Supplementary Data 3 (22 .tif files);  Live imaging of regenerated Parhyale legs: complete datasets Complete image 3D+T hyperstacks for each of the 22 time lapse recordings presented in Figure 3. These files have been generated by concatenating the original image stacks and correcting any image shifts, as described in the Methods section of the paper. Supplementary Data 4 (.zip file);  Analysis of trade-offs of imaging resolution and image quality The data used for the analysis of trade-offs in imaging and the results shown in Table 1 are included in this compressed folder. Folders for the original recording (labelled 00), for each of the subsampled datasets (labelled 01 to 05), and for the denoised and deconvoluted datasets each include the corresponding image data and ground truth cell tracking files (.tif, .h5, .xml and .mastodon files) and three sets of cell track predictions (.mastodon files). There are also separate folders containing the Elephant detection and flow model parameters for each set of predictions. Supplementary Data 4 (.zip file);  Analysis of trade-offs of imaging resolution and image quality The data used for the analysis of trade-offs in imaging and the results shown in Table 1 are included in two folders. The folder named Image_and_tracking_data includes the image data (.tif, .h5, .xml), ground truth cell tracking files (.mastodon files) and three sets of cell track predictions (.mastodon files) for the original recording (labelled 00), for each of the subsampled datasets (labelled 01 to 05), and for the denoised and deconvoluted datasets. It also includes separate folders containing the Elephant detection and flow model parameters for each set of predictions. The folder named CTC_tracking_results includes the ground-truth data along with three sets of predictions for detection and tracking for each dataset, following the Cell Tracking Challenge format. For each dataset we include label image files (.tif) for every time point along with tracking results in .txt format, and each results directory (01_RES_*) also contains the evaluation results from the Cell Tracking Challenge Evaluation Software. For a detailed explanation of the folder structure, please refer to the Cell Tracking Challenge documentation. Supplementary Data 5 (.zip file);  Tracking the progenitors of spineless-expressing cells in the distal carpus The data used to generate Figure 7 are included in this compressed folder, including the live imaging and cell tracking files (.h5, .xml and .mastodon files) and the image stack of the spineless and futsch HCR and DAPI stainings (.tif file). Channel 2 shows spineless expression (mostly nascent transcripts in nuclei), as well as background signal in epidermal nuclei (possibly due to photoconversion of DAPI, see Karg & Golic 2018, Chromosoma 127: 235-245) and strong autofluorescence in granular cells (also visible in channel 1, depicting futsch HCR). Supplementary Data 6 (.txt file);  Sequences of Parhyale genes targeted by the HCR probes The sequences are provided in FASTA format. Supplementary Data 7 (.zip file);  Apoptosis in legs that have not been subjected to live imaging The data used to generate Figure 2 supplement 2 are contained in this compressed folder, including 9 image stacks of T4 and T5 legs fixed and stained with DAPI 3 days post amputation (with apoptotic nuclei marked) and a .txt file containing the apoptotic cell counts. Supplementary Data 8 (.zip file);  Analysis of tracking performance in relation to imaging depth The data used to generate Figure 5 are contained in this compressed folder, including separate folders for the data extracted from the analysis of datasets #1 to #5. Each folder includes data from three replicates (batches 001 to 003), with .csv files listing the z location of nucleus centroids (in µm) for the nuclei that were incorrectly detected by Elephant – either as false positives (FP) or as false negatives (FN) – and the ground truth data (GT). The folder also includes an .xlsx file gathering all the relevant data and the measurements of precision and recall. Supplementary Data 9 (.zip file);  Detecting the temporal pattern of cell divisions in regenerating legs The data used to generate Figure 4 are contained in this compressed folder, including the five image datasets (.tif, .h5, .xml), the detected cell divisions (.mastodon files), and an .xlxs file containing all the cell divisions counts and graphs. Video 1.  Time lapse recording of regeneration in a Parhyale T5 leg (dataset li48-t5) Live imaging of nuclei labelled with H2B-mREFruby (maximum projection of z slices 3-10). Proximal parts of the leg are to the left and the amputation site is at the right of the frame. For annotations of different features please refer to Figure 2. Shortly after leg amputation (0 hpa) hemocytes adhere to the wound. By 16 hpa the wound has melanized. Up to ~32 hpa epithelial cells can be seen migrating and accumulating at the wound, below the melanized scab (Figure 2A,B). Around 31 hpa, the leg tissues become detached from the scab (Figure 2C). At 43 hpa, the carpus-propodus boundary first becomes visible, and thereafter many cells can be observed dividing at the distal part of the leg stump (Figure 2D). At 56 hpa, the propodus-dactylus boundary first becomes visible (Figure 2E). At later stages, tissues in more proximal parts of the leg retract, making space for the regenerating leg to grow (Figure 2F,G). After ~90 hpa cell proliferation there is less cell proliferation and cell movements, and the nuclear positions within the tissue become fixed. Scale bars, 20 µm. Video 2.  Time lapse recording of regeneration in a Parhyale T5 leg (dataset li36-t5) Live imaging of nuclei labelled with H2B-mREFruby (maximum projection of z slices 3-15). Proximal parts of the leg are to the left and the amputation site is at the right of the frame. The sequence of events is similar to that described in Video 1, but the progression is slower: epithelial migration towards the wound is observed up to 40 hpa, tissues detach from the scab at 65 hpa, and the carpus-propodus and propodus-dactylus boundaries first become visible at 78 and 91 hpa. The tissues making up the carpus and propodus can be seen pulsating from 105 to 145 hpa. Scale bars, 20 µm.

opencc-by-4.0Sep 2024View details →

zenodo44/100

Coral thermotolerance retained following year-long exposure to a novel environment

Description of datasets: <h4>HTSeqCounts</h4> Raw HTSeq count data used for downstream RNA sequencing analyses and visualisations including Principal Component Analysis and Differential Gene Expression analysis of the four habitat groups (mangrove to reef, reef to reef, wild mangrove and wild reef). <h4>GO enriched gene results:</h4> GO enriched genes summarised by broad categories of GO Slims for Pocillopora acuta corals originating from a mangrove system vs colonies from an adjacent reef site, translocated to a reef environment for one year. Samples for gene expression were collected during an acute heat stress assay to assess differentially enriched genes between mangrove and reef corals under 36 ºC. Enriched GO terms were grouped into broader functional categories using the “goSlim” function in the GSEABase R package with GOslim generic obo as the reference database to allow for a summary of key functions enriched in groups under heat stress. See manuscript methods for further detail on differential gene expression analysis. <h4>2022-2023 mangrove/reef temperature and pH</h4> Temperature and pH data measured in the Low Isles mangrove lagoon and reef habitat using HOBO MX2510 loggers deployed from February 2022 - February 2023.  <h4>Methylated DNA data</h4> Percent DNA methylation relative to total DNA of mangrove to reef, reef to reef and wild mangrove groups under acute temperature stress during the February 2023 CBASS experiment. <h4>Analysis code.zip</h4> A copy of all R code used in the data analysis of this project

opencc-by-4.0Jun 2024View details →

zenodo44/100

Long term effects of payment for performance on maternal and child health outcomes– evidence from Tanzania

These are the datasets underpinning the paper entitled: Long term effects of payment for performance on maternal and child health outcomes– evidence from Tanzania. The datasets are provided in csv and Stata 16 format along with variable descriptions, and the Stata do file used for analysis.

opencc-by-4.0Oct 2021View details →

zenodo44/100

Simulation of the Long-Term Evolution of a Meandering River

This dataset contains three files which accompany the simulations (in MATLAB) associated with the publication of my dissertation.  The first is a small input file which sets the initial planform of the channel of a meandering river.  The second and third files are the resulting evolution through time, saved at 30 year time intervals but offset so that the actual temporal sequence is the first element from one file, then the first element from the other file then the second element from each file. These output files can serve as a check for other simulations, or save the simulation time required to generate them. See the accompanying dissertation document (to be referenced once the official reference is available) and the GitHub repository which includes the code required. The core 'engine' of this simulation was originally published as dowloadable code files in the supplementary online materials associated with this paper: Schwenk, J., Lanzoni, S., & Foufoula‐Georgiou, E. (2015). The life of a meander bend: Connecting shape and dynamics via analysis of a numerical model. Journal of Geophysical Research: Earth Surface, 120(4), 690-710. https://doi.org/10.1002/2014JF003252

opencc-by-4.0Dec 2020View details →

zenodo44/100

Long time-series ecological niche modelling using archaeological settlement data.

CR_settlement_niche_[N]_[Yr]_[BC/AD].tif Ecological niche models in GeoTIFF format generated with the MaxEnt software based using prehistoric settlement evidence as training data and environmental layers (elevation, mean annual precipitation, mean annual temperature, landscape water balance, soil types) as background data. Raster values represent the probability of presence of a settlement. N - chronological ordering Yr, BC/AD - calendar years BC or AD   CR_settlement_niche_combined.tif All models combined by averaging.   CR_settlement_archeo.zip Archaeological data used to train the MaxEnt models in ESRI SHP format with the following fields: Site_Type: Cemetery or Settlement Archeo_Dat: Archaeological dating (culture or period) Source: Source dataset (AMCR or LONGWOOD) AMCR: Archeologická mapa České republiky – Archaeological Map of the Czech Republic. Retrieved from https://digiarchiv.aiscr.cz/. LONGWOOD: Kolář, J., Tkáč, P., Macek, M., & Szabó, P. (2016).  Archaeology and Historical Ecology: the Archaeological Database of the LONGWOOD ERC Project. Archäologisches Korrespondenzblatt 46/4, 539-554. Yrs_BP_Avg: Average dating in calendar years BP (based on the archaeological dating) Yrs_BP_Unc: Temporal uncertainty of the dating (half of the culture or period's duration) Loc_Accur: Spatial accuracy derived from the recorded degree of the accuracy of location (radius in meters around the center point)

opencc-by-4.0Nov 2021View details →

zenodo44/100

A long-term hydrometeorological observations from the Lammin-Suo peatland station

Introduction The Lammin-Suo peatland station is located near Ilichevo settlement in Vyborgskiy district, Leningrad region, Russia (60.243541 N, 29.815533 E) in a hilly area with numerous sandy hills up to 150 m high, followed by deep pans. The Lammin-Suo peatland are classified as raised bog, the most common type of bog in Eastern Fennoscandia. The research at the Lammin-Suo peatland station focuses on hydrology, meteorology, soil processes and the landscape evolution of the peatland. This dataset presents hydrological, hydrogeological, and meteorological data collected at the Lammin-Suo peatland station over the period from 1 January 1952 to 31 December 2020. Monitored parameters include air temperature, air humidity, atmospheric pressure, wind speed and direction, cloudiness, precipitation, evaporation, snow depth, peat temperature, swamp water level, groundwater level and temperature. File naming conventions observation-type_periodicity_start-end_[meteostation_number].csv observation-type: meteo / precipitation / peat-temp / swamp-freezing / evapotranspiration / water-discharge / swamp-water-level / surface-elevation / groundwater-level / groundwater-temp periodicity: hourly / daily / 10d (ten days) / annual / 5y (five years) start-end: time coverage of the dataset [meteostation_number]: optional, encountered only in meteo_….csv Observation types and file contents 1. Meteo 1.1 Methods Meteorological observations have been carried out daily since 1952 at two meteorological stations. Observed parameters include air temperature, wind speed, air humidity, cloudiness, snow depth (in winter). Daily data are measured once per day (snow thickness) or calculated from hourly data (air temperature). Hourly data are measured at 01:00, 07:00, 13:00, and 19:00. Measuring equipment: air temperature (hourly, mean) – thermometer TM-4; maximum air temperature – maximum thermometer TM-1; minimum air temperature – minimum thermometer TM-2; wind speed - meteorological mast М-82-I, weathervane FVT / FVL; partial water vapour pressure in the air – meteorological hair hygrometer M-19; atmospheric pressure - aneroid barometer MD-49-2; cloud cover is observed visually. 1.2 Measurement site description Meteorological Station WS1. Coordinates: 60,243361 N 29,815583 E; Data period: 1952-2020 (active); Vegetation: Sphagnum, dwarf shrub, cotton grass, inclusions of pine (genetic center of the swamp). Meteorological Station WS2. Coordinates: 60,246979 N 29,810368 E; Data period: 1952-1965 (closed); Located near the building of the swamp station, on a forest plot. 1.3 File contents meteo_hourly_1952-2020_WS1.csv DATE_TIME format is %Y-%m-%dT%H:%M:%S AIR_TEMP is the air temperature observed at a moment of time DATE_TIME (degree Celsius) WIND_SPEED is the wind speed observed at a moment of time DATE_TIME (m per s) AIR_HUMIDITY is the partial water vapour pressure in the air observed at a moment of time DATE_TIME (mb) CLOUDINESS_TOTAL is the cloud cover (ranged between 0 and 10) CLOUDINESS_LOWER is the cloud cover of low-level clouds (ranged between 0 and 10) ATMO_PRESSURE is the atmospheric pressure observed at a moment of time DATE_TIME (mb)   meteo_hourly_1952-1965_WS2.csv DATE_TIME format is %Y-%m-%dT%H:%M:%S AIR_TEMP is the air temperature observed at a moment of time DATE_TIME (degree Celsius) AIR_HUMIDITY is the partial water vapour pressure in the air observed at a moment of time DATE_TIME (mb)   meteo_daily_1952-2020_WS1.csv DATE_TIME format is %Y-%m-%d MEAN_AIR_TEMP is the mean daily air temperature (degree Celsius) MAX_AIR_TEMP is the maximum observed daily air temperature (degree Celsius) MIN_AIR_TEMP is the minimum observed daily air temperature (degree Celsius) SNOW_THICKNESS (cm)   meteo_daily_1952-1965_WS2.csv DATE_TIME format is %Y-%m-%d MEAN_AIR_TEMP is the mean daily air temperature (degree Celsius) MAX_AIR_TEMP is the maximum observed daily air temperature (degree Celsius) MIN_AIR_TEMP is the minimum observed daily air temperature (degree Celsius) 2. Precipitation 2.1 Methods Precipitation is measured once per day in winter (15:00) and twice per day in summer (9:00, 15:00). The precipitation data are given as a total daily amount. Precipitation type (solid or liquid) has been observed since 1964. Precipitation is measured with a Tretyakov non-recording precipitation gauge. Corrections of wetting have been introduced into the measured daily precipitation values . No wind corrections were made. Studies have shown that pine growing in a swamp around the meteorological site acts as a wind protection [Novikov S., Batyev V. Hydrometeorological regime and water balance of raised bogs of North-West Russia, 2019]. The measurements were carried out on 7 rain gauges, 6 of them were installed in different parts of the swamp, P7 rain gauge - on a forest site near the swamp. For gauges P2-P6 precipitation measurements were carried out during the growing season only (from April/May to October). 2.2 Measurement site description Gauge P1. Coordinates: 60,243373 N 29,815466 E; Data period: 1952-2020 (active); Vegetation: Sphagnum, dwarf shrub, cotton grass, inclusions of pine (genetic center of the swamp). Gauge P2. Coordinates: 60.240412 N 29.808487 E; Data period: 1963-1976 (closed); Vegetation: Sphagnum, cotton grass, dwarf shrub, inclusions of pine and deadwood (southwest edge of the swamp). Gauge P3. Coordinates: 60.245829 N 29.82098 E; Data period: 1963-1976 (closed); Vegetation: Sphagnum, dwarf shrub, pine (northwest edge of the swamp). Gauge P4. Coordinates: 60.235467 N 29.821103 E; Data period: 1963-1976 (closed); Vegetation: Sphagnum, sedge, inclusions of birch and pine (south edge of the swamp). Gauge P5. Coordinates: 60.24037 N 29.832438 E; Data period: 1963-1976 (closed); Vegetation: Sphagnum, dwarf shrub, pine (southeastern edge of the swamp). Gauge P6. Coordinates: 60.234514 N 29.831253 E; Data period: 1963-1975 (closed); Vegetation: Sphagnum, pine, dwarf shrub (southern edge of the swamp). Gauge P7. Coordinates: 60,246979 N 29,810368 E; Data period: 1952-2020 (active); Location: the kame hill, near the building of the swamp station. 2.3 File contents precipitation_daily_1952-2020.csv DATE_TIME format is %Y-%m-%d PRECIPITATION is the daily precipitation amount (mm) SOLID marks whether precipitation is in the solid form (1 - solid, 0 - liquid, empty - not observed) 3. Peat temperature 3.1 Methods Peat temperature monitoring has been carried out daily since 1953 at the two sites. Peat temperature measurements include measurements at the swamp surface (average, minimum, maximum) and at depths of 5, 10, 15, 20, 40, 60, 80, 120, 160, 240, 290, 320 cm (different depth sets were used throughout the observation period). The swamp surface temperature was measured with thermometer TM-3, maximum thermometer TM-1, minimum thermometer TM-2 and low-degree thermometer TM-9-1. The temperature at the horizons of 5-15 cm was measured with Savinov thermometers. The temperature at the horizons of 20-320 cm was measured with soil-depth thermometers TM-10. 3.2 Measurement site description Site PT1. Coordinates: 60.243334 N 29.815287 E; Data period: 1956-2020 (active); Located next to the meteorological site No. 1; Vegetation: Sphagnum, dwarf shrub, cotton grass, inclusions of pine (central part of the swamp). Site PT2. Coordinates: 60.240477 N 29.808621 E; Data period: 1963-1995 (closed); Located next to the swamp water well 210; Vegetation: Sphagnum, cotton grass, dwarf shrub, inclusions of pine and deadwood (edge of the swamp). 3.3 File contents peat-temp_hourly_1976-2020.csv - The file contains temperature data by observation time at 09:00, 15:00, 21:00. DATE_TIME format is %Y-%m-%dT%H:%M:%S DEPTH is the measurement depth under the peat surface (cm) PEAT_TEMP is the peat temperature (degree Celsuis) SITE is the observation site QC is the quality control: 1 - ready-to-use value; 0 - unreliable value   peat-temp_daily_1953-1958.csv - The file contains average daily values, temperature was measured by electro thermometers.  DATE_TIME format is %Y-%m-%d DEPTH is the measurement depth under the peat surface (cm) PEAT_TEMP is the peat temperature (degree Celsuis) OBS_TYPE is the daily average SITE is the observation site QC is the quality control: 1 - ready-to-use value; 0 - unreliable value   peat-temp_daily_1954-2020.csv - The file contains average daily values, maximum and minimum temperature values per day, measurements were carried out with mercury thermometers. The maximum and minimum temperatures have been measured since June 1954, average daily values have been measured since January 1960. DATE_TIME format is %Y-%m-%d DEPTH is the measurement depth under the peat surface (cm) PEAT_TEMP is the peat temperature (degree Celsuis) OBS_TYPE are the daily average/daily min/daily max SITE is the observation site QC is the quality control: 100 - ready-to-use value; 0 - unreliable value 4. Freezing and thawing of the swamp 4.1 Methods Observations of the freezing and thawing of the swamp are carried out on the fixed sites. The freezing depth is measured twice (on a hummock and in a depression / on a ridge and in a hollow) near each of the four corner points of the site in places where the snow cover has not previously been disturbed. Simultaneously with the observation of freezing, observations of the snow cover on the site are carried out. Snow depth is measured using pre-installed snow gauge battens. The height of the installed battens is 120 cm. The snow gauges are placed on 5 cross-sections at a distance of 5 m from each other, the total number of snow-gauge battens is 50 pieces. Measurements are carried out once every 10 days in winter and until the peat deposits are completely thawed. The data table shows the averaged values of the measured values for the date of measurement. Observations begin in the autumn after a stable transition of average daily air temperatures to negative values. 4.2 Measurement site description Site SF1. Coordinates: 60.2443889 N 29.8138889 E; Data period: 1954-2020 (active); Size of the site: 50x50 m; Vegetation: Sphagnum, dwarf shrub, inclusions of pine (near the ridge-hollow complex). Site SF2. Coordinates: 60.2423889 N 29.8179167 E; Data period: 1989-2020 (active); Size of the site: 50x50 m; Vegetation: Sphagnum, dwarf shrub, cotton grass, inclusions of pine (central part of the swamp). Site SF3. Coordinates: 60.240451 N 29.821004 E; Data period: 1954-1991 (closed); Size of the site: 85x30 m; Vegetation: Sphagnum, cotton grass, dwarf shrub, inclusions of pine (the ridge-hollow complex). Site SF4. Coordinates: 60.239727 N 29.819625 E; Data period: 1954-1989 (closed); Observations are carried out on a route with a length of 250 m; Vegetation: Sphagnum, dwarf shrub, pine (forest ring). Site SF5. Coordinates: 60.237594 N 29.82639 E; Data period: 1954-1991 (closed); Size of the site: 50x50 m; Vegetation: Sphagnum, cotton grass, dwarf shrub, inclusions of pine and deadwood. 4.3 File contents swamp_freezing_d10_1954-2020.csv  DATE_TIME format is %Y-%m-%d FROZEN depth of the frozen layer (cm) THAW depth of thawed layer which was formed during thawing of a previously frozen layer (cm) WATER_CONTENT water layer above the surface of the swamp (cm) SNOW_COVER snow depth (cm) TERRAIN type of landscape (0 - depression; 1 - hummock; 2 - hollow; 3 - ridge) SITE is the observation site, valid values 1-5 5. Evapotranspiration 5.1 Methods Evapotranspiration is measured directly using the swamp evaporimeter (model GGI-B1000). Measurements are performed decadely during the growing season. Evaporimeters were initially installed at 6 sites located in different types of microlandscape (bog microlandscape – a part of a bog that is homogeneous in terms of the nature of the vegetation cover, surface microrelief and physical properties of the upper (active) horizon of a peat deposit). Currently, one evaporimeter is in operation, located in the central part of the swamp. Evapotranspiration is measured by the weighing method. Evapotranspiration between observation periods (10 days) is determined by measuring the mass of a peat monolith with natural marsh vegetation placed in an swamp evaporimeter, taking into account the precipitation that fell during the same period and swamp water level. 5.2 Measurement site description Site ET1. Coordinates: 60.243270 N 29.815240 E; Data period: 1956-2020; Vegetation: Sphagnum, dwarf shrub, cotton grass, rare inclusions of pine. Site ET2. Coordinates: 60.240252 N 29.821496; Data period: 1971-1976; Vegetation: Ridge-hollow complex (ridge and hollow). Site ET2.1. Coordinates: 60.240252 N 29.821496; Data period: 1962-1965; 1968-1970; Vegetation: Ridge-hollow complex (ridge). Site ET2.2. Coordinates: 60.240252 N 29.821496; Data period: 1962-1965; 1968-1970; Vegetation: Ridge-hollow complex (hollow). Site ET3. Coordinates: 60.240459 N 29.808622 E; Data period: 1966; 1968; 1970-1976; Vegetation: Sphagnum, cotton grass, dwarf shrub, inclusions of pine and deadwood (edge of the swamp). Site ET4. Coordinates: 60.237556 N 29.825795 E; Data period: 1968; 1970-1971, 1973-1974; Vegetation: Sphagnum, cotton grass, dwarf shrub, inclusions of pine and deadwood (hummocks). 5.3 File contents evapotranspiration_10d_1956-2020.csv - Measured values of evapotranspiration with a ten-day resolution. START_DATE format is %Y-%m-%d END_DATE format is %Y-%m-%d DAYS is amount of days between START_DATE and END_DATE (days) SITE is evapotranspiration measurement site ET is evapotranspiration amount between START_DATE and END_DATE (mm)   evapotranspiration_annual_1956-2020.csv - Evapotranspiration values aggregated by growing seasons. START_DATE format is %Y-%m-%d END_DATE format is %Y-%m-%d SITE is evapotranspiration measurement site ET is evapotranspiration amount between START_DATE and END_DATE (mm) 6. Water discharge 6.1 Methods There are no open streams directly in the swamp. Runoff from the swamp to the edge is carried out by filtration and then by streams formed at the swamp border. Water flow from the swamp is carried out through 5 streams. One of these streams (Vostochny stream) is temporary; water flows through it mainly only during the spring flood. Each of the streams is equipped with a hydrometric facility: thin-walled triangular weirs (V-notch) with wooden inlet and outlet channels. Year-round observations of the water level are carried out in the gauging chutes. The water level is recorded by the recorder continuously during the warm period of the year, observations are duplicated by manual measurement of the level every 5 days year-round. Water discharge rates are calculated according to the theoretical curve for a triangular weir with a cut angle of 90 degrees. In addition to observing the water runoff from the swamp by streams, the observation of the inflow of water into the swamp is carried out. Water inflow into the swamp with slope runoff is measured at the Runoff Plot. The Runoff Plot is located near the northwestern edge of the bog on the slope of the esker ridge. The catchment area of the plot is 400 sq m (20x20 m), the average longitudinal slope is 0.15. The runoff from the plot goes into a drainage gutter from which water enters the tank. The tank is located in a special pavilion. A level recorder is installed in the tank, recording level changes continuously during the warm period of the year. In parallel with this, manual measurements of the level are carried out once a day. In the column « DISCHARGE» column, an empty cell means no runoff, a value of "0" means that there was a minimum runoff through the tray that could not be measured and was observed visually. 6.2 Measurement site description Stream gauge Western-1 (Stream Zapadny-1); Coordinates: 60.2435278  N 29.8013333 E; Data period: 1955-2020 (active); Stream Western-2 flows from the western edge of the swamp. The catchment area in the hydrometric section is 0.32 sq. km. Distance from the swamp edge to the hydrometric section 0.37 km. Stream gauge Western-2 (Stream Zapadny-2); Coordinates: 60.2420000 N 29.8029444 E; Data period: 1955-2020 (active); Stream Western-1 flows from the western edge of the swamp. The catchment area in the hydrometric section is 0.32 sq. km. Distance from the swamp edge to the hydrometric section 0.32 km. Stream gauge Northern (Stream Severny); Coordinates: 60.2458333 N 29.8263056 E; Data period: 1953-2020 (active); Stream Northern flows from the northern edge of the swamp. The catchment area in the hydrometric section is 0.37 sq. km. Distance from the swamp edge to the hydrometric section 0.19 km. Stream gauge Southern (Stream Yuzhny); Coordinates: 60.2316667 N 29.8339167 E; Data period: 1950-2020 (active); Stream Southern flows from the southern edge of the swamp. The catchment area in the hydrometric section is 1.45 sq. km. Distance from the swamp edge to the hydrometric section 0.17 km Stream gauge Eastern (Stream Vostochny); Coordinates: 60.241975 N 29.828673 E; Data period: 1971-1988 (closed); Stream Eastern flows from the eastern edge of the swamp. The catchment area in the hydrometric section is 0.16 sq. km. Runoff Plot (Stokovaya ploshadka)  Coordinates: 60.2475278 N 29.8140278 E; Data period: 1963-2020 (active); The Runoff Plot is located 200 m east of the station, on the slope of the esker ridge in a pine forest. The catchment area of the site is 400 m2 (20 × 20 m), the average longitudinal slope is 0.15, the slope of the drainage gutter is 0.03. 6.3 File contents water-discharge_daily_1950-2020.csv DATE_TIME format is %Y-%m-%d DISCHARGE is the water discharge (cm3 s-1) SITE is the observation site name 7. Swamp water level 7.1 Methods The swamp water level is measured manually inside swamp water wells. In addition to the swamp water level, the water level is measured in the Dve Sestry lake. Lake Dve Sestry is located in the swamp. Measurements are carried out once every five days throughout the year. Water level in the well No.104 is measured daily. The swamp water level is given relative to the swamp surface elevation in the area around each well. The swamp surface elevation in the area around each well was measured every 5 years (1965-1995). А в абсолютных отметках? 7.2 Measurement site description Well S101. Coordinates: 60.245952 N 29.811333E; Data period: 1950-2020 (active). Vegetation: Sphagnum, dwarf shrub, pine (edge of the swamp, near the lakes). Well S102. Coordinates: 60.245029 N 29.812909 E; Data period: 1950-1989 (closed). Vegetation: Sphagnum, dwarf shrub, pine (edge of the swamp, near the lakes). Well S102a. Coordinates: 60.244680 N 29,813525 E; Data period: 1975-2020 (active). Vegetation: Sphagnum, dwarf shrub, pine. Well S103. Coordinates: 60.244362 N 29.814135 E; Data period: 1950-2020 (active). Vegetation: Sphagnum, dwarf shrub, cotton grass, inclusions of pine (central part of the swamp). Well S104. Coordinates: 60.2437640 N 29.815820 E; Data period: 1950-2020 (active). Vegetation: Sphagnum, dwarf shrub, cotton grass, inclusions of pine (central part of the swamp). Well S105. Coordinates: 60.242622 N 29.816963 E; Data period: 1950-1989 (closed). Vegetation: Sphagnum, dwarf shrub, cotton grass, inclusions of pine (central part of the swamp). Well S106. Coordinates: 60.241896 N 29.818209 E; Data period: 1950-2020 (active). Vegetation: Sphagnum, dwarf shrub, inclusions of pine (near the ridge-hollow complex). Well S107. Coordinates: 60.239240 N 29.823000 E; Data period: 1950-1989 (closed). Vegetation: Ridge-hollow complex (ridge and hollow).  Sphagnum, cotton grass, dwarf shrub, inclusions of pine. Well S107a. Coordinates: 60.242059 N 29.818111 E; Data period: 1970-1975 (closed). Vegetation: Ridge-hollow complex (ridge).  Sphagnum, cotton grass, dwarf shrub, inclusions of pine. Well S107b. Coordinates: 60.240126 N 29.821439 E; Data period: 1970-2020 (active). Vegetation: Ridge-hollow complex (hollow).  Sphagnum, cotton grass, dwarf shrub, inclusions of pine. Well S108. Coordinates: 60.237358 N 29.825969 E; Data period: 1950-2020 (active). Vegetation: Sphagnum, cotton grass, dwarf shrub, inclusions of pine and deadwood (hummocks). Well S109. Coordinates: 60.235011 N 29.830004 E; Data period: 1950-2020 (active). Vegetation: Sphagnum, pine, dwarf shrub (southern edge of the swamp). Well S210. Coordinates: 60.240483 N 29.808689 E; Data period: 1950-2020 (active). Vegetation: Sphagnum, cotton grass, dwarf shrub, inclusions of pine and deadwood (edge of the swamp). Well S211. Coordinates: 60.240833 N 29.809519 E; Data period: 1950-1989 (closed). Vegetation: Ridge-hollow complex. Sphagnum, cotton grass, dwarf shrub, inclusions of pine and deadwood. Well S212. Coordinates: 60.245206 N 29.819994 E; Data period: 1950-1984 (closed). Vegetation: Sphagnum, dwarf shrub, cotton grass, inclusions of pine (edge of the swamp, near the ridge-hollow complex). Well S313. Coordinates: 60.235525 N 29.821599 E; Data period: 1950-2020 (active). Vegetation: Sphagnum, sedge, inclusions of birch and pine. Well S314. Coordinates: 60.236543 N 29.823923 E; Data period: 1950-1989 (closed). Vegetation: Sphagnum, cotton grass, dwarf shrub, inclusions of pine and deadwood. Well S315. Coordinates: 60.239574 N 29.831197 E; Data period: 1950-1989 (closed). Vegetation: Sphagnum, cotton grass, dwarf shrub, inclusions of pine and deadwood. Well S316. Coordinates: 60.240437 N 29.833265 E; Data period: 1950-2020 (active). Vegetation: Sphagnum, dwarf shrub, pine (southeastern edge of the swamp). Well S317. Coordinates: 60.240762 N 29.833472 E; Data period: 1972-1989 (closed). Vegetation: Sphagnum, pine, dwarf shrub (southeastern edge of the swamp). Well S318. Coordinates: 60.242370 N 29.831554 E; Data period: 1978-1989 (closed). Vegetation: Sphagnum, pine, dwarf shrub (southeastern edge of the swamp). Well S319. Coordinates: 60.242534 N 29.831394 E; Data period: 1978-1989 (closed). Vegetation: Sphagnum, dwarf shrub. Dve Sestry Lake. Coordinates: 60.245241 N 29.812483 E; Data period: 1968-2020 (active). Water level in the lake Dve Sestry. Sphagnum, dwarf shrub, pine (edge of the swamp). 7.3 File contents swamp-water-level_daily_1950-2020.csv DATE_TIME format is %Y-%m-%d SW_WELL is the well (lake) name SW_LEVEL is the swamp water level (cm) 8. Swamp-surface elevation 8.1 Methods The swamp-surface elevation was determined by the linear taxation method. The principle of the method is to determine the height of the swamp-surface on a line of a certain length (taxation line) by a certain number of points. The taxation line is selected on an area with similar vegetation, conditions and micro-relief. As a result of static processing of the measured values, one absolute surface elevation is obtained for each taxation line. The taxation lines are confined to groundwater and swamp-water wells.Measurements are performed once every 5 years during the growing season.   8.2 Measurement site description The Data period of the wells is indicated for water level measurements. Swamp-surface elevation measurements were carried out for swamp-water wells in the period 1965-1995, for groundwater wells 1970-1995. Well S101. Coordinates: 60.245952 N 29.811333E; Data period: 1950-2020 (active). Vegetation: Sphagnum, dwarf shrub, pine (edge of the swamp, near the lakes). Well S102. Coordinates: 60.245029 N 29.812909 E; Data period: 1950-1989 (closed). Vegetation: Sphagnum, dwarf shrub, pine (edge of the swamp, near the lakes). Well S102a. Coordinates: 60.244680 N 29,813525 E; Data period: 1975-2020 (active). Vegetation: Sphagnum, dwarf shrub, pine. Well S103. Coordinates: 60.244362 N 29.814135 E; Data period: 1950-2020 (active). Vegetation: Sphagnum, dwarf shrub, cotton grass, inclusions of pine (central part of the swamp). Well S104. Coordinates: 60.2437640 N 29.815820 E; Data period: 1950-2020 (active). Vegetation: Sphagnum, dwarf shrub, cotton grass, inclusions of pine (central part of the swamp). Well S105. Coordinates: 60.242622 N 29.816963 E; Data period: 1950-1989 (closed). Vegetation: Sphagnum, dwarf shrub, cotton grass, inclusions of pine (central part of the swamp). Well S106. Coordinates: 60.241896 N 29.818209 E; Data period: 1950-2020 (active). Vegetation: Sphagnum, dwarf shrub, inclusions of pine (near the ridge-hollow complex). Well S107. Coordinates: 60.239240 N 29.823000 E; Data period: 1950-1989 (closed). Vegetation: Ridge-hollow complex (ridge and hollow).  Sphagnum, cotton grass, dwarf shrub, inclusions of pine. Well S107a. Coordinates: 60.242059 N 29.818111 E; Data period: 1970-1975 (closed). Vegetation: Ridge-hollow complex (ridge).  Sphagnum, cotton grass, dwarf shrub, inclusions of pine. Well S107b. Coordinates: 60.240126 N 29.821439 E; Data period: 1970-2020 (active). Vegetation: Ridge-hollow complex (hollow).  Sphagnum, cotton grass, dwarf shrub, inclusions of pine. Well S108. Coordinates: 60.237358 N 29.825969 E; Data period: 1950-2020 (active). Vegetation: Sphagnum, cotton grass, dwarf shrub, inclusions of pine and deadwood (hummocks). Well S109. Coordinates: 60.235011 N 29.830004 E; Data period: 1950-2020 (active). Vegetation: Sphagnum, pine, dwarf shrub (southern edge of the swamp). Well S210. Coordinates: 60.240483 N 29.808689 E; Data period: 1950-2020 (active). Vegetation: Sphagnum, cotton grass, dwarf shrub, inclusions of pine and deadwood (edge of the swamp). Well S211. Coordinates: 60.240833 N 29.809519 E; Data period: 1950-1989 (closed). Vegetation: Ridge-hollow complex. Sphagnum, cotton grass, dwarf shrub, inclusions of pine and deadwood. Well S212. Coordinates: 60.245206 N 29.819994 E; Data period: 1950-1984 (closed). Vegetation: Sphagnum, dwarf shrub, cotton grass, inclusions of pine (edge of the swamp, near the ridge-hollow complex). Well S313. Coordinates: 60.235525 N 29.821599 E; Data period: 1950-2020 (active). Vegetation: Sphagnum, sedge, inclusions of birch and pine. Well S314. Coordinates: 60.236543 N 29.823923 E; Data period: 1950-1989 (closed). Vegetation: Sphagnum, cotton grass, dwarf shrub, inclusions of pine and deadwood. Well S315. Coordinates: 60.239574 N 29.831197 E; Data period: 1950-1989 (closed). Vegetation: Sphagnum, cotton grass, dwarf shrub, inclusions of pine and deadwood. Well S316. Coordinates: 60.240437 N 29.833265 E; Data period: 1950-2020 (active). Vegetation: Sphagnum, dwarf shrub, pine (southeastern edge of the swamp). Well S317. Coordinates: 60.240762 N 29.833472 E; Data period: 1972-1989 (closed). Vegetation: Sphagnum, pine, dwarf shrub (southeastern edge of the swamp). Well S318. Coordinates: 60.242370 N 29.831554 E; Data period: 1978-1989 (closed). Vegetation: Sphagnum, pine, dwarf shrub (southeastern edge of the swamp). Well S319. Coordinates: 60.242534 N 29.831394 E; Data period: 1978-1989 (closed). Vegetation: Sphagnum, dwarf shrub. Dve Sestry Lake. Coordinates: 60.245241 N 29.812483 E; Data period: 1968-2020 (active). Water level in the lake Dve Sestry. Sphagnum, dwarf shrub, pine (edge of the swamp). Well G359. Coordinates: 60.245754 N 29.811594 E; Data period: 1964-2020 (active); Well depth: 5.6 m; Location: edge of the swamp, near the lakes. Well G360. Coordinates: 60.246631 N 29.809902 E; Data period: 1964-2020 (active). Well depth: 6.17 m; Location: the foot of the kame hill. Well G361. Coordinates: 60.247386 N 29.811244 E; Data period: 1964-1980 (closed). Well depth: 15.0 m; Location: the kame hill, near the station. Well G362. Coordinates: 60.246119 N 29.821464 E; Data period: 1964-1983 (closed). Well depth: 10.33 m; Location: on the hillside of esker. Well G363. Coordinates: 60.241874 N 29.836113 E; Data period: 1964-1972 (closed). Well depth: 18.41 m; Location: on the northeastern slope of the kame hill. Well G364. Coordinates: 60.243446 N 29.804700 E; Data period: 1964-2020 (active). Well depth: 5.06 m; Location: near the stream West-1. Well G365. Coordinates: 60.239495 N 29.805881 E; Data period: 1964-2020 (active). Well depth: 3.07 m; Location: southwest edge of the swamp, in the forest. Well G366. Coordinates: 60.239645 N 29.805699 E; Data period: 1964-2020 (active). Well depth: 5.63 m; Location: southwest edge of the swamp, in the forest. Well G367. Coordinates: 60.239796 N 29.805541 E; Data period: 1964-2020 (active). Well depth: 1.99 m; Location: southwest edge of the swamp, in the forest. Well G369. Coordinates: 60.231555 N 29.834127 E; Data period: 1964-2020 (active). Well depth: 4.69 m; Location: in the forest, 150 m from the swamp edge, 50 m from the stream Southern. Well G374. Coordinates: 60.245117 N 29.812595 E; Data period: 1965-2020 (active). Well depth: 5.72 m; Location: the swamp, near the Dve Sestry Lake. Well G375. Coordinates: 60.237512 N 29.826382 E; Data period: 1965-2020 (active). Well depth: 6.68 m; Location: southeast of the swamp. Well G376. Coordinates: 60.240623 N 29.833485 E; Data period: 1965-2020 (active). Well depth: 6.09 m; Location: southeast of the swamp, 50 m from the swamp edge. Well G377. Coordinates: 60.243325 N 29.815822 E; Data period: 1965-2020 (active). Well depth: 9.44 m; Location: central part of the swamp. Well G378. Coordinates: 60.234610 N 29.819447 E; Data period: 1965-2020 (active). Well depth: 4.26 m; Location: a swampy forest, 80 m from the swamp edge. Well G379. Coordinates: 60.234373 N 29.818323 E; Data period: 1965-2020 (active). Well depth: 4.35 m; Location: a swampy forest, 100 m from the swamp edge. Well G386. Coordinates: 60.246681 N 29.822289 E; Data period: 1965-1986 (closed). Well depth: 10.00 m; Location: North of the edge of swamp, 19 m from the Bolotny stream. Well G387. Coordinates: 60.247045 N 29.823210 E; Data period: 1965-1986 (closed). Well depth: 5.13 m; Location: North of the edge of swamp, on the esker, 115 m from well 386. Well G388. Coordinates: 60.247988 N 29.820518 E; Data period: 1965-1986 (closed). Well depth: 4.96 m; Location: North of the edge of swamp, at the head of the Bolotny stream. Well G389. Coordinates: 60.247668 N 29.819822 E; Data period: 1965-1986 (closed). Well depth: 9.0 m; Location: North of the edge of swamp, on the esker. Well G390. Coordinates: 60.247418 N 29.822103 E; Data period: 1965-1986 (closed). Well depth: 12.6 m; Location: North of the edge of swamp 8.3 File contents surface-elevation_5y_1965-1995.csv DATE_TIME format is %Y WELL_NO is the well number WELL_TYPE is the well type (SW - swamp water, GW - groundwater) SURF_ELEV is the surface elevation above sea level (m) 9. Groundwater level 9.1 Methods The groundwater level is measured manually inside groundwater wells. Measurements are carried out daily or once every five days during the year. The groundwater level is given relative to the surface elevation in the area around each well. Surface elevation in the area around each well was measured every 5 years (1970-1995). Что за уровень болота? Это мне непонятно, почему не БС? 9.2 Measurement site description Well G359. Coordinates: 60.245754 N 29.811594 E; Data period: 1964-2020 (active); Well depth: 5.6 m; Location: edge of the swamp, near the lakes. Well G360. Coordinates: 60.246631 N 29.809902 E; Data period: 1964-2020 (active). Well depth: 6.17 m; Location: the foot of the kame hill. Well G361. Coordinates: 60.247386 N 29.811244 E; Data period: 1964-1980 (closed). Well depth: 15.0 m; Location: the kame hill, near the station. Well G362. Coordinates: 60.246119 N 29.821464 E; Data period: 1964-1983 (closed). Well depth: 10.33 m; Location: on the hillside of esker. Well G363. Coordinates: 60.241874 N 29.836113 E; Data period: 1964-1972 (closed). Well depth: 18.41 m; Location: on the northeastern slope of the kame hill. Well G364. Coordinates: 60.243446 N 29.804700 E; Data period: 1964-2020 (active). Well depth: 5.06 m; Location: near the stream West-1. Well G365. Coordinates: 60.239495 N 29.805881 E; Data period: 1964-2020 (active). Well depth: 3.07 m; Location: southwest edge of the swamp, in the forest. Well G366. Coordinates: 60.239645 N 29.805699 E; Data period: 1964-2020 (active). Well depth: 5.63 m; Location: southwest edge of the swamp, in the forest. Well G367. Coordinates: 60.239796 N 29.805541 E; Data period: 1964-2020 (active). Well depth: 1.99 m; Location: southwest edge of the swamp, in the forest. Well G369. Coordinates: 60.231555 N 29.834127 E; Data period: 1964-2020 (active). Well depth: 4.69 m; Location: in the forest, 150 m from the swamp edge, 50 m from the stream Southern. Well G374. Coordinates: 60.245117 N 29.812595 E; Data period: 1965-2020 (active). Well depth: 5.72 m; Location: the swamp, near the Dve Sestry Lake. Well G375. Coordinates: 60.237512 N 29.826382 E; Data period: 1965-2020 (active). Well depth: 6.68 m; Location: southeast of the swamp. Well G376. Coordinates: 60.240623 N 29.833485 E; Data period: 1965-2020 (active). Well depth: 6.09 m; Location: southeast of the swamp, 50 m from the swamp edge. Well G377. Coordinates: 60.243325 N 29.815822 E; Data period: 1965-2020 (active). Well depth: 9.44 m; Location: central part of the swamp. Well G378. Coordinates: 60.234610 N 29.819447 E; Data period: 1965-2020 (active). Well depth: 4.26 m; Location: a swampy forest, 80 m from the swamp edge. Well G379. Coordinates: 60.234373 N 29.818323 E; Data period: 1965-2020 (active). Well depth: 4.35 m; Location: a swampy forest, 100 m from the swamp edge. Well G386. Coordinates: 60.246681 N 29.822289 E; Data period: 1965-1986 (closed). Well depth: 10.00 m; Location: North of the edge of swamp, 19 m from the Bolotny stream. Well G387. Coordinates: 60.247045 N 29.823210 E; Data period: 1965-1986 (closed). Well depth: 5.13 m; Location: North of the edge of swamp, on the esker, 115 m from well 386. Well G388. Coordinates: 60.247988 N 29.820518 E; Data period: 1965-1986 (closed). Well depth: 4.96 m; Location: North of the edge of swamp, at the head of the Bolotny stream. Well G389. Coordinates: 60.247668 N 29.819822 E; Data period: 1965-1986 (closed). Well depth: 9.0 m; Location: North of the edge of swamp, on the esker. Well G390. Coordinates: 60.247418 N 29.822103 E; Data period: 1965-1986 (closed). Well depth: 12.6 m; Location: North of the edge of swamp. 9.3 File contents groundwater-level_daily_1964-1986.csv DATE_TIME format is %Y-%m-%d GW_WELL is the well name GW_LEVEL is the groundwater level (cm) 10. Groundwater temperature 10.1 Methods Water temperature in groundwater wells is measured manually with a mercury thermometer (model TM-10). Measurements were taken on the 10th, 20th and the last day of the month during the year. 10.2 Measurement site description The Data period of the wells is indicated for groundwater temperature measurements. Well G359. Coordinates: 60.245754 N 29.811594 E; Data period: 1974-2020 (active); Well depth: 5.6 m; Location: edge of the swamp, near the lakes. Well G360. Coordinates: 60.246631 N 29.809902 E; Data period: 1974-2020 (active). Well depth: 6.17 m; Location: the foot of the kame hill. Well G362. Coordinates: 60.246119 N 29.821464 E; Data period: 1974-1983 (closed). Well depth: 10.33 m; Location: on the hillside of esker. Well G364. Coordinates: 60.243446 N 29.804700 E; Data period: 1974-2020 (active). Well depth: 5.06 m; Location: near the stream West-1. Well G365. Coordinates: 60.239495 N 29.805881 E; Data period: 1974-2020 (active). Well depth: 3.07 m; Location: southwest edge of the swamp, in the forest. Well G366. Coordinates: 60.239645 N 29.805699 E; Data period: 1974-2020 (active). Well depth: 5.63 m; Location: southwest edge of the swamp, in the forest. Well G367. Coordinates: 60.239796 N 29.805541 E; Data period: 1974-2020 (active). Well depth: 1.99 m; Location: southwest edge of the swamp, in the forest. Well G369. Coordinates: 60.231555 N 29.834127 E; Data period: 1974-2020 (active). Well depth: 4.69 m; Location: in the forest, 150 m from the swamp edge, 50 m from the stream Southern. Well G374. Coordinates: 60.245117 N 29.812595 E; Data period: 1975-2020 (active). Well depth: 5.72 m; Location: the swamp, near the Dve Sestry Lake. Well G375. Coordinates: 60.237512 N 29.826382 E; Data period: 1974-2020 (active). Well depth: 6.68 m; Location: southeast of the swamp. Well G376. Coordinates: 60.240623 N 29.833485 E; Data period: 1974-2020 (active). Well depth: 6.09 m; Location: southeast of the swamp, 50 m from the swamp edge. Well G377. Coordinates: 60.243325 N 29.815822 E; Data period: 1974-2020 (active). Well depth: 9.44 m; Location: central part of the swamp. Well G378. Coordinates: 60.234610 N 29.819447 E; Data period: 1974-2020 (active). Well depth: 4.26 m; Location: a swampy forest, 80 m from the swamp edge. Well G379. Coordinates: 60.234373 N 29.818323 E; Data period: 1974-2020 (active). Well depth: 4.35 m; Location: a swampy forest, 100 m from the swamp edge. Well G386. Coordinates: 60.246681 N 29.822289 E; Data period: 1974-1986 (closed). Well depth: 10.00 m; Location: North of the edge of swamp, 19 m from the Bolotny stream. Well G387. Coordinates: 60.247045 N 29.823210 E; Data period: 1974-1986 (closed). Well depth: 5.13 m; Location: North of the edge of swamp, on the esker, 115 m from well 386. Well G388. Coordinates: 60.247988 N 29.820518 E; Data period: 1974-1986 (closed). Well depth: 4.96 m; Location: North of the edge of swamp, at the head of the Bolotny stream. Well G389. Coordinates: 60.247668 N 29.819822 E; Data period: 1974-1986 (closed). Well depth: 9.0 m; Location: North of the edge of swamp, on the esker. Well G390. Coordinates: 60.247418 N 29.822103 E; Data period: 1974-1986 (closed). Well depth: 12.6 m; Location: North of the edge of swamp. 10.3 File contents groundwater-temp_daily_1974-2020.csv DATE_TIME format is %Y-%m-%d GW_WELL is the well name GW_TEMP is the groundwater temperature (degree Celsuis) Coordinates of the observation sites Location of the observation sites could be found in the coords.geojson file.

opencc-by-4.0Jul 2021View details →

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Long-term measurements of aerosol precursor concentrations in the Finnish sub-Arctic boreal forest

This data set is connected to the article:  Jokinen, T., Lehtipalo, K., Thakur, R. C., Ylivinkka, I., Neitola, K., Sarnela, N., Laitinen, T., Kulmala, M., Petäjä, T., and Sipilä, M.: Measurement report: Long-term measurements of aerosol precursor concentrations in the Finnish sub-Arctic boreal forest, Atmos. Chem. Phys., 2022

opencc-by-4.0Jan 2022View details →

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Selection against admixture and gene regulatory divergence in a long-term primate field study

Selection against admixture and gene regulatory divergence in a long-term primate field study Vilgalys & Fogel et al. (bioRxiv) <ul> <li><a href="https://zenodo.org/api/files/7cb721ac-b8e0-4dc0-a91b-fd54cc70f8d7/Panubis1.0_to_hg38.chain.gz">Panubis1.0_to_hg38.chain.gz</a>; <a href="https://zenodo.org/api/files/7cb721ac-b8e0-4dc0-a91b-fd54cc70f8d7/hg38_to_Panubis1.0.chain.gz">hg38_to_Panubis1.0.chain.gz</a>: Liftover chain files between Panubis1.0 and hg38. </li> <li><a href="https://zenodo.org/api/files/7cb721ac-b8e0-4dc0-a91b-fd54cc70f8d7/amboseli_LCLAE_tracts.txt.gz">amboseli_LCLAE_tracts.txt.gz</a>: Local ancestry calls for 442 wild, hybrid baboons studied as part of the Amboseli Baboon Research Project. Local ancestry was called using LCLAE and is represented by a 0 for homozygous yellow ancestry, 2 for homozygous anubis ancestry, and 1 for heterozygous ancestry. Each row in the file has a genomic position (chromosome, start, and end), local ancestry call, and the individual for whom the call was made.  </li> <li><a href="https://zenodo.org/api/files/7cb721ac-b8e0-4dc0-a91b-fd54cc70f8d7/masked_yellow_and_anubis.vcf.gz?versionId=25e26878-bca3-4667-9668-9e19424bc23e">masked_yellow_and_anubis.vcf.gz</a>: Genotype calls for non-Amboseli yellow and anubis baboons, after masking to remove putative introgressed ancestry. </li> <li>A time-stamped version of the code is included here, and also available on GitHub at <a href="http://github.com/TaurVil/VilgalysFogel_Amboseli_admixture">github.com/TaurVil/VilgalysFogel_Amboseli_admixture</a>. </li> </ul>

opencc-by-4.0Aug 2021View details →

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Dataset: Infrared-radiofluorescence: dose saturation and long-term signal stability of a K-feldspar sample

Original measurement and processed data of the study Infrared-radiofluorescence: dose saturation and long-term signal stability of a K-feldspar sample submitted for review to Radiation Measurements. The data are structured as follows: <ol> <li>Measurement data </li> <li>Processed data</li> </ol> Experiments were carried out at the Archéosciences Bordeaux (UMR 6034, CNRS - Université Bordeaux Montaigne; former IRAMAT-CRP2A) in Bordeaux (France) and at the Département des sciences de la Terre of the Université du Québec à Montréal (Canada). The subfolders are organised by the laboratory where the experiments were carried out: spectrometer measurements in Montréal (00_Montreal_Spectrometer) and spatially resolved measurements (camera) in Bordeaux (10_Bordeaux_Camera).  Measurement data contains sequence files used to run the experiments (so-called *.lseq files) as well as the raw, unaltered measurement output in the form of files with the ending *.xsyg and *.tiff. For the camera measurements in Bordeaux, the system returned a couple of single TIFF files. We merged those files in two files, one for RFnat and RFreg, for convenience reasons. The data are, however, unprocessed.   Processed data is organized like the measurement data folder containing all kinds of semi-automated processed data (PDF files, images). All data were processed with the R (R Core Team, 2021) package 'Luminescence' (Kreutzer et al., 2012; 2021) and an ImageJ macro detailed in Mittelstraß and Kreutzer (2021)   References Kreutzer, S., Schmidt, C., Fuchs, M.C., Dietze, M., Fischer, M., Fuchs, M., 2012. Introducing an R package for luminescence dating analysis. Ancient TL 30, 1–8. Kreutzer, S., Burow, C., Dietze, M., Fuchs, M.C., Schmidt, C., Fischer, M., Friedrich, J., Mercier, N., Smedley, R.K., Christophe, C., Zink, A., Durcan, J., King, G.E., Philippe, A., Guérin, G., Riedesel, S., Autzen, M., Guibert, P., Mittelstrass, D., Gray, H.J., 2021. Luminescence: Comprehensive luminescence dating data analysis. CRAN. https://doi.org/10.5281/zenodo.4729933 Mittelstraß, D., Kreutzer, S., 2021. Spatially resolved infrared radiofluorescence: single-grain K-feldspar dating using CCD imaging. Geochronology 3, 299–319. https://doi.org/10.5194/gchron-3-299-2021 R Core Team, 2021. R: A language and environment for statistical computing. https://www.r-project.org

opencc-by-4.0Mar 2022View details →

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Supplementary material for "Long-term trends of reproductive success of black grouse Lyrurus tetrix in the southern Swiss Alps in relation to changes in climate and land use"

Abstract Breeding success of an Alpine black grouse Lyrurus tetrix population in southern Switzerland was monitored from 1981 to 2020. This long-term dataset allows exploring relationships of reproductive rates with climate and habitat, which have shown marked changes during this period. Over the 40 years, the average elevation of black grouse breeding sites increased by around 100 m in Central/Southern Ticino but showed only a slight increase in Northern Ticino, where black grouse occur at higher elevations. Average reproductive rates in Northern Ticino remained constant throughout the study period but declined in Central/Southern Ticino. Relationships between reproductive success and weather as well as habitat variables were analysed with a multiple regression model. Temperature during the early chick-rearing phase and the time of egg-laying was positively correlated with reproductive rate. Correlations between reproductive rates and precipitation were less clear, and only small proportions of the variance in reproductive rates could be explained by precipitation. Brush forest explained the greatest amount of variation in reproductive rate (6.2%). Forest, alpine agricultural areas, and unproductive vegetation all showed a positive relationship with reproductive rate, but the proportion of the variance explained was small. Year (5.1%) and its interaction with region (2.3%) explained considerable amounts of the variance. While in Northern Ticino reproductive success did not show a negative trend when correcting for weather and habitat changes, there remained a negative trend over the years in Central/Southern Ticino. Despite the positive correlations of reproductive rate with temperature, increasing temperatures do not appear to have improved reproductive success, likely as a result of habitat changes that forced black grouse towards higher elevations. Changes in reproductive success were limited to the southern region, indicating deteriorating conditions at the edge of the distribution range.

opencc-by-4.0Jan 2022View details →

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Towards a systematic approach to manual annotation of code smells - C# Dataset of Long Method and Large Class code smells

This dataset includes open-source projects written in C# programing language, annotated for the presence of Long Method and God Class code smells. Each instance was manually annotated by at least two annotators. We explain our motivation and methodology for creating this dataset in our <a href="https://www.techrxiv.org/articles/preprint/Towards_a_systematic_approach_to_manual_annotation_of_code_smells/14159183/1">preprint</a>: Luburić, N., Prokić, S., Grujić, K.G., Slivka, J., Kovačević, A., Sladić, G. and Vidaković, D., 2021. Towards a systematic approach to manual annotation of code smells.  The dataset contains two excel datasheets: <ul> <li>DataSet_Large Class.xlsx – C# classes annotated for the Large Class code smell severity.</li> <li>DataSet_Long Method.xlsx – C# methods annotated for the Long method code smell severity.</li> </ul>  The columns in the datasheet represent: <ul> <li>Code Snippet ID – the full name of the code snippet.  <ul> <li>For classes, this is the package/namespace name followed by the class name. The full name of inner classes also contains the names of any outer classes (e.g., namespace.subnamespace.outerclass.innerclass).</li> <li>For methods, this is the full name of the class and the methods’s signature (e.g., namespace.class.method(param1Type, param2Type) ).</li> </ul> </li> <li>Link – The GitHub link to the code snippet, including the commit and the start and end LOC.</li> <li>Code Smell – code smell for which the code snippet is examined (Large Class or Long Method).</li> <li>Project Link – the link to the version of the code repository that was annotated.</li> <li>Metrics – a list of metrics for the code snippet, calculated by our <a href="https://github.com/Clean-CaDET/platform#readme">platform</a>. Our dataset provides 25 class-level metrics for Large Class detection and 18 method-level metrics for Long Method detection The list of metrics and their definitions is available <a href="https://github.com/Clean-CaDET/platform/blob/c4acff95ec00ff6c25fa62dde4818c1f40e39d39/CodeModel/CaDETModel/CodeItems/CaDETMetrics.cs">here</a>.</li> <li>Final annotation – a single severity score calculated by a majority vote. </li> <li>Annotators – each annotator's (1, 2, or 3) assigned severity score.</li> </ul> To help guide their reasoning for evaluating the presence and the severity of a code smell, three annotators independently annotated whether the considered heuristics apply to an evaluated code snippet. We provide these results in two separate excel datasheets: <ul> <li>LargeClass_Heuristics.xlsx - C# classes annotated for the presence of heuristics relevant for the Large Class code smell.</li> <li>LongMethod_Heuristics.xlsx - C# classes annotated for the presence of heuristics relevant for the Large Class code smell.</li> </ul> The columns of these two datasheets are: <ul> <li>Code Snippet ID - the full name of the code snippet (matching the IDs from DataSet_Large Class.xlsx and DataSet_Long Method.xlsx)</li> <li>Annotators – heuristics labelled by each of the annotators (1, 2, or 3).</li> <li>Heuristics – whether the heuristic is applicable to the examined code snippet or not (Section 1.2.4 lists heuristics relevant for the Large Class detection, and Section 1.2.5 lists the heuristics relevant for the Long Method detection).</li> </ul>

opencc-by-4.0May 2022View details →

zenodo44/100

Long-term annual soil nitrogen surplus across Europe (1850 – 2019)

This dataset consists of annual long-term reconstruction of total N surplus (both agricultural and non-agricultural soils) across Europe at a 5 arcmin spatial resolution for the period 1850 to 2019. The dataset consists of 16 N surplus estimates that account for the uncertainties resulting from input data sources and methodological choices in major components of the N surplus.This dataset offers the flexibility of aggregating the N surplus at any spatial scale of relevance to support water and land management strategies.   Data description: 1. Gridded N surplus data (NetCDF format): 16 files, each of them containing 170 years (1850-2019) of gridded data N surplus 2. Aggregated N surplus at European NUTS level (csv format) : 3 files (NUTS 1, NUTS 2, NUTS 3), each of them containing 170 years (1850-2019) of N surplus (mean and standard deviation of 16 estimates). Additionally, a readme file is provided for the NUTS ID's. 3. Aggregated N surplus at European river basins (csv format) : 1 file, each of them containing 170 years (1850-2019) of N surplus (mean and standard deviation of 16 estimates). Additionally, a readme file is provided for the river basin ID's. Unit: kg/ha/yr (ha = physical area of grid cell/NUTS/river basins) Time period: 1850-2019 Further information: Details/Citation: Long-term annual soil nitrogen surplus across Europe (1850-2019) by M. Batool, F.J.Sarrazin, S. Attinger, N.B.Basu, K.Van Meter and R. Kumar. Further queries regardig these datasets can be directed to Masooma Batool (masooma.batool@ufz.de) and Rohini Kumar (rohini.kumar@ufz.de).

opencc-by-4.0May 2022View details →

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Human Long Bone Dataset for csg-toolkit

The present dataset is provided for testing and demonstration purposes for the `csg-toolkit`, a GNU Octave package based on the "long-bone-diaphyseal-CSG-Toolkit". It consists of eight 3D triangular mesh bone models of bilateral bones (humerus, ulna, femur, and tibia) of the lower and upper limbs. These bones are part of the Athens modern reference skeletal collection and they have been digitized by means of 3D photogrammetry with an accuracy of 0.02mm. For reference, they belong to the same individual, a Greek male, 26 years old.

opencc-by-4.0Jun 2022View details →

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Machine learning classifiers for species classification of fungi using error-prone long-reads on extended metabarcodes

Machine learning models used in the decision tree of linked machine learning models (<a href="https://github.com/teenjes/fungal_ML">https://github.com/teenjes/fungal_ML</a>)

opencc-by-4.0May 2022View details →

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European Aerosol Phenomenology - 8: Harmonised Source Apportionment of Organic Aerosol using 22 Year-long ACSM/AMS Datasets

Organic aerosol (OA) is a key component of total submicron particulate matter (PM1), and comprehensive knowledge of OA sources across Europe is crucial to mitigate PM1 levels. Europe has a well-established air quality research infrastructure from which yearlong datasets using 21 aerosol chemical speciation monitors (ACSMs) and 1 aerosol mass spectrometer (AMS) were gathered during 2013–2019. It includes 9 non-urban and 13 urban sites. This study developed a state-of-the-art source apportionment protocol to analyse long-term OA mass spectrum data by applying the most advanced source apportionment strategies (i.e., rolling PMF, ME-2, and bootstrap). This harmonised protocol was followed strictly for all 22 datasets, making the source apportionment results more comparable. In addition, it enables quantification of the most common OA components such as hydrocarbon-like OA (HOA), biomass burning OA (BBOA), cooking-like OA (COA), more oxidised-oxygenated OA (MO-OOA), and less oxidised-oxygenated OA (LO-OOA). Other components such as coal combustion OA (CCOA), solid fuel OA (SFOA: mainly mixture of coal and peat combustion), cigarette smoke OA (CSOA), sea salt (mostly inorganic but part of the OA mass spectrum), coffee OA, and ship industry OA could also be separated at a few specific sites. Oxygenated OA (OOA) components make up most of the submicron OA mass (average = 71.1%, range from 43.7 to 100%). Solid fuel combustion-related OA components (i.e., BBOA, CCOA, and SFOA) are still considerable with in total 16.0% yearly contribution to the OA, yet mainly during winter months (21.4%). Overall, this comprehensive protocol works effectively across all sites governed by different sources and generates robust and consistent source apportionment results. Our work presents a comprehensive overview of OA sources in Europe with a unique combination of high time resolution (30–240 min) and long-term data coverage (9–36 months), providing essential information to improve/validate air quality, health impact, and climate models.

opencc-by-4.0May 2022View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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Allen Brain Atlas

Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

Annotated Behaviour and Observability Dataset (ABODe)

ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

DANDI Archive for NWB datasets

DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

International Brain Laboratory public data

The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

OpenNeuro

OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record

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Dataset results

Metagenome-assembled genomes from Stordalen Mire, Sweden (2019) (MAGs from long-read, short-read, & hybrid assemblies)

Nanoparticle clustering in supraparticles to control magnetic long-range interactions

Dataset for the paper "Combining near-term benefits of climate adaptation with long-term benefits of emissions abatement"

Century Long Reconstruction of Gridded Phosphorus Surplus Across Europe (1850-2019)

Long-read sequencing and structural variant characterization in 1,019 samples from the 1000 Genomes Project

Long-term live imaging, cell identification and cell tracking in regenerating crustacean legs

Coral thermotolerance retained following year-long exposure to a novel environment

Long term effects of payment for performance on maternal and child health outcomes– evidence from Tanzania

Simulation of the Long-Term Evolution of a Meandering River

Long time-series ecological niche modelling using archaeological settlement data.

A long-term hydrometeorological observations from the Lammin-Suo peatland station

Long-term measurements of aerosol precursor concentrations in the Finnish sub-Arctic boreal forest

Selection against admixture and gene regulatory divergence in a long-term primate field study

Dataset: Infrared-radiofluorescence: dose saturation and long-term signal stability of a K-feldspar sample

Supplementary material for "Long-term trends of reproductive success of black grouse Lyrurus tetrix in the southern Swiss Alps in relation to changes in climate and land use"

Towards a systematic approach to manual annotation of code smells - C# Dataset of Long Method and Large Class code smells

Long-term annual soil nitrogen surplus across Europe (1850 – 2019)

Human Long Bone Dataset for csg-toolkit

Machine learning classifiers for species classification of fungi using error-prone long-reads on extended metabarcodes

European Aerosol Phenomenology - 8: Harmonised Source Apportionment of Organic Aerosol using 22 Year-long ACSM/AMS Datasets

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Allen Brain Atlas

Annotated Behaviour and Observability Dataset (ABODe)

DANDI Archive for NWB datasets

International Brain Laboratory public data

OpenNeuro

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