Find research datasets worth reusing

Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.

6,569

datasets available to search

ShareScore release 0.9.0

Dataset results

6,569 results for “Long-term”

Learn how ShareScore rates datasets ↗

zenodo44/100

Long-Term Tracing of Indoor Solar Harvesting

Dataset Information This dataset presents long-term term indoor solar harvesting traces and jointly monitored with the ambient conditions. The data is recorded at 6 indoor positions with diverse characteristics at our institute at ETH Zurich in Zurich, Switzerland. The data is collected with a measurement platform [3] consisting of a solar panel (AM-5412) connected to a bq25505 energy harvesting chip that stores the harvested energy in a virtual battery circuit. Two TSL45315 light sensors placed on opposite sides of the solar panel monitor the illuminance level and a BME280 sensor logs ambient conditions like temperature, humidity and air pressure. The dataset contains the measurement of the energy flow at the input and the output of the bq25505 harvesting circuit, as well as the illuminance, temperature, humidity and air pressure measurements of the ambient sensors. The following timestamped data columns are available in the raw measurement format, as well as preprocessed and filtered HDF5 datasets: <ul> <li><code>V_in</code> - Converter input/solar panel output voltage, in volt</li> <li><code>I_in</code> - Converter input/solar panel output current, in ampere</li> <li><code>V_bat</code> - Battery voltage (emulated through circuit), in volt</li> <li><code>I_bat</code> - Net Battery current, in/out flowing current, in ampere</li> <li><code>Ev_left</code> - Illuminance left of solar panel, in lux</li> <li><code>Ev_right</code> - Illuminance left of solar panel, in lux</li> <li><code>P_amb</code> - Ambient air pressure, in pascal</li> <li><code>RH_amb</code> - Ambient relative humidity, unit-less between 0 and 1</li> <li><code>T_amb</code> - Ambient temperature, in centigrade Celsius</li> </ul> The following publication presents and overview of the dataset and more details on the deployment used for data collection. A copy of the abstract is included in this dataset, see the file <code>abstract.pdf</code>. <blockquote> L. Sigrist, A. Gomez, and L. Thiele. "Dataset: Tracing Indoor Solar Harvesting." In Proceedings of the 2nd Workshop on Data Acquisition To Analysis (DATA '19), 2019. </blockquote> Folder Structure and Files <ul> <li><code>processed/</code> - This folder holds the imported, merged and filtered datasets of the power and sensor measurements. The datasets are stored in HDF5 format and split by measurement position <code>posXX</code> and and power and ambient sensor measurements. The files belonging to this folder are contained in archives named <code>yyyy_mm_processed.tar</code>, where <code>yyyy</code> and <code>mm</code> represent the year and month the data was published. A separate file lists the exact content of each archive (see below).</li> <li><code>raw/</code> - This folder holds the raw measurement files recorded with the RocketLogger [1, 2] and using the measurement platform available at [3]. The files belonging to this folder are contained in archives named <code>yyyy_mm_raw.tar</code>, where <code>yyyy</code> and <code>mm</code>represent the year and month the data was published. A separate file lists the exact content of each archive (see below).</li> <li><code>LICENSE</code> - License information for the dataset.</li> <li><code>README.md</code> - The README file containing this information.</li> <li><code>abstract.pdf</code> - A copy of the above mentioned abstract submitted to the DATA '19 Workshop, introducing this dataset and the deployment used to collect it.</li> <li><code>raw_import.ipynb</code> [<a href="https://nbviewer.jupyter.org/urls/zenodo.org/record/3715472/files/raw_import.ipynb">open in nbviewer</a>] - Jupyter Python notebook to import, merge, and filter the raw dataset from the <code>raw/</code> folder. This is the exact code used to generate the processed dataset and store it in the HDF5 format in the <code>processed/</code>folder.</li> <li><code>raw_preview.ipynb</code> [<a href="https://nbviewer.jupyter.org/urls/zenodo.org/record/3715472/files/raw_preview.ipynb">open in nbviewer</a>] - This Jupyter Python notebook imports the raw dataset directly and plots a preview of the full power trace for all measurement positions.</li> <li><code>processing_python.ipynb</code> [<a href="https://nbviewer.jupyter.org/urls/zenodo.org/record/3715472/files/processing_python.ipynb">open in nbviewer</a>] - Jupyter Python notebook demonstrating the import and use of the processed dataset in Python. Calculates column-wise statistics, includes more detailed power plots and the simple energy predictor performance comparison included in the abstract.</li> <li><code>processing_r.ipynb</code> [<a href="https://nbviewer.jupyter.org/urls/zenodo.org/record/3715472/files/processing_r.ipynb">open in nbviewer</a>] - Jupyter R notebook demonstrating the import and use of the processed dataset in R. Calculates column-wise statistics and extracts and plots the energy harvesting conversion efficiency included in the abstract. Furthermore, the harvested power is analyzed as a function of the ambient light level.</li> </ul> Dataset File Lists Processed Dataset Files The list of the processed datasets included in the <code>yyyy_mm_processed.tar</code> archive is provided in <code>yyyy_mm_processed.files.md</code>. The markdown formatted table lists the name of all files, their size in bytes, as well as the SHA-256 sums. Raw Dataset Files A list of the raw measurement files included in the <code>yyyy_mm_raw.tar</code> archive(s) is provided in <code>yyyy_mm_raw.files.md</code>. The markdown formatted table lists the name of all files, their size in bytes, as well as the SHA-256 sums. Dataset Revisions v1.0 (2019-08-03) Initial release. Includes the data collected from 2017-07-27 to 2019-08-01. The dataset archive files related to this revision are <code>2019_08_raw.tar</code> and <code>2019_08_processed.tar</code>. For position pos06, the measurements from 2018-01-06 00:00:00 to 2018-01-10 00:00:00 are filtered (data inconsistency in file <code>indoor1_p27.rld</code>). v1.1 (2019-09-09) Revision of the processed dataset v1.0 and addition of the final dataset abstract. Updated processing scripts reduce the timestamp drift in the processed dataset, the archive <code>2019_08_processed.tar</code> has been replaced. For position pos06, the measurements from 2018-01-06 16:00:00 to 2018-01-10 00:00:00 are filtered (<code>indoor1_p27.rld</code> data inconsistency). v2.0 (2020-03-20) Addition of new data. Includes the raw data collected from 2019-08-01 to 2019-03-16. The processed data is updated with full coverage from 2017-07-27 to 2019-03-16. The dataset archive files related to this revision are <code>2020_03_raw.tar</code> and <code>2020_03_processed.tar</code>. Dataset Authors, Copyright and License <ul> <li>Authors: Lukas Sigrist, Andres Gomez, and Lothar Thiele</li> <li>Contact: Lukas Sigrist (<a href="mailto:lukas.sigrist@tik.ee.ethz.ch">lukas.sigrist@tik.ee.ethz.ch</a>)</li> <li>Copyright: (c) 2017-2019, ETH Zurich, Computer Engineering Group</li> <li>License: Creative Commons Attribution 4.0 International License (<a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a>)</li> </ul> References [1] L. Sigrist, A. Gomez, R. Lim, S. Lippuner, M. Leubin, and L. Thiele. Measurement and validation of energy harvesting IoT devices. In Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017. [2] ETH Zurich, Computer Engineering Group. RocketLogger Project Website, <a href="https://rocketlogger.ethz.ch/">https://rocketlogger.ethz.ch/</a>. [3] L. Sigrist. Solar Harvesting and Ambient Tracing Platform, 2019. <a href="https://gitlab.ethz.ch/tec/public/employees/sigristl/harvesting_tracing">https://gitlab.ethz.ch/tec/public/employees/sigristl/harvesting_tracing</a>

opencc-by-4.0Aug 2019View details →

zenodo44/100

Quantifying hail and lightning risk factors using long-term observations around Australia - Hail and Lightning Datasets

These data accompany a paper referenced as doi: 10.1029/2020JD033101. Two spreadsheets are provided as used to derive the hourly and monthly occurrence of hail or lightning events within the domains of the ten radars sites (Melbourne, Wollongong, Gympie, Grafton, Canberra, Marburg, Adelaide, Namoi, Perth, Hobart). Lightning events as defined in this paper (derived from post-processed lightning information) were provided for 2005-2018 and hail data from 1997-2018 (noting that the start date of hail is dependent on when the respective radar site was established). A value of 1 indicates a lightning/hail event occurred during that hour (in UTC +0). Information on processing methods and data are provided in the published paper for the doi listed above.

opencc-by-4.0Aug 2020View details →

zenodo44/100

SGD-SM: Generating Seamless Global Daily AMSR2 Soil Moisture Long-term Products (2013-2019)

If you used our dataset, please cite our reference: Zhang, Q., Yuan, Q., Li, J., Wang, Y., Sun, F., and Zhang, L.: Generating seamless global daily AMSR2 soil moisture (SGD-SM) long-term products for the years 2013–2019, Earth Syst. Sci. Data, 13, 1385–1401, https://doi.org/10.5194/essd-13-1385-2021, 2021. Description: <ul> <li>A seamless global daily (SGD) AMSR2 soil moisture long-term (2013-2019) dataset is generated through the proposed model. This daily products include 2553 global soil moisture NetCDF4 files, starting from Jan 01, 2013 to Dec 31, 2019 (about 20GB memory after uncompressing this zip file).</li> <li>To further validate the effectiveness of these products, three verification ways are employed as follow: 1) In-situ validation; 2) Time-series validation; And 3) simulated missing regions validation. More validation results can be viewed at <a href="https://qzhang95.github.io/Projects/Global-Daily-Seamless-AMSR2">SGD-SM</a>.</li> <li>An example Python code of extracting this dataset is also available at <a href="https://github.com/qzhang95/SGD-SM">https://github.com/qzhang95/SGD-SM</a>.</li> <li>Official LPRM AMSR2 Descending L3 soil moisture products indeed only have 28 daily files in May 2013 (missing data files in date May 11, May 12, and May 13).</li> <li>This soil moisture dataset is comprised of netCDF4 (*.nc) files. Therefore, users need to install netCDF4 toolkit before reading the data: <pre><code class="language-python">pip install netCDF4 pip install numpy</code></pre>   </li> <li>It should be noted that the original and reconstructed soil moisture data are both recorded in one NC file. User can read the original data, reconstructed data, and mask data as follows:</li> <li> <pre><code class="language-python">Data = nc.Dataset(NC_file_position) Ori_data = Data.variables['original_sm_c1'] Rec_data = Data.variables['reconstructed_sm_c1'] Ori = Ori_data[0:720, 0:1440] Rec = Rec_data[0:720, 0:1440] Mask_ori = np.ma.getmask(Ori)</code></pre>   </li> </ul>

opencc-by-4.0Jan 2021View details →

zenodo44/100

Long-term live imaging and multiscale analysis identify heterogeneity and core principles of epithelial organoid morphogenesis - Image data

The dataset contains raw imaging data from the work: "Long-term live imaging and multiscale analysis identify heterogeneity and core principles of epithelial organoid morphogenesis" The dataset is organized as the following: the "FigureX_" or SupplementaryFigure_X" suffix in the filename refers to the figure in the paper in which the raw data is analyzed and/or visualized. The data is "raw", i.e. not processed. However, in many cases, maximum projections of the original 3D image stacks have been uploaded due to size limitations. The total size of the image stacks approaches 0.5TB. To access the full 3D image stacks please contact the corresponding author (Francesco Pampaloni, fpampalo@bio.uni-frankfurt.de). Authors Lotta Hof1*, Till Moreth1*, Michael Koch1, Tim Liebisch2, Marina Kurtz3, Julia Tarnick4, Susanna M. Lissek5, Monique M.A. Verstegen6, Luc J.W. van der Laan6, Meritxell Huch7, Franziska Matthäus2, Ernst H.K. Stelzer1, Francesco Pampaloni1§ 1Physical Biology Group, Buchmann Institute for Molecular Life Sciences (BMLS), Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany 2Faculty of Biological Sciences, Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany 3Department of Physics, Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany 4Deanery of Biomedical Science, University of Edinburgh, Edinburgh, United Kingdom 5Experimental Medicine and Therapy Research, University of Regensburg, Regensburg, Germany 6Department of Surgery, Erasmus MC – University Medical Center, Rotterdam, The Netherlands 7The Wellcome Trust/CRUK Gurdon Institute, University of Cambridge, Cambridge, United Kingdom. Present address: Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany *contributed equally §corresponding author: fpampalo@bio.uni-frankfurt.de Abstract Background Organoids are morphologically heterogeneous three-dimensional cell culture systems and serve as an ideal model for understanding the principles of collective cell behaviour in mammalian organs during development, homeostasis, regeneration and pathogenesis. To investigate the underlying cell organisation principles of organoids, we imaged hundreds of pancreas and cholangio carcinoma organoids in parallel using light sheet and bright field microscopy for up to seven days. Results We quantified organoid behaviour at single-cell (microscale), individual-organoid (mesoscale), and entire-culture (macroscale) levels. At single-cell resolution, we monitored formation, monolayer polarisation and degeneration, and identified diverse behaviours, including lumen expansion and decline (size oscillation), migration, rotation and multi-organoid fusion. Detailed individual organoid quantifications lead to a mechanical 3D agent-based model. A derived scaling law and simulations support the hypotheses that size oscillations depend on organoid properties and cell division dynamics, which is confirmed by bright field microscopy analysis of entire cultures. Conclusion Our multiscale analysis provides a systematic picture of the diversity of cell organisation in organoids by identifying and quantifying the core regulatory principles of organoid morphogenesis.

opencc-by-4.0Jan 2021View details →

zenodo44/100

CLRD-GLPS: A Long-term Seasonal Dataset of Ruminant Livestock Distribution in China's Grazing Production Systems (2000-2021) Using Stacking-based Interpretable Machine Learning

Advanced computational methods integrating ensemble learning with interpretable machine learning are essential for precision livestock management under increasing environmental constraints and food security pressures. This study develops a novel stacking-based interpretable machine learning (IML) framework that combines multiple algorithms with SHAP analysis techniques to generate the China's Long-term Ruminant Livestock Distribution in Grazing Livestock Production Systems (CLRD-GLPS) dataset. Our computational approach addresses critical challenges in livestock distribution modelling: livestock segmentation and spatial prediction accuracy. The framework integrates Random Forest, XGBoost, CatBoost, LightGBM, and Extra Trees through a two-layer stacking architecture, enhanced with SHAP (Shapley Additive Explanations) analysis for model interpretability. We also implemented interpretable machine learning for livestock production system segmentation to distinguish grazing from total livestock populations. The stacking ensemble demonstrated superior performance over individual algorithms, achieving R² values of 0.954-0.961 for cattle and 0.896-0.901 for sheep and goats, with improvements of up to 8.3% compared to best performance single-model approaches. Multi-scale validation confirmed computational robustness: livestock segmentation achieved R² = 0.80 at county level, while independent city-level validation of CLRD-GLPS datasets yielded R² = 0.76-0.80. SHAP interpretability analysis revealed distinct environmental drivers, with vegetation indices and topography primarily influencing cattle distribution, while snow conditions and elevation dominated sheep and goat patterns. This computational framework advances livestock distribution modelling through enhanced prediction accuracy, model stability, and interpretability, while the CLRD-GLPS dataset provides essential spatial-temporal information for rangeland sustainability assessments and evidence-based livestock management policies. This dataset is supported by the Second Tibetan Plateau Scientific Expedition and Research Program (STEP, grant no. 2019QZKK0906).

opencc-by-4.0Nov 2024View details →

zenodo44/100

Exploring the critical zone heterogeneity and the hydrological diversity using an integrated ecohydrological model in three contrasted long-term observatories

These files provide useful data and supplementary material associated with the publication 'Exploring the critical zone heterogeneity and the hydrological diversity using an integrated ecohydrological model in three contrasted long-term observatories' (MNT information, atmospheric forcings, R scripts used to process and draw the graphs from the EcH2O-iso simulations, and observed water discharges).

opencc-by-4.0Oct 2023View details →

zenodo44/100

Long-Term Net Primary Productivity Dataset of the Tibetan Plateau from 1982 to 2013

<ul><li>This dataset is generated by the advanced CASA model, encompassing vegetation net primary productivity (NPP) raster data for the Tibetan Plateau from 1982 to 2013. The model's input parameters comprise NDVI time series, monthly average temperature, monthly total precipitation, monthly total solar radiation, and vegetation type. The data is provided at an 8 km × 8 km spatial resolution and is formatted in ENVI format (.dat).</li><li>Remarkably, during cross-validation with the MODIS 500m resolution product (MOD17A3HGF.061), it exhibited significantly strong correlations, with the correlation coefficients (R) ranging from 0.74 to 0.82.</li><li>Please cite this dataset as Tan, Q., Sun, G., & Pang, Y. (2023). Long-Term Net Primary Productivity Dataset of the Tibetan Plateau from 1982 to 2013 (V1.0) [Data set]. Zenodo. https://doi.org/<a href="https://doi.org/10.5281/zenodo.10040818">10.5281/zenodo.10040818</a></li></ul>

opencc-by-4.0Oct 2023View details →

zenodo44/100

Long-term monitoring of the fish community in the Minho Estuary (NW Iberian Peninsula)

The dataset contains data from fyke nets deployed in the Minho Estuary (Portugal) from 2010 to 2019. The fyke nets were used for fish sampling and data collection. The sampling frequency varied but, on average, data was collected weekly using five different fyke nets. However, due to technical issues (e.g. lost or damaged fyke nets), the sampling pattern is not constant, with some fyke nets staying underwater for shorter or longer periods, and occasionally having fewer than five fyke nets per parentEventID. The dataset includes various terms such as parentEventID, eventID, eventDate, year, startDayOfYear, endDayOfYear, country, countryCode, geodeticDatum, decimalLatitude, decimalLongitude, coordinateUncertaintyInMeters, DEIMS.iD, habitat, basisOfRecord, samplingProtocol, sampleSizeValue, sampleSizeUnit, samplingEffort, occurrenceStatus, occurrenceID, organismQuantity, organismQuantityType, degreeOfEstablishment, vernacularName, scientificName, acceptedNameUsageID, taxonRank, kingdom, phylum, order, family, genus, and scientificNameAuthorship.

opencc-by-4.0Jun 2023View details →

zenodo44/100

Shapefiles showing the locations of long-term climate change refugia and hotspots identified in the FairSeas report "A Climate Resilient Path for Ireland's Marine Protected Areas Network"

Shapefiles created for the report "A Climate Resilient Path for Ireland’s Marine Protected Areas Network", an addendum chapter to "Revitalising Our Seas report: Identifying Areas of Interest for Marine Protected Area Designation in Irish Waters" These shapefiles summarise long-term patterns that emerge from the spatial-meta analysis of physical-biogeochemical and species distribution modelling data, providing an overview of the distribution of climate change refugia and climate change hotspots across Ireland's National Marine Planning Framework between 2026 - 2069, and across the two emissions scenarios considered in the report (RCP4.5 and RCP8.5).  Filenames refer to the specific analysis each set of shapefiles belong to: Benthic habitats, benthic megafauna, pelagic habitats, pelagic megafauna and forage fish. Details of the modelling datasets used in each of these analyses, the meta-analysis method and shapefile creation can be found in Annex A1 in the report "A Climate Resilient Path for Ireland’s Marine Protected Areas Network".

opencc-by-4.0Mar 2024View details →

zenodo44/100

Change detection technique comparison in long-term wetland monitoring: datasets and maps of the Poitevin Marsh (France)

<h3>For a full description of the methodology and results, please see the following article:</h3> <div> <div>Demarquet, Q., Rapinel, S., Gore, O., Dufour, S., Hubert-Moy, L., 2024. Continuous change detection outperforms traditional post-classification change detection for long term monitoring of wetlands. International Journal of Applied Earth Observation and Geoinformation 133, 104142. <a href="https://doi.org/10.1016/j.jag.2024.104142">https://doi.org/10.1016/j.jag.2024.104142</a></div> <div> </div> <div>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</div> </div> <h3># Datasets</h3> Points datasets are projected in WGS84 (EPSG:4326), and are provided in the open source GeoPackage format. The first dataset (Dataset_1.gpkg) contains training and validation points for random forest classification of EUNIS habitats in the Poitevin Marsh. This dataset consists of 3360 training and 840 validation points (total:  4200). Fields description: <ul> <li>"ID": unique identifier</li> <li>"CLASS": EUNIS first level habitat type, classified as following: <ul> <li>1: EUNIS habitat A</li> <li>2: EUNIS habitat B</li> <li>3: EUNIS habitat C1J5</li> <li>4: EUNIS habitat C3</li> <li>5: EUNIS habitat E</li> <li>6: EUNIS habitat G</li> <li>7: EUNIS habitat I</li> <li>8: EUNIS habitat J</li> </ul> </li> <li>"DATE": Date associated with EUNIS habitat sample</li> <li>"LON": Point longitude in decimal degrees</li> <li>"LAT": Point latitude in decimal degrees</li> <li>"TYPE": Either training ("train") or validation ("test") sample</li> </ul> The second dataset (Dataset_2.gpkg) contains points for the Olofsson correction method. This dataset consists of 326 points where the change classes are classified as following: -10 (wetland loss), 10 (wetland gain), 100 (stable existing wetland), and 200 (stable damaged wetland). Fields description: <ul> <li>"ID": unique identifier</li> <li>"LON": Point longitude in decimal degrees</li> <li>"LAT": Point latitude in decimal degrees</li> <li>"REFERENCE": Change class reference</li> <li>"CCDC": Change class obtained from the Continuous Change Detection and Classification approach</li> <li>"PCCD": Change class obtained from the Post-Classification Change Detection approach</li> </ul> Supplementary layout files (Dataset_1.qml and Dataset_2.qml) support formatting of the points in QGIS software. -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- <h3># EUNIS habitat</h3> Maps are projected in WGS84 (EPSG:4326), and are provided in the GeoTiff format at 30m of spatial resolution.  Habitat maps are given for the two approaches in years 1984 and 2022: <ul> <li>CCDC: Continuous Change Detection and Classification (CCDC_HABITAT_1984.tif and CCDC_HABITAT_2022.tif)</li> <li>PCCD: Traditional post-classification approach (PCCD_HABITAT_1984.tif and PCCD_HABITAT_2022.tif)</li> </ul> Supplementary layout files (CCDC_HABITAT_1984.qml, CCDC_HABITAT_2022.qml, PCCD_HABITAT_1984.qml, PCCD_HABITAT_2022.qml) support formatting of raster layers in QGIS software. -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- <h3># Change detection during the 1984-2022 period</h3> Maps are projected in WGS84 (EPSG:4326), and are provided in the GeoTiff format at 30m of spatial resolution. Raster values follow the classification scheme used in Dataset_2. Change detection maps are given for the two approaches: <ul> <li>CCDC: Continuous Change Detection and Classification (CCDC_CHANGE_1984_2022.tif)</li> <li>PCCD: Traditional post-classification approach (PCCD_CHANGE_1984_2022.tif)</li> </ul> Supplementary layer files (CCDC_CHANGE_1984_2022.qml and PCCD_CHANGE_1984_2022.qml) support formatting of the raster layers in QGIS software. -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- <h3># GEE repository</h3> To get direct access to GEE scripts and assets, please follow those two links: https://code.earthengine.google.com/?accept_repo=users/demarquetquentin/CCDC_Poitevin https://code.earthengine.google.com/?asset=projects/ee-quen-dem/assets/CCDC_Poitevin

opencc-by-4.0Mar 2024View details →

zenodo44/100

DELTA: Dense Electromyography for Long-Term Adaptive control

The DELTA dataset, namely ”Dense Electromyography for Long-Term Adaptive control”, holds significance in the realm of prosthetic applications, featuring High Density surface Electromyography data collected over an extended duration. It serves as a valuable resource for training data-driven models and testing them under conditions that closely emulate real-world prosthetic applications. Recognizing the temporal variability in EMG data, constructing models that are agnostic to such fluctuations could significantly enhance the efficacy of machine learning models in prosthetic applications

opencc-by-4.0Mar 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

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

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

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 →

zenodo44/100

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 →

zenodo44/100

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 →

zenodo44/100

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 →

zenodo44/100

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 →

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 →

ScienceDex guides

Understand access before you commit

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

Compare curated datasets

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

Find research datasets worth reusing

Dataset results

Long-Term Tracing of Indoor Solar Harvesting

Quantifying hail and lightning risk factors using long-term observations around Australia - Hail and Lightning Datasets

SGD-SM: Generating Seamless Global Daily AMSR2 Soil Moisture Long-term Products (2013-2019)

Long-term live imaging and multiscale analysis identify heterogeneity and core principles of epithelial organoid morphogenesis - Image data

CLRD-GLPS: A Long-term Seasonal Dataset of Ruminant Livestock Distribution in China's Grazing Production Systems (2000-2021) Using Stacking-based Interpretable Machine Learning

Exploring the critical zone heterogeneity and the hydrological diversity using an integrated ecohydrological model in three contrasted long-term observatories

Long-Term Net Primary Productivity Dataset of the Tibetan Plateau from 1982 to 2013

Long-term monitoring of the fish community in the Minho Estuary (NW Iberian Peninsula)

Shapefiles showing the locations of long-term climate change refugia and hotspots identified in the FairSeas report "A Climate Resilient Path for Ireland's Marine Protected Areas Network"

Change detection technique comparison in long-term wetland monitoring: datasets and maps of the Poitevin Marsh (France)

DELTA: Dense Electromyography for Long-Term Adaptive control

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

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

Simulation of the Long-Term Evolution of a Meandering River

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"

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

Understand access before you commit

Allen Brain Atlas

Annotated Behaviour and Observability Dataset (ABODe)

DANDI Archive for NWB datasets

International Brain Laboratory public data

OpenNeuro

Your academic CV and NIH biosketch