Find research datasets worth reusing

Data from: Combined experimental-numerical analysis of the temperature evolution and distribution during friction surfacing

This dataset contains the data for the publication "Combined experimental-numerical analysis of the temperature evolution and distribution during friction surfacing".

Distribution of large carnivores in Europe 2012 - 2016: Distribution map for Golden Jackal (Canis aureus)

Abstract Regular assessments of species’ status are an essential component of conservation planning and adaptive management. They allow the progress of past or ongoing conservation actions to be evaluated and can be used to redirect and prioritize future conservation actions. Most countries perform periodic assessments for their own national adaptive management procedures or national red lists. Furthermore, the countries of the European Union have to report on the status of all species listed on the directives of the Habitats Directive every 6 years as part of their obligations under Article 17. However, these national level assessments are often made using non-standardized procedures and do not always adequately reflect the biological units (i.e., the populations) which are needed for ecologically meaningful assessments. Since the early 2000’s the Large Carnivore Initiative for Europe (a Specialist Group of the IUCN’s Species Survival Commission) has been coordinating periodic surveys of the status of large carnivores across Europe (e.g., von Arx et al. 2004; Salvatori & Linnell 2005, Kaczensky et al. 2013). These have covered the Eurasian lynx (Lynx lynx), the wolf (Canis lupus), the brown bear (Ursus arctos) and the wolverine (Gulo gulo). The golden jackal (Canis aureus) has been added to the LCIE prerogatives in 2014. The species is rapidly expanding in Europe (Trouwborst et al. 2015; Männil & Ranc 2022), a large-scale phenomenon that resembles that of the other large carnivores. Golden jackals are thriving in human-dominated landscapes (Ćirović et al. 2016; Lanszki et al. 2018; Fenton et al. 2021), where they are often functioning as the top predators, despite having smaller body size that is typical for large carnivores. The expansion of the species triggers many questions among scientists, stakeholders, and policy makers (Trouwborst et al. 2015; Hatlauf et al. 2021), that are closely connected to those raised by the other large carnivores (e.g., potential conflicts with livestock or hunting). In this context, monitoring the species’ expansion, delineating populations, assessing the species' legal and protection status, and addressing the concerns raised by this rapidly expanding carnivore requires a high level of coordination among regional experts. These surveys involve the contributions of the best available experts and sources of information. While the underlying data quality and field methodology varies widely across Europe, these coordinated assessments do their best to integrate the diverse data in a comparable manner and make the differences transparent. They also endeavor to conduct the assessments on the most important scales. This includes the continental scale (all countries except for Russia, Belarus, Moldova and the parts of Ukraine outside the Carpathian Mountain range), the scale of the EU 28 (where the Habitats Directive operates) and of the biological populations which reflect the scale at which ecological processes occur (Linnell et al. 2008). In this way, the independent LCIE assessments provide a valuable complement to the ongoing national processes. Our last assessments covered the period 2006-2011 (Kaczensky et al. 2013; Chapron et al. 2014) but, at the time, did not include golden jackals. The current assessment is based on the period 2012-2016 and broadly follows the same methodology. Explicit distinctions are made between classification based on empirical data and expert opinion. The population definitions used in this report follow those proposed in (Ranc et al. 2018); areas whose presence category was defined by expert opinion were not assigned to a specific population, though.    Methods The mapping approach follows the methods described in Chapron et al. (2014) and Kaczensky et al. (2013). It updates the published Species Online Layers (SPOIS) to the period 2012-2016. In short, large carnivore presence was mapped at a 10x10 km ETRS89-LAEA Europe grid scale. This grid is widely used for the Flora-Fauna-Habitat reporting by the European Union (EU) and can be downloaded at: http://www.eea.europa.eu/data-and-maps/data/eea-reference-grids-2 The map encompasses the EU countries plus the non-EU Balkan states, Switzerland, Norway, and the Carpathian region of Ukraine. Presence in a grid cell was ideally mapped based on carnivore presence and frequency in a cell resulting in: 1 = Permanent (presence confirmed in >= 3 years in the last 5 years OR in >50% of the time OR reproduction confirmed within the last 3 years) 3 = Sporadic (highly fluctuating presence) (presence confirmed in <3 years in the last 5 years OR in <50% of the time) 5 = Expert-based presence (high confidence) (expert-based opinion; very suitable habitat near permanent presence areas) 6 = Expert-based presence (low confidence or unconfirmed records) (expert-based opinion; suitable habitat near presence areas or unconfirmed C3 records of jackal presence) 7 = Expert-based absence (high confidence) (jackal presence according to coarse-resolution hunting bag data but experts think, with high confidence, the species is not present) 8 = Expert-based absence (low confidence) (jackal presence according to coarse-resolution hunting bag data but experts think the species is not present) Where grid cells were assigned different values between neighboring countries; the “disputed” cells were given the “higher” presence values e.g., a cell categorized as “sporadic” by one country and “permanent” by another was categorized as “permanent”. Data-based categories (1,3) were given priority over expert-based categories (5 through 8). To assess the quality of carnivore signs we used the SCALP criteria developed for the standardized monitoring of Eurasian lynx (Lynx lynx) in the Alps (Molinari-Jobin et al. 2012): Category 1 (C1): “Hard facts”, verified and unchallenged large carnivore presence signs (e.g., dead animals, DNA, verified camera trap images); Category 2 (C2): Large carnivore presence signs controlled and confirmed by a large carnivore expert (e.g., trained member of the network), which requires documentation of large carnivore signs; and Category 3 (C3): Unconfirmed category 2 large carnivore presence signs and all presence signs such as sightings and calls which, if not additionally documented, cannot be verified. See Hatlauf and Böcker (2022) for best practices regarding golden jackal records.   Usage Notes The data available consists of a shapefile at a 10 x 10 km resolution compiled for the period 2012-2016 for the Large Carnivore Initiative of Europe IUCN Specialist Group and for the IUCN Red List Assessment.   References Boitani, L., F. Alvarez, O. Anders, H. Andren, E. Avanzinelli, V. Balys, J. C. Blanco, U. Breitenmoser, G. Chapron, P. Ciucci, A. Dutsov, C. Groff, D. Huber, O. Ionescu, F. Knauer, I. Kojola, J. Kubala, M. Kutal, J. Linnell, A. Majic, P. Mannil, R. Manz, F. Marucco, D. Melovski, A. Molinari, H. Norberg, S. Nowak, J. Ozolins, S. Palazon, H. Potocnik, P.-Y. Quenette, I. Reinhardt, R. Rigg, N. Selva, A. Sergiel, M. Shkvyria, J. Swenson, A. Trajce, M. Von Arx, M. Wolfl, U. Wotschikowsky and D. Zlatanova. 2015. Key actions for Large Carnivore populations in Europe. Institute of Applied Ecology (Rome, Italy). Report to DG Environment, European Commission, Bruxelles. Contract no. 07.0307/2013/654446/SER/B3 Ćirović, D., A. Penezić and M. Krofel. 2016. Jackals as cleaners: Ecosystem services provided by a mesocarnivore in human-dominated landscapes. Biological Conservation, 199: 51–55. Chapron, G., Kaczensky, P., Linnell, J.D.C., von Arx, M., Huber, D., Andrén, H., López-Bao, J.V., Adamec, M., Álvares, F., Anders, O., Balčiauskas, L., Balys, V., Bedő, P., Bego, F., Blanco, J.C., Breitenmoser, U., Brøseth, H., Bufka, L., Bunikyte, R., Ciucci, P., Dutsov, A., Engleder, T., Fuxjäger, C., Groff, C., Holmala, K., Hoxha, B., Iliopoulos, Y., Ionescu, O., Jeremić, J., Jerina, K., Kluth, G., Knauer, F., Kojola, I., Kos, I., Krofel, M., Kubala, J., Kunovac, S., Kusak, J., Kutal, M., Liberg, O., Majić, A., Männil, P., Manz, R., Marboutin, E., Marucco, F., Melovski, D., Mersini, K., Mertzanis, Y., Mysłajek, R.W., Nowak, S., Odden, J., Ozolins, J., Palomero, G., Paunović, M., Persson, J., Potočnik, H., Quenette, P.-Y., Rauer, G., Reinhardt, I., Rigg, R., Ryser, A., Salvatori, V., Skrbin&scaron;ek, T., Stojanov, A., Swenson, J.E., Szemethy, L., Trajçe, A., Tsingarska[1]Sedefcheva, E., Váňa, M., Veeroja, R., Wabakken, P., Wölfl, M., Wölfl, S., Zimmermann, F., Zlatanova, D. and Boitani, L. 2014. Recovery of large carnivores in Europe’s modern human-dominated landscapes. Science 346: 1517-1519. Fenton, S., Moorcroft, P.R., Ćirović, D., Lanszki, J., Heltai, M., Cagnacci, F., Breck, S., Bogdanović, N., Pantelić, I., Ács, K. and Ranc, N. 2021. Movement, space-use and resource preferences of European golden jackals in human-dominated landscapes: insights from a telemetry study. Mammalian Biology, 101: 619–630. Hatlauf, J. and Böcker, F. 2022. Recommendations for the documentation and assessment of golden jackal (Canis aureus) records in Europe. BOKU reports on wildlife research and willdife management 27. Ed: Institute of Wildlife Biology and Game Management (IWJ), University of Natural Resources and Life Sciences, Vienna. ISBN: 978-3-900932-94-7 Hatlauf, J., Bayer, K., Trouwborst, A. and Hackländer, K. 2021. New rules or old concepts? The golden jackal (Canis aureus) and its legal status in Central Europe. European Journal of Wildlife Research, 67, 25. Kaczensky, P., Chapron, G., Von Arx, M., Huber, D., Andrén, H. and Linnell, J. 2013. Status, management and distribution of large carnivores - bear, lynx, wolf and wolverine - in Europe. Istituto di Ecologia Applicata, Rome, Italy. Lanszki, J., Schally, G., Heltai, M. and Ranc, N. 2018. Golden jackal expansion in Europe: first telemetry evidence of a natal dispersal. Mammalian Biology, 88: 81–84. Linnell, J.D.C., Salvatori, V. and Boitani, L. 2008. Guidelines for population level management plans for large carnivores in Europe. A Large Carnivore Initiative for Europe report prepared for the European Commission (contract 070501/2005/424162/MAR/B2). Männil, P. and Ranc, N. 2022. Golden jackal (Canis aureus) in Estonia: development of a thriving population in the boreal ecoregion. Mammalian Research, 67: 245-250. Molinari-Jobin, A., Kéry, M., Marboutin, E., Molinari, P., Koren, I., Fuxjäger, C., Breitenmoser-Würsten, C., Wölfl, S., Fasel, M., Kos, I., Wölfl, M. and Breitenmoser, U. 2012. Monitoring in the presence of species misidentification: the case of the Eurasian lynx in the Alps. Animal Conservation 15: 266-273. Ranc, N., Krofel, M. and Cirovic, D. 2018. IUCN Red List Mapping for the regional assessment of the Golden Jackal (Canis aureus) in Europe. IUCN Red List Threatened Species, 13. Salvatori, V. and Linnell, J.D.C. 2005. Report on the conservation status and threats for wolf (Canis lupus) in Europe. Council of Europe Report T-PVS/Inf (2005) 16. Trouwborst, A., Krofel, M. and Linnell, J.D.C. 2015. Legal implications of range expansions in a terrestrial carnivore: the case of the golden jackal (Canis aureus) in Europe. Biodiversity Conservation, 24: 2593–2610. von Arx, M., Breitenmoser-Würsten, C., Zimmermann, F. and Breitenmoser, U. 2004. Status and conservation of the Eurasian lynx (Lynx lynx) in Europe in 2001. KORA Report 19e: 1-330.   Contact information Nathan Ranc, nathan.ranc@inrae.fr

openepl-2.0Mar 2022View details →

Geosci. Model Dev. paper data for Flipo et al., "Regional coupled surface-subsurface hydrological model fitting based on a spatially distributed minimalist reduction of frequency-domain discharge data"

Data and associated user guide, as part of the paper : Flipo N., Gallois N., Schuite J. Regional coupled surface-subsurface hydrological model fitting based on a spatially distributed minimalist reduction of frequency-domain discharge data, Geoscientific Model Development. In consistency with the “Code and data availability” sub-section of the paper, all data necessary for the reproduction of Figs. 7, 8c, 8d, 9, 10 and 11 are here provided.

New dataset obtained from 2D positioning system with distributed control

The dataset contains the obtained trajectory (encoder measurements) of the 2D positioning system, as well as the reference (commanded) trajectory. The control task is distributed to the low-level controllers for x and y axes synchronized using IEEE 1588 Precision Time Protocol (PTP), where the movement of the axes is realized based on the data that the low-level controllers receive from the high-level controller. The dataset includes 60 signals (30 measurements for x and y axis) that represent obtained trajectories and 2 signals (x and y axes) that represent commanded trajectory, where the length of each signal is 61,000 samples. The system was designed and built by the Cyber-Physical Systems Lab at the Pratt School of Engineering, Duke University, where it is located. Table 1 shows the list of collected signals, whereas a detailed description of the system can be found in [1]. For more information, see [2, 3].

Factors determining distributions of rainforest Drosophila shift from interspecific competition to high temperature with decreasing elevation (original datasets)

This repository provides the data for the manuscript "Factors determining distributions of rainforest Drosophila shift from interspecific competition to high temperature with decreasing elevation" We investigated thermal tolerances and interspecific competition as causes of species turnover in the nine most abundant species of Drosophila along elevational gradients in the Australian Wet Tropics. Specifically, we 1) analyzed the distribution patterns of the studies Drosophila species; 2) fitted thermal performance curves; 3) tested the correlation between multiple thermal traits and distribution patterns; 4) fitted the Beverton-Holt model to describe the single-generation intra- and inter-specific competition effect; 5) examined the long-term effect of competition and temperature on the population size of a pair of Drosophila species. More details are provided in the README file.

Lightning flashover simulations on medium voltage distribution lines

[Version 1.2] This version of the dataset fixes a bug found in the previous versions (see below for more information). Dataset has been generated from the Monte Carlo simulations of lightning flashovers on medium voltage (MV) distribution lines. It is suitable for training machine learning models for classifying lightning flashovers on distribution lines, as well as for line insulation coordination studies. The dataset is hierarchical in nature (see below for more information) and class imbalanced. Following five different types of lightning interaction with the MV distribution line have been simulated: (1) direct strike to phase conductor (when there is no shield wire present on the line), (2) direct strike to phase conductor with shield wire(s) present on the line (i.e. shielding failure), (3) direct strike to shield wire with backflashover event, (4) indirect near-by lightning strike to ground where shield wire is not present, and (5) indirect near-by lightning strike to ground where shield wire is present on the line. Last two types of lightning interactions induce overvoltage on the phase conductors by radiating EM fields from the strike channel that are coupled to the line conductors. Shield wire(s) provide shielding effects to direct, as well as screening effects to indirect, lightning strikes. Dataset consists of the following variables: <ul> <li>'dist': perpendicular distance of the lightning strike location from the distribution line axis (m), generated from the Uniform distribution [0, 500] m,</li> <li>'ampl': lightning current amplitude of the strike (kA), generated from the Log-Normal distribution (see IEC 60071 for additional information),</li> <li>'veloc': velocity of the lightning return stroke current (m/us), generated from the Uniform distribution [50, 500] m/us,</li> <li>'shield': binary indicator that signals presence or absence of the shield wire(s) on the line (0/1), generated from the Bernoulli distribution with a 50% probability,</li> <li>'Ri': average value of the impulse impedance of the tower's grounding (Ohm), generated from the Normal distribution (clipped at zero on the left side) with median value of 50 Ohm and standard deviation of 12.5 Ohm; it should be mentioned that the impulse impedance is often much larger than the associated grounding resistance value, which is why a rather high value of 50 Ohm have been used here,</li> <li>'EGM': electrogeometric model used for analyzing striking distances of the distribution line's tower; following options are available: 'Wagner', 'Young', 'AW', 'BW', 'Love', and 'Anderson', where 'AW' stands for Armstrong & Whitehead, while 'BW' means Brown & Whitehead model; statistical distribution of EGM models follows a user-defined discrete categorical distribution with respective probabilities: p = [0.1, 0.2, 0.1, 0.1, 0.3, 0.2],</li> <li>'CFO': critical flashover voltage level of the distribution line's insulation (kV); following three levels have been used: 150, 150, and 160 kV, respectively, for three different distribution lines of height 10, 12, and 14 m,</li> <li>'height': height of the phase conductors of the distribution line (m); distribution line has flat configuration of phase conductors with following heights: 10, 12, and 14 m; twin shield wires, if present, are 1.5 m above the phase conductors and 3 m apart; data set consists of 10000 simulations for each line height,</li> <li>'flash': binary indicator that signals if the flashover has been recorded (1) or not (0). This variable is the outcome (binary class).</li> </ul> Note: It should be mentioned that the critical flashover voltage (CFO) level of the line is taken at 150 kV for the first two lines (10 m and 12 m) and 160 kV for the third line (14 m), and that the diameters of the phase conductors and shield wires for all treated lines are, respectively, 10 mm and 5 mm. Also, average grounding resistance of the shield wire is assumed at 10 Ohm for all treated cases (it has no discernible influence on the flashover rate). Dataset is class imbalanced and consists in total of 30000 simulations, with 10000 simulations for each of the three different MV distribution line heights (geometry) and CFO levels. Important: Version 1.2 of the dataset fixes an important bug found in the previous data sets, where the column 'Ri' contained duplicate data from the column 'veloc'. This issue is now resolved. Mathematical background used for the analysis of lightning interaction with the MV distribution line can be found in the references below. References: J. A. Martinez and F. Gonzalez-Molina, "Statistical evaluation of lightning overvoltages on overhead distribution lines using neural networks," in IEEE Transactions on Power Delivery, vol. 20, no. 3, pp. 2219-2226, July 2005, doi: 10.1109/TPWRD.2005.848734. A. R. Hileman, "Insulation Coordination for Power Systems", CRC Press, Boca Raton, FL, 1999.

opencc-by-4.0Apr 2022View details →

Spatial distribution of oil slicks in the Gulf of Guinea between 2002 and 2012

The map illustrates the spatial distribution of the 18,063 oil slicks that have been detected and then mapped in the Gulf of Guinea over the period 2002-2012 using Envisat ASAR images

Presence-Absence Points for Tree Species Distribution Modelling for Europe

The dataset is a collection of presence and absence points for forest tree species for Europe. Each unique combination of longitude, latitude and year was considered as an independent sample. Presence data was obtained from the harmonized tree species occurrence dataset by <a href="https://zenodo.org/record/5524611">Heisig and Hengl (2020)</a> and absence data from the <a href="https://ec.europa.eu/eurostat/web/lucas">LUCAS</a> (in-situ source) dataset. A set of 50 different forest tree species was selected from the harmonized tree species dataset and data lacking a temporal observation was overlaid with yearly forest masks derived from land cover maps produced by <a href="https://zenodo.org/record/4725429">Parente et al. (2021)</a>. We overlaid the points with the probability maps for the classes: <ul> <li>311: Broad-leaved forest,</li> <li>312: Coniferous forest,</li> <li>313: Mixed forest,</li> <li>323: Sclerophyllous forest,</li> <li>324: Transitional woodland-shrub,</li> <li>333: Sparsely vegetated area.</li> </ul> Points were included in the dataset only if the probability value extracted for at least one of the above classes was ≥ 50% for all the years considered. An additional quality flag was added to distinguish points coming from this operation and the points with original year of observation coming from source datasets. The final dataset contains 4,359,999 observations for and a total of 630 columns.  The first 8 columns of the dataset contain metadata information used to uniquely identify the points: <ul> <li>id: unique point identifier,</li> <li>year: year of observation,</li> <li>postprocess: quality flag to identify if the temporal reference of an observation comes from the original dataset or is the result of spatiotemporal overlay with forest masks,</li> <li>Tile_ID: contains the tile id from the eu_tiling_system (30 km grid),</li> <li>easting: longitude coordinates in Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035),</li> <li>northing: latitude coordinates in Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035),</li> <li>Atlas_class: name of the tree species according to the European Atlas of Forest Tree Species or NULL in case of absence point,</li> <li>lc1: contains original LUCAS land cover class or NULL if it's a presence point.</li> </ul> The remaining columns contain the extracted values of a series of predictor variables (temperature, precipitation, elevation, topographical information, spectral reflectance) useful for species distribution modeling applications. These points were used to model the potential and realized distribution of a series of 16 target species for the period 2000 - 2020. The approach involved training three ML models to predict probability of presence (i.e. <a href="http://link.springer.com/article/10.1023/A:1010933404324">Random Forest</a>, <a href="http://dl.acm.org/doi/abs/10.1145/2939672.2939785">XGBoost</a>, <a href="https://rss.onlinelibrary.wiley.com/doi/abs/10.2307/2344614">GLM</a>), which served as input to train a linear meta-model (i.e. <a href="http://papers.nips.cc/paper/2014/file/ede7e2b6d13a41ddf9f4bdef84fdc737-Paper.pdf">Logistic regression classifier</a>), responsible for predicting the final probability of presence for each species. The RDS file is created from a data.table object and suitable for fast reading in the R-programming environment. The CSV.GZ file contains records as a table with easting and northing in Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035) and can be fed in a GIS after being unzipped. We provide RDS files for a 30km tile as an example containing raster stacks at 30m resolution of all the covariates included in the regression matrix. You can find the specific geographical location of the tile in Europe using the attached GeoPackage ("eu_tiling_system_30km"): open it in QGIS and filter by "ID". In our approach we considered both static and dynamic covariates: dynamic covariates are calculated as averages of a 4 years time window (example: 2004 contains averages from 2002 to 2006). To get the predictions for a specific year, covariates contained in the static RDS file need to be bound with the respective year. To access our predictions (probabilities and uncertainties) produced for the target species access: <ul> <li>Open Data Science Europe viewer: <a href="https://maps.opendatascience.eu">https://maps.opendatascience.eu</a></li> <li>Check the Related identifiers section of this repository to access each species individually</li> </ul> If you instead would like to know more about the creation of this dataset and the modeling: <ul> <li>watch the talk at Open Data Science Workshop 2021 (<a href="https://doi.org/10.5446/55256">TIB AV-PORTAL</a>)</li> <li>access the repository with our R/Python scripts and follow the instructions (<a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/tree/master/veg_tree.species_anv.pnv.eml">GitLab</a>)</li> </ul> A publication describing, in detail, all processing steps, accuracy assessment and general analysis of species distribution maps is available on <a href="https://doi.org/10.7717/peerj.13728">PeerJ</a>. To suggest any improvement/fix use <a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues">https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues</a>.

opencc-by-4.0May 2022View details →

Experimental Assessment of the Thermal Strain Distribution in Nb3Sn React & Wind Conductor Prototype for European DEMO

The measured data, processed data and metadata related to the publication "Experimental Assessment of the Thermal Strain Distribution in Nb3Sn React & Wind Conductor Prototype for European DEMO"  (doi: 10.1109/TASC.2022.3141699) are uploaded.  The raw data correspond to susceptibility measurement as a function of temperature.  Out of this measurement, strand distribution in the superconducting cable is determined by analysis. This work was supported by the Swiss National Science Foundation (SNF) under contract number 200021_179134.

opencc-by-4.0Nov 2018View details →

Estimating the Global Distribution of Field Size using Crowdsourcing

There is increasing evidence that smallholder farms contribute substantially to food production globally yet spatially explicit data on agricultural field sizes are currently lacking. Automated field size delineation using remote sensing or the estimation of average farm size at subnational level using census data are two approaches that have been used but both have limitations, e.g. limited geographical coverage by remote sensing or coarse spatial resolution when using census data. This paper demonstrates another approach to quantifying and mapping field size globally using crowdsourcing. A campaign was run in June 2017 where participants were asked to visually interpret very high resolution satellite imagery from Google Maps and Bing using the Geo-Wiki application. During the campaign, participants collected field size data for 130K unique locations around the globe. Using this sample, we have produced an improved global field size map (over the previous version) and estimated the percentage of different field sizes, ranging from very small to very large, in agricultural areas at global, continental and national levels. The results show that smallholder farms occupy no more than 40% of agricultural areas, which means that, potentially, there are much more smallholder farms in comparison with the current global estimate of 12%. The global field size map and the crowdsourced data set are openly available and can be used for integrated assessment modelling, comparative studies of agricultural dynamics across different contexts and contribute to SDG 2, among many others.   The dataset (global field sizes.zip) contains: - map of dominant field sizes (dominant_field_size_categories.tif) and description of legend items (legend_items.txt) - table with all submissions by the participant (those who completed more than 10 classifications) and table description - table with quality score of all the participants and table description - table with estimated dominant field sizes at each location and table description

opencc-by-4.0Jun 2022View details →

Parameter distributions

Parameter distributions for health economic model hosted on API, some of the rows are considered sensitive, and therefore cannot be edited in requests to the API, whereas others can be, this allows the data-owner to protect sensitive data while allowing others to run complex models that reply on it.

opencc-by-4.0Jun 2022View details →

Dataset of 30 energy customers with flexibility data, and distributed generation, considering residential, small commerce, large commerce, and industrial customers

The dataset has 30 customers: ten residential, ten small commerce, five large commerce, and five industrial customers. The combination of several energy customer types allows the creation of a dataset with different types of consumption profiles, generation, and flexibility, and, therefore, different values of participation in demand response events. The residential profiles of the considered customers use the data available in the Working Group on Intelligent Data Mining and Analysis (IDMA): https://site.ieee.org/pes-iss/data-sets/ The values represent a week period using 15 minutes reading periods. All the values are expressed in kWh and the matrixes were created as [customer x time_period].   We would be grateful if you could acknowledge the use of this dataset in your publications. Please use the Zenodo publication to cite this work.

The dataset of photovoltaic power plant distribution in China by 2020

Photovoltaic (PV) technology, an efficient solution for mitigating the impacts of climate change, has been increasingly used across the world to replace fossil-fuel power to minimize greenhouse gas emissions. With the world's highest cumulative and fastest built PV capacity, China needs to assess the environmental and social impacts of these established photovoltaic (PV) power plants. However, a comprehensive map regarding the PV power plants' locations and extent remain scarce on the country scale. This study developed a workflow combining machine learning and visual interpretation methods with big satellite data to map PV power plants across China. We applied a pixel-based Random Forest (RF) model to classify the PV power plants from composite images in 2020 with 30-meter spatial resolution on Google Earth Engine (GEE). The result classification map was further improved by a visual interpretation approach. Eventually, we established a map of PV power plants in China by 2020, covering a total area of 2917 km2. We found that most PV power plants were sited on cropland, followed by barren land and grassland based on the derived national PV map. In addition, the installation of PV power plants has generally decreased the vegetation cover. This new dataset is expected to be conducive to policy management, environmental assessment, and further classification of PV power plants.

Potential and realized distribution at 30m for Silver fir (Abies alba) in Europe for 2000 - 2020

Probability and uncertainty maps showing the potential and realized distribution for the silver fir (Abies alba, Mill.) for Europe from the dataset prepared by <a href="http://doi.org/10.5281/zenodo.5818021">Bonannella et al. (2022)</a> and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020, split in the following time periods: <ul> <li>2000 - 2002,</li> <li>2002 - 2006,</li> <li>2006 - 2010,</li> <li>2010 - 2014,</li> <li>2014 - 2018,</li> <li>2018 - 2020.</li> </ul> Files are named according to the following naming convention, e.g: <ul> <li>veg_abies.alba_anv.eml_md_30m_0..0cm_2000..2002_eumap_epsg3035_v0.3</li> </ul> with the following fields: <ul> <li>theme: e.g. veg,</li> <li>species code: e.g. abies.alba,</li> <li>species distribution type: e.g. anv (= actual natural vegetation),</li> <li>species estimation method: e.g. eml,</li> <li>species estimation type: e.g. md ( = model deviation),</li> <li>resolution in meters e.g. 30m,</li> <li>reference depths (vertical dimension): e.g. 0..0cm,</li> <li>reference period begin end: e.g. 2000..2002,</li> <li>reference area: e.g. eumap,</li> <li>coordinate system: e.g. epsg3035,</li> <li>data set version: e.g. v0.3.</li> </ul> For each species is then easy to identify probability and uncertainty distribution maps: <ul> <li>veg_abies.alba_anv.eml_md: model uncertainty for realized distribution</li> <li>veg_abies.alba_anv.eml_p: probability for realized distribution</li> <li>veg_abies.alba_pnv.eml_md: model uncertainty for potential distribution</li> <li>veg_abies.alba_pnv.eml_p: probability for potential distribution</li> </ul> Files are provided as <a href="https://gdal.org/drivers/raster/cog.html">Cloud Optimized GeoTIFFs</a> and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files are provided in both SLD and QML format. If you would like to know more about the creation of the maps and the modeling: <ul> <li>watch the talk at Open Data Science Workshop 2021 (<a href="https://doi.org/10.5446/55256">TIB AV-PORTAL</a>)</li> <li>access the repository with our R/Python scripts and follow the instructions (<a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/tree/master/veg_tree.species_anv.pnv.eml">GitLab</a>)</li> <li>access the repository with the training dataset (<a href="https://doi.org/10.5281/zenodo.5818021">Zenodo</a>)</li> <li>read the tutorial with executable code on our <a href="https://opengeohub.github.io/spatial-prediction-eml/spatiotemporal-ml.html#spatiotemporal-distribution-of-fagus-sylvatica">GitBook</a></li> </ul> A publication describing, in detail, all processing steps, accuracy assessment and general analysis of species distribution maps is available on <a href="https://doi.org/10.7717/peerj.13728">PeerJ</a>. To suggest any improvement/fix use <a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues">https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues</a>.

Potential and realized distribution at 30m for Sweet chestnut (Castanea sativa) in Europe for 2000 - 2020

Probability and uncertainty maps showing the potential and realized distribution for the sweet chestnut (Castanea sativa, Mill.) for Europe from the dataset prepared by <a href="http://doi.org/10.5281/zenodo.5818021">Bonannella et al. (2022)</a> and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020, split in the following time periods: <ul> <li>2000 - 2002,</li> <li>2002 - 2006,</li> <li>2006 - 2010,</li> <li>2010 - 2014,</li> <li>2014 - 2018,</li> <li>2018 - 2020.</li> </ul> Files are named according to the following naming convention, e.g: <ul> <li>veg_castanea.sativa_anv.eml_md_30m_0..0cm_2000..2002_eumap_epsg3035_v0.3</li> </ul> with the following fields: <ul> <li>theme: e.g. veg,</li> <li>species code: e.g. castanea.sativa,</li> <li>species distribution type: e.g. anv (= actual natural vegetation),</li> <li>species estimation method: e.g. eml,</li> <li>species estimation type: e.g. md ( = model deviation),</li> <li>resolution in meters e.g. 30m,</li> <li>reference depths (vertical dimension): e.g. 0..0cm,</li> <li>reference period begin end: e.g. 2000..2002,</li> <li>reference area: e.g. eumap,</li> <li>coordinate system: e.g. epsg3035,</li> <li>data set version: e.g. v0.3.</li> </ul> For each species is then easy to identify probability and uncertainty distribution maps: <ul> <li>veg_castanea.sativa_anv.eml_md: model uncertainty for realized distribution</li> <li>veg_castanea.sativa_anv.eml_p: probability for realized distribution</li> <li>veg_castanea.sativa_pnv.eml_md: model uncertainty for potential distribution</li> <li>veg_castanea.sativa_pnv.eml_p: probability for potential distribution</li> </ul> Files are provided as <a href="https://gdal.org/drivers/raster/cog.html">Cloud Optimized GeoTIFFs</a> and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files are provided in both SLD and QML format. If you would like to know more about the creation of the maps and the modeling: <ul> <li>watch the talk at Open Data Science Workshop 2021 (<a href="https://doi.org/10.5446/55256">TIB AV-PORTAL</a>)</li> <li>access the repository with our R/Python scripts and follow the instructions (<a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/tree/master/veg_mapping">GitLab</a>)</li> <li>access the repository with the training dataset (<a href="https://doi.org/10.5281/zenodo.5818021">Zenodo</a>)</li> <li>read the tutorial with executable code on our <a href="https://opengeohub.github.io/spatial-prediction-eml/spatiotemporal-ml.html#spatiotemporal-distribution-of-fagus-sylvatica">GitBook</a></li> </ul> A publication describing, in detail, all processing steps, accuracy assessment and general analysis of species distribution maps is available on <a href="https://doi.org/10.7717/peerj.13728">PeerJ</a>. To suggest any improvement/fix use <a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues">https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues</a>.

Potential and realized distribution at 30m for the European beech (Fagus sylvatica) in Europe for 2000 - 2020

Probability and uncertainty maps showing the potential and realized distribution for the European beech (Fagus sylvatica, Mill.) for Europe from the dataset prepared by <a href="http://doi.org/10.5281/zenodo.5818021">Bonannella et al. (2022)</a> and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020, split in the following time periods: <ul> <li>2000 - 2002,</li> <li>2002 - 2006,</li> <li>2006 - 2010,</li> <li>2010 - 2014,</li> <li>2014 - 2018,</li> <li>2018 - 2020.</li> </ul> Files are named according to the following naming convention, e.g: <ul> <li>veg_fagus.sylvatica_anv.eml_md_30m_0..0cm_2000..2002_eumap_epsg3035_v0.3</li> </ul> with the following fields: <ul> <li>theme: e.g. veg,</li> <li>species code: e.g. fagus.sylvatica,</li> <li>species distribution type: e.g. anv (= actual natural vegetation),</li> <li>species estimation method: e.g. eml,</li> <li>species estimation type: e.g. md ( = model deviation),</li> <li>resolution in meters e.g. 30m,</li> <li>reference depths (vertical dimension): e.g. 0..0cm,</li> <li>reference period begin end: e.g. 2000..2002,</li> <li>reference area: e.g. eumap,</li> <li>coordinate system: e.g. epsg3035,</li> <li>data set version: e.g. v0.3.</li> </ul> For each species is then easy to identify probability and uncertainty distribution maps: <ul> <li>veg_fagus.sylvatica_anv.eml_md: model uncertainty for realized distribution</li> <li>veg_fagus.sylvatica_anv.eml_p: probability for realized distribution</li> <li>veg_fagus.sylvatica_pnv.eml_md: model uncertainty for potential distribution</li> <li>veg_fagus.sylvatica_pnv.eml_p: probability for potential distribution</li> </ul> Files are provided as <a href="https://gdal.org/drivers/raster/cog.html">Cloud Optimized GeoTIFFs</a> and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files are provided in both SLD and QML format. If you would like to know more about the creation of the maps and the modeling: <ul> <li>watch the talk at Open Data Science Workshop 2021 (<a href="https://doi.org/10.5446/55256">TIB AV-PORTAL</a>)</li> <li>access the repository with our R/Python scripts and follow the instructions (<a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/tree/master/veg_mapping">GitLab</a>)</li> <li>access the repository with the training dataset (<a href="https://doi.org/10.5281/zenodo.5818021">Zenodo</a>)</li> <li>read the tutorial with executable code on our <a href="https://opengeohub.github.io/spatial-prediction-eml/spatiotemporal-ml.html#spatiotemporal-distribution-of-fagus-sylvatica">GitBook</a></li> </ul> A publication describing, in detail, all processing steps, accuracy assessment and general analysis of species distribution maps is available on <a href="https://doi.org/10.7717/peerj.13728">PeerJ</a>. To suggest any improvement/fix use <a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues">https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues</a>.

Potential and realized distribution at 30m for Olive tree (Olea europaea) in Europe for 2000 - 2020

Probability and uncertainty maps showing the potential and realized distribution for the olive tree (Olea europaea, L.) for Europe from the dataset prepared by <a href="http://doi.org/10.5281/zenodo.5818021">Bonannella et al. (2022)</a> and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020, split in the following time periods: <ul> <li>2000 - 2002,</li> <li>2002 - 2006,</li> <li>2006 - 2010,</li> <li>2010 - 2014,</li> <li>2014 - 2018,</li> <li>2018 - 2020.</li> </ul> Files are named according to the following naming convention, e.g: <ul> <li>veg_olea.europaea_anv.eml_md_30m_0..0cm_2000..2002_eumap_epsg3035_v0.3</li> </ul> with the following fields: <ul> <li>theme: e.g. veg,</li> <li>species code: e.g. olea.europaea,</li> <li>species distribution type: e.g. anv (= actual natural vegetation),</li> <li>species estimation method: e.g. eml,</li> <li>species estimation type: e.g. md ( = model deviation),</li> <li>resolution in meters e.g. 30m,</li> <li>reference depths (vertical dimension): e.g. 0..0cm,</li> <li>reference period begin end: e.g. 2000..2002,</li> <li>reference area: e.g. eumap,</li> <li>coordinate system: e.g. epsg3035,</li> <li>data set version: e.g. v0.3.</li> </ul> For each species is then easy to identify probability and uncertainty distribution maps: <ul> <li>veg_olea.europaea_anv.eml_md: model uncertainty for realized distribution</li> <li>veg_olea.europaea_anv.eml_p: probability for realized distribution</li> <li>veg_olea.europaea_pnv.eml_md: model uncertainty for potential distribution</li> <li>veg_olea.europaea_pnv.eml_p: probability for potential distribution</li> </ul> Files are provided as <a href="https://gdal.org/drivers/raster/cog.html">Cloud Optimized GeoTIFFs</a> and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files are provided in both SLD and QML format. If you would like to know more about the creation of the maps and the modeling: <ul> <li>watch the talk at Open Data Science Workshop 2021 (<a href="https://doi.org/10.5446/55256">TIB AV-PORTAL</a>)</li> <li>access the repository with our R/Python scripts and follow the instructions (<a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/tree/master/veg_mapping">GitLab</a>)</li> <li>access the repository with the training dataset (<a href="https://doi.org/10.5281/zenodo.5818021">Zenodo</a>)</li> <li>read the tutorial with executable code on our <a href="https://opengeohub.github.io/spatial-prediction-eml/spatiotemporal-ml.html#spatiotemporal-distribution-of-fagus-sylvatica">GitBook</a></li> </ul> A publication describing, in detail, all processing steps, accuracy assessment and general analysis of species distribution maps is available on <a href="https://doi.org/10.7717/peerj.13728">PeerJ</a>. To suggest any improvement/fix use <a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues">https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues</a>.

Potential and realized distribution at 30m for Common hazel (Corylus avellana) in Europe for 2000 - 2020

Probability and uncertainty maps showing the potential and realized distribution for the common hazel (Corylus avellana, L.) for Europe from the dataset prepared by <a href="http://doi.org/10.5281/zenodo.5818021">Bonannella et al. (2022)</a> and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020, split in the following time periods: <ul> <li>2000 - 2002,</li> <li>2002 - 2006,</li> <li>2006 - 2010,</li> <li>2010 - 2014,</li> <li>2014 - 2018,</li> <li>2018 - 2020.</li> </ul> Files are named according to the following naming convention, e.g: <ul> <li>veg_corylus.avellana_anv.eml_md_30m_0..0cm_2000..2002_eumap_epsg3035_v0.3</li> </ul> with the following fields: <ul> <li>theme: e.g. veg,</li> <li>species code: e.g. corylus.avellana,</li> <li>species distribution type: e.g. anv (= actual natural vegetation),</li> <li>species estimation method: e.g. eml,</li> <li>species estimation type: e.g. md ( = model deviation),</li> <li>resolution in meters e.g. 30m,</li> <li>reference depths (vertical dimension): e.g. 0..0cm,</li> <li>reference period begin end: e.g. 2000..2002,</li> <li>reference area: e.g. eumap,</li> <li>coordinate system: e.g. epsg3035,</li> <li>data set version: e.g. v0.3.</li> </ul> For each species is then easy to identify probability and uncertainty distribution maps: <ul> <li>veg_corylus.avellana_anv.eml_md: model uncertainty for realized distribution</li> <li>veg_corylus.avellana_anv.eml_p: probability for realized distribution</li> <li>veg_corylus.avellana_pnv.eml_md: model uncertainty for potential distribution</li> <li>veg_corylus.avellana_pnv.eml_p: probability for potential distribution</li> </ul> Files are provided as <a href="https://gdal.org/drivers/raster/cog.html">Cloud Optimized GeoTIFFs</a> and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files are provided in both SLD and QML format. If you would like to know more about the creation of the maps and the modeling: <ul> <li>watch the talk at Open Data Science Workshop 2021 (<a href="https://doi.org/10.5446/55256">TIB AV-PORTAL</a>)</li> <li>access the repository with our R/Python scripts and follow the instructions (<a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/tree/master/veg_mapping">GitLab</a>)</li> <li>access the repository with the training dataset (<a href="https://doi.org/10.5281/zenodo.5818021">Zenodo</a>)</li> <li>read the tutorial with executable code on our <a href="https://opengeohub.github.io/spatial-prediction-eml/spatiotemporal-ml.html#spatiotemporal-distribution-of-fagus-sylvatica">GitBook</a></li> </ul> A publication describing, in detail, all processing steps, accuracy assessment and general analysis of species distribution maps is available on <a href="https://doi.org/10.7717/peerj.13728">PeerJ</a>. To suggest any improvement/fix use <a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues">https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues</a>.

Potential and realized distribution at 30m for Aleppo pine (Pinus halepensis) in Europe for 2000 - 2020

Probability and uncertainty maps showing the potential and realized distribution for the Aleppo pine (Pinus halepensis, Mill.) for Europe from the dataset prepared by <a href="http://doi.org/10.5281/zenodo.5818021">Bonannella et al. (2022)</a> and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020, split in the following time periods: <ul> <li>2000 - 2002,</li> <li>2002 - 2006,</li> <li>2006 - 2010,</li> <li>2010 - 2014,</li> <li>2014 - 2018,</li> <li>2018 - 2020.</li> </ul> Files are named according to the following naming convention, e.g: <ul> <li>veg_pinus.halepensis_anv.eml_md_30m_0..0cm_2000..2002_eumap_epsg3035_v0.3</li> </ul> with the following fields: <ul> <li>theme: e.g. veg,</li> <li>species code: e.g. pinus.halepensis,</li> <li>species distribution type: e.g. anv (= actual natural vegetation),</li> <li>species estimation method: e.g. eml,</li> <li>species estimation type: e.g. md ( = model deviation),</li> <li>resolution in meters e.g. 30m,</li> <li>reference depths (vertical dimension): e.g. 0..0cm,</li> <li>reference period begin end: e.g. 2000..2002,</li> <li>reference area: e.g. eumap,</li> <li>coordinate system: e.g. epsg3035,</li> <li>data set version: e.g. v0.3.</li> </ul> For each species is then easy to identify probability and uncertainty distribution maps: <ul> <li>veg_pinus.halepensis_anv.eml_md: model uncertainty for realized distribution</li> <li>veg_pinus.halepensis_anv.eml_p: probability for realized distribution</li> <li>veg_pinus.halepensis_pnv.eml_md: model uncertainty for potential distribution</li> <li>veg_pinus.halepensis_pnv.eml_p: probability for potential distribution</li> </ul> Files are provided as <a href="https://gdal.org/drivers/raster/cog.html">Cloud Optimized GeoTIFFs</a> and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files are provided in both SLD and QML format. If you would like to know more about the creation of the maps and the modeling: <ul> <li>watch the talk at Open Data Science Workshop 2021 (<a href="https://doi.org/10.5446/55256">TIB AV-PORTAL</a>)</li> <li>access the repository with our R/Python scripts and follow the instructions (<a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/tree/master/veg_mapping">GitLab</a>)</li> <li>access the repository with the training dataset (<a href="http://doi.org/10.5281/zenodo.5818021">Zenodo</a>)</li> <li>read the tutorial with executable code on our <a href="http://opengeohub.github.io/spatial-prediction-eml/spatiotemporal-ml.html#spatiotemporal-distribution-of-fagus-sylvatica">GitBook</a></li> </ul> A publication describing, in detail, all processing steps, accuracy assessment and general analysis of species distribution maps is available on <a href="http://doi.org/10.7717/peerj.13728">PeerJ</a>. To suggest any improvement/fix use <a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues">https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues</a>.

Potential and realized distribution at 30m for Stone pine (Pinus pinea) in Europe for 2000 - 2020

Probability and uncertainty maps showing the potential and realized distribution for the stone pine (Pinus pinea, L.) for Europe from the dataset prepared by <a href="http://doi.org/10.5281/zenodo.5818021">Bonannella et al. (2022)</a> and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020, split in the following time periods: <ul> <li>2000 - 2002,</li> <li>2002 - 2006,</li> <li>2006 - 2010,</li> <li>2010 - 2014,</li> <li>2014 - 2018,</li> <li>2018 - 2020.</li> </ul> Files are named according to the following naming convention, e.g: <ul> <li>veg_pinus.pinea_anv.eml_md_30m_0..0cm_2000..2002_eumap_epsg3035_v0.3</li> </ul> with the following fields: <ul> <li>theme: e.g. veg,</li> <li>species code: e.g. pinus.pinea,</li> <li>species distribution type: e.g. anv (= actual natural vegetation),</li> <li>species estimation method: e.g. eml,</li> <li>species estimation type: e.g. md ( = model deviation),</li> <li>resolution in meters e.g. 30m,</li> <li>reference depths (vertical dimension): e.g. 0..0cm,</li> <li>reference period begin end: e.g. 2000..2002,</li> <li>reference area: e.g. eumap,</li> <li>coordinate system: e.g. epsg3035,</li> <li>data set version: e.g. v0.3.</li> </ul> For each species is then easy to identify probability and uncertainty distribution maps: <ul> <li>veg_pinus.pinea_anv.eml_md: model uncertainty for realized distribution</li> <li>veg_pinus.pinea_anv.eml_p: probability for realized distribution</li> <li>veg_pinus.pinea_pnv.eml_md: model uncertainty for potential distribution</li> <li>veg_pinus.pinea_pnv.eml_p: probability for potential distribution</li> </ul> Files are provided as <a href="https://gdal.org/drivers/raster/cog.html">Cloud Optimized GeoTIFFs</a> and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files are provided in both SLD and QML format. If you would like to know more about the creation of the maps and the modeling: <ul> <li>watch the talk at Open Data Science Workshop 2021 (<a href="https://doi.org/10.5446/55256">TIB AV-PORTAL</a>)</li> <li>access the repository with our R/Python scripts and follow the instructions (<a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/tree/master/veg_mapping">GitLab</a>)</li> <li>access the repository with the training dataset (<a href="https://doi.org/10.5281/zenodo.5818021">Zenodo</a>)</li> <li>read the tutorial with executable code on our <a href="https://opengeohub.github.io/spatial-prediction-eml/spatiotemporal-ml.html#spatiotemporal-distribution-of-fagus-sylvatica">GitBook</a></li> </ul> A publication describing, in detail, all processing steps, accuracy assessment and general analysis of species distribution maps is available on <a href="https://doi.org/10.7717/peerj.13728">PeerJ</a>. To suggest any improvement/fix use <a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues">https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues</a>.