Find research datasets worth reusing

Text-fig. 8. Ammonoids Melvilloceras rotaii (LIBROVITCH in POPOV, 1979) from the Mospyne Formation. a, b: A slightly laterally compressed specimen IGSU-7/8040, lateral view (a), ventral view (b). c, d: A slightly laterally compressed specimen IGSU-7/472, lateral view (c), ventral view (d). e, f: Specimen IGSU-7/540, lateral view (e), ventral view (f). g–i: Specimen IGSU-7/468, dorsal view (g), lateral view (h), ventral view (i). j: Specimen IGSU-7/6538, dorsal view. k, l: Specimen IGSU-7/620, lateral view (k), ventral view (l). m–q: Lateral views of the conchs showing the different relief of the umbilical nodes; m – specimen IGSU-4/4148, n – specimen IGSU-7/522, o – specimen IGSU-7/468a, p – specimen IGSU-7/472a, q – specimen IGSU-7/316. Scale bars 10 mm. in Late Bashkirian Ammonoids From The Mospyne Formation Of The Donets Basin, Ukraine

Text-fig. 8. Ammonoids Melvilloceras rotaii (LIBROVITCH in POPOV, 1979) from the Mospyne Formation. a, b: A slightly laterally compressed specimen IGSU-7/8040, lateral view (a), ventral view (b). c, d: A slightly laterally compressed specimen IGSU-7/472, lateral view (c), ventral view (d). e, f: Specimen IGSU-7/540, lateral view (e), ventral view (f). g–i: Specimen IGSU-7/468, dorsal view (g), lateral view (h), ventral view (i). j: Specimen IGSU-7/6538, dorsal view. k, l: Specimen IGSU-7/620, lateral view (k), ventral view (l). m–q: Lateral views of the conchs showing the different relief of the umbilical nodes; m – specimen IGSU-4/4148, n – specimen IGSU-7/522, o – specimen IGSU-7/468a, p – specimen IGSU-7/472a, q – specimen IGSU-7/316. Scale bars 10 mm.

GlobBiomass dataset of forest biomass, South America N (25 m)

The dataset consists of a map of above ground forest biomass (AGB, unit: tons/ha i.e., Mg/ha) of the South American region (between 10°S and 24°N) for the year 2010 (raster dataset) with a pixel size of 25 m x 25 m. AGB is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots. Per-pixel estimates of above-ground biomass uncertainty expressed as standard error in Mg/ha (raster dataset) are also provided.  The AGB estimates were obtained from spaceborne SAR (ALOS PALSAR, Envisat ASAR), optical (Landsat-7), LiDAR (ICESAT), auxiliary datasets with multiple estimation procedures (Santoro et al., ESSD, 2021).  In this repository, the AGB data are available in form of tiles of 2° x 2° (bounding box: longitude -180°E/-26°E latitude: -10°N/+24°N). This dataset is the basis for the official GlobBiomass dataset consisting of global estimates of forest biomass with 1 ha pixels (<a href="https://doi.pangaea.de/10.1594/PANGAEA.894711">https://doi.pangaea.de/10.1594/PANGAEA.894711</a>). The dataset in this repository represents the original GlobBiomass dataset of AGB from which the official dataset was obtained after averaging from 25 m to 100 m. Given the lower accuracy of the 25 m pixel-based estimates, it is recommended to use the official GlobBiomass dataset unless detailed spatial resolution is a fundamental asset.  Technical specifications are provided in the file README_GLOBBIOMASS_South_America_N_20210428.pdf

GlobBiomass dataset of forest biomass, South Asia (25 m)

The dataset consists of a map of above ground forest biomass (AGB, unit: tons/ha i.e., Mg/ha) of the South Asian region for the year 2010 (raster dataset) with a pixel size of 25 m x 25 m. AGB is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots. Per-pixel estimates of above-ground biomass uncertainty expressed as standard error in Mg/ha (raster dataset) are also provided.  The AGB estimates were obtained from spaceborne SAR (ALOS PALSAR, Envisat ASAR), optical (Landsat-7), LiDAR (ICESAT), auxiliary datasets with multiple estimation procedures (Santoro et al., ESSD, 2021).  In this repository, the AGB data are available in form of tiles of 2° x 2° (bounding box: longitude 56°E/180°E latitude: -60°N/+30°N). This dataset is the basis for the official GlobBiomass dataset consisting of global estimates of forest biomass with 1 ha pixels (<a href="https://doi.pangaea.de/10.1594/PANGAEA.894711">https://doi.pangaea.de/10.1594/PANGAEA.894711</a>). The dataset in this repository represents the original GlobBiomass dataset of AGB from which the official dataset was obtained after averaging from 25 m to 100 m. Given the lower accuracy of the 25 m pixel-based estimates, it is recommended to use the official GlobBiomass dataset unless detailed spatial resolution is a fundamental asset.  Technical specifications are provided in the file README_GLOBBIOMASS_South_Asia_20210428.pdf

GlobBiomass dataset of forest biomass, North America N (25 m)

The dataset consists of a map of above ground forest biomass (AGB, unit: tons/ha i.e., Mg/ha) of the North American region between 50°N and 90°N for the year 2010 (raster dataset) with a pixel size of 25 m x 25 m. AGB is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots. Per-pixel estimates of above-ground biomass uncertainty expressed as standard error in Mg/ha (raster dataset) are also provided.  The AGB estimates were obtained from spaceborne SAR (ALOS PALSAR, Envisat ASAR), optical (Landsat-7), LiDAR (ICESAT), auxiliary datasets with multiple estimation procedures (Santoro et al., ESSD, 2021).  In this repository, the AGB data are available in form of tiles of 2° x 2° (bounding box: longitude -180°E/-26°E latitude: 50°N/+90°N). This dataset is the basis for the official GlobBiomass dataset consisting of global estimates of forest biomass with 1 ha pixels (<a href="https://doi.pangaea.de/10.1594/PANGAEA.894711">https://doi.pangaea.de/10.1594/PANGAEA.894711</a>). The dataset in this repository represents the original GlobBiomass dataset of AGB from which the official dataset was obtained after averaging from 25 m to 100 m. Given the lower accuracy of the 25 m pixel-based estimates, it is recommended to use the official GlobBiomass dataset unless detailed spatial resolution is a fundamental asset.  Technical specifications are provided in the file README_GLOBBIOMASS_North_America_N_20210428.pdf

GlobBiomass dataset of forest biomass, North America S (25 m)

The dataset consists of a map of above ground forest biomass (AGB, unit: tons/ha i.e., Mg/ha) of the North American region between 24°N and 50°N for the year 2010 (raster dataset) with a pixel size of 25 m x 25 m. AGB is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots. Per-pixel estimates of above-ground biomass uncertainty expressed as standard error in Mg/ha (raster dataset) are also provided.  The AGB estimates were obtained from spaceborne SAR (ALOS PALSAR, Envisat ASAR), optical (Landsat-7), LiDAR (ICESAT), auxiliary datasets with multiple estimation procedures (Santoro et al., ESSD, 2021).  In this repository, the AGB data are available in form of tiles of 2° x 2° (bounding box: longitude -180°E/-26°E latitude: 24°N/+54°N). This dataset is the basis for the official GlobBiomass dataset consisting of global estimates of forest biomass with 1 ha pixels (<a href="https://doi.pangaea.de/10.1594/PANGAEA.894711">https://doi.pangaea.de/10.1594/PANGAEA.894711</a>). The dataset in this repository represents the original GlobBiomass dataset of AGB from which the official dataset was obtained after averaging from 25 m to 100 m. Given the lower accuracy of the 25 m pixel-based estimates, it is recommended to use the official GlobBiomass dataset unless detailed spatial resolution is a fundamental asset.  Technical specifications are provided in the file README_GLOBBIOMASS_North_America_S_20210428.pdf

GlobBiomass dataset of forest biomass, North Asia N (25 m)

The dataset consists of a map of above ground forest biomass (AGB, unit: tons/ha i.e., Mg/ha) of the North Asian region between 56°N and 90°N for the year 2010 (raster dataset) with a pixel size of 25 m x 25 m. AGB is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots. Per-pixel estimates of above-ground biomass uncertainty expressed as standard error in Mg/ha (raster dataset) are also provided.  The AGB estimates were obtained from spaceborne SAR (ALOS PALSAR, Envisat ASAR), optical (Landsat-7), LiDAR (ICESAT), auxiliary datasets with multiple estimation procedures (Santoro et al., ESSD, 2021).  In this repository, the AGB data are available in form of tiles of 2° x 2° (bounding box: longitude 56°E/180°E latitude: 56°N/+90°N). This dataset is the basis for the official GlobBiomass dataset consisting of global estimates of forest biomass with 1 ha pixels (<a href="https://doi.pangaea.de/10.1594/PANGAEA.894711">https://doi.pangaea.de/10.1594/PANGAEA.894711</a>). The dataset in this repository represents the original GlobBiomass dataset of AGB from which the official dataset was obtained after averaging from 25 m to 100 m. Given the lower accuracy of the 25 m pixel-based estimates, it is recommended to use the official GlobBiomass dataset unless detailed spatial resolution is a fundamental asset.  Technical specifications are provided in the file README_GLOBBIOMASS_North_Asia_N_20210428.pdf

GlobBiomass dataset of forest biomass, North Asia S (25 m)

The dataset consists of a map of above ground forest biomass (AGB, unit: tons/ha i.e., Mg/ha) of the North Asian region between 30°N and 56°N for the year 2010 (raster dataset) with a pixel size of 25 m x 25 m. AGB is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots. Per-pixel estimates of above-ground biomass uncertainty expressed as standard error in Mg/ha (raster dataset) are also provided.  The AGB estimates were obtained from spaceborne SAR (ALOS PALSAR, Envisat ASAR), optical (Landsat-7), LiDAR (ICESAT), auxiliary datasets with multiple estimation procedures (Santoro et al., ESSD, 2021).  In this repository, the AGB data are available in form of tiles of 2° x 2° (bounding box: longitude +56°E/+180°E latitude: +30°N/+56°N). This dataset is the basis for the official GlobBiomass dataset consisting of global estimates of forest biomass with 1 ha pixels (<a href="https://doi.pangaea.de/10.1594/PANGAEA.894711">https://doi.pangaea.de/10.1594/PANGAEA.894711</a>). The dataset in this repository represents the original GlobBiomass dataset of AGB from which the official dataset was obtained after averaging from 25 m to 100 m. Given the lower accuracy of the 25 m pixel-based estimates, it is recommended to use the official GlobBiomass dataset unless detailed spatial resolution is a fundamental asset.  Technical specifications are provided in the file README_GLOBBIOMASS_North_Asia_S_20210428.pdf

NB-IoT vs. LTE-M: Measurement Data of the Energy Consumption of LPWAN Technologies

NB-IoT vs. LTE-M: Measurement Data of the Energy Consumption of LPWAN Technologies This dataset contains the raw energy measurements as well as R scripts to reproduce the energy consumption plot for the corresponding paper. Each .csv file contains a specific set of measurements and we provide a script to read, process and plot the contained data. Figure 3 Mean energy consumption of the different phases for Authentication for NB-IoT and LTE-M. Due to the fact that the duration of Idle Connected in the measurement scripts was 30 seconds and 60 seconds for Idle Not Connected, the D-value and the mean power consumption are divided by 2. <ul> <li>Data – energy_measurements_fig3.csv</li> <li>Code – fig3.R</li> </ul> Figure 4 Mean energy consumption of the different phases for Data Connection and Download for NB-IoT and LTE-M for 1KB of data in HTTP. The delay between the measurements for Figure 4 were all 30 seconds long, but the identified Standby and Idle phases have different lengths. Therefore, the Idle phase values for both access technologies have been normalized and calculated for 20 seconds each. <ul> <li>Data – energy_measurements_fig4.csv</li> <li>Code – fig4.R</li> </ul> Figure 5 Mean energy consumption of the different phases for Data Connection and Download for HTTP and MQTT for 1KB of data in NB-IoT. In this scenario the delay between the measurements were different again. For MQTT the delay was 150 seconds and for HTTP 30 seconds. Therefore, the data during the Idle and Standby (only for MQTT) phase is normalized and calculated for 20 seconds and 10 seconds, respectively. During the MQTT Idle phase measurements, the device disconnects. This is not taken into account for the evaluation, which is why these energy values are discarded for this figure. <ul> <li>Data – energy_measurements_fig5.csv</li> <li>Code – fig5.R</li> </ul> Contact For questions or issues with this code, please contact Viktoria Vomhoff (viktoria.vomhoff@uni-wuerzburg.de) or any of the authors of the related publication.

Equilibrated Kremer-Grest polymer melts of M=500 linear chains with Z=100 entanglements for varying chain stiffness.

Kremer-Grest model polymer melts of highly entangled linear chains. Each melt has approximately 500 chains of Z=100 entanglements each. Systems have been generated for integer and half-integer stiffness kappa=-2.0 to 6.0. System sizes range from 8M to 2M beads. For details regarding the equilibration process and the Kremer-Grest polymer model see C. Svaneborg & R. Everaers ""Multiscale equilibration of highly entangled isotropic model polymer melts" J. Chem. Phys. 158, 054903 (2023)  <a href="https://doi.org/10.1063/5.0123431">https://doi.org/10.1063/5.0123431</a> Filenames denote the kappa<value> used when equilibrating the melt as well as the number of entanglements Z<number> and the number of molecules M<number>. The files are in ASCII format in the format of a LAMMPS data files. (https://lammps.sandia.gov) The semantics is self-explanatory, sections contains id, molecule, unwrapped coordinates of all beads, as well as bond and angular interactions between all beads. We acknowledge that part of the results of this research was obtained using the PRACE Research Infrastructure resource Joliot-Curie SKL based in France at GENCI@CEA. Computing facilities were provided by the eScience Center at the University of Southern Denmark and financed by the Faculty of Science. Please cite as: @article{MultiscaleEquilibrationHighlyEntangledIsotropicModelPolymerMelts,     author = {Svaneborg,Carsten  and Everaers,Ralf },     title = {Multiscale equilibration of highly entangled isotropic model polymer melts},     journal = {J. Chem. Phys.},     volume = {158},     number = {5},     pages = {054903},     year = {2023},     doi = {10.1063/5.0123431},     URL = {https://doi.org/10.1063/5.0123431} }   <pre>@misc{EquilibratedKGMeltsZ100, author = {Svaneborg,Carsten  and Everaers,Ralf}, title = {Equilibrated Kremer-Grest polymer melts of M=500 linear chains with Z=100 entanglements for varying chain stiffness.}, month = feb, year = 2023, publisher = {Zenodo}, version = {1.0}, doi = {10.5281/zenodo.7319837}, url = {https://doi.org/10.5281/zenodo.7319837} }</pre>

Figs 19–21 in Megacraspedus (Lepidoptera: Gelechiidae) of the Altai Mountains with description of a new species belonging to the M. majorella group

Figs 19–21. Habitats of Megacraspedus species. 19–20 – M. bidzilyai sp. nov., Russia, Altai Mts. 21 – M. latiuncus Huemer & Karsholt, 2018, Kyrgyzstan, North Tian-Shan Mts. (19 – photographed by Z. Růžičková, 21 – photographed by I. Dvořák).

Figs 15–17 in Megacraspedus (Lepidoptera: Gelechiidae) of the Altai Mountains with description of a new species belonging to the M. majorella group

Figs 15–17. Megacraspedus latiuncus Huemer & Karsholt, 2018, Kyrgyzstan, Issyk-Kul (barcoded). 15 – adult. 16 – head (enlarged). 17 – male genitalia: a – phallus; b – detail of spines; c – detail of apical part of phallus.

Figs 5–7 in Megacraspedus (Lepidoptera: Gelechiidae) of the Altai Mountains with description of a new species belonging to the M. majorella group

Figs 5–7. Genitalia of Megacraspedus bidzilyai sp. nov., Russia, Altai Mts., details in text. 5–6 – male genitalia, paratypes: a – phallus; b – detail of apical part of phallus. 7 – female genitalia: c – detail of ostium; d – signum; e – oposite view of signum.

Fig. 18 in Megacraspedus (Lepidoptera: Gelechiidae) of the Altai Mountains with description of a new species belonging to the M. majorella group

Fig. 18. Neighbor-joining tree of Megacraspedus species recorded in Altai Mts., M. latiuncus Huemer & Karsholt, 2018, and Caryocolum tetrameris (Meyrick, 1926) as an outgroup. Source: Barcode of Life Database, cf. RATNASINGHAM & HEBERT (2007).

Figs 1–4 in Megacraspedus (Lepidoptera: Gelechiidae) of the Altai Mountains with description of a new species belonging to the M. majorella group

Figs 1–4. Adults of Megacraspedus bidzilyai sp. nov., Russia, Altai Mts., details in text. 1–3 – males: 1 – holotype (barcoded); 2 – paratype; 3 – head (enlarged); 4 – female, paratype.

Dataset for publication Sikora P., El-Khayatt A.M., Saudi H.A., Liard M., Lootens D., Chung S.-Y., Woliński P., Abd Elrahman M. Rheological, mechanical, microstructural and radiation shielding properties of cement pastes containing magnetite (Fe3O4) nanoparticles. International Journal of Concrete Structures and Materials (2023), 17, 7

Open dataset for publication  Sikora P., El-Khayatt A.M., Saudi H.A., Liard M., Lootens D., Chung S.-Y., Woliński P., Abd Elrahman M. Rheological, mechanical, microstructural and radiation shielding properties of cement pastes containing magnetite (Fe3O4) nanoparticles. International Journal of Concrete Structures and Materials (2023), 17, 7. https://doi.org/10.1186/s40069-022-00568-y File 1 - X-ray diffractogram and particle size distribution (laser granulometry) data - *.opju (Origin) File 2 - Rheological test results - *.opju (Origin) File 5 - Mechanical peformance (early strength - ultrasounds and compressive strength) and density test results - *.opju (Origin) File 4 - Mercury intrusion porosimetry test data - *.opju (Origin)

Starspot mapping with adaptive parallel tempering. II. Application to TESS data for M-dwarf flare stars, AU Microscopii, YZ Canis Minoris, and EV Lacertae (Flare Tables)

For TESS PDC-SAP data of AU Mic (Sector 1 and 27), YZ CMi (Sector 7 and 34), and EV Lac (Sector 16), detected flare properties are listed: the peak time (BJD-2745000), amplitude, equivalent duration (sec), and e-folding time (day).

The global 30-m forest canopy height map for 2020

The global forest canopy height map with a resolution of 30 m for 2020 (GlobeFCH_2020_30m_v1) was generated by integrating the new-generation space-borne LiDAR (Global Ecosystem Dynamics Investigation, GEDI; Ice, Cloud, and Land Elevation Satellite-2, ICESat-2), Sentinel-1 SAR images, Sentinel-2 optical images and other ancillary data based on Google Earth Engine (GEE) platform. The coordinate system of the GlobeFCH_2020_30m_v1 is World Geodetic System 1984 (WGS 84) and the unit of the forest canopy height value is centimeter. The GlobeFCH_2020_30m_v1 was divided into 305 files, and the range of each file is 10°×10°.

8-day, 500-m Gross Primary Production for Europe from 2001 to 2016(YEAR_doy: 2004337 to 2010073)

FGM GPP from YEAR_DOY of 2004337 to 20010073

opencc-by-4.0Apr 2007View details →

Fig. 3. M in A new hangingfly (Insecta: Mecoptera: Bittacidae) from the Middle Jurassic of Inner Mongolia, China

Fig. 3. M. daohugouensis holotype: (A) anal field of right fore wing; (B) anal field of left fore wing; (C) fore or middle last two tarsomeres; (D) hind last two tarsomeres. Scale bars = 0.2 mm.

Global maps of agricultural expansion potential at a 300 m resolution

Global maps of agricultural expansion potential at a 300 m resolution This repository contains data from “Global maps of agricultural expansion potential at a 300 m resolution” study. Abstract: The global expansion of agricultural land is a leading driver of climate change and biodiversity loss. However, the spatial resolution of current global land change models is relatively coarse, which limits environmental impact assessments. To address this issue, we developed global maps representing the potential for conversion into agricultural land at a resolution of 10 arc-seconds (approximately 300 m at the equator). We created the maps using Artificial Neural Network (ANN) models relating locations of recent past conversions (2007-2020) into one of three cropland categories (cropland only, mosaics with >50% crops, and mosaics with <50% crops) to various predictor variables reflecting topography, climate, soil and accessibility. Cross-validation of the models indicated good performance with Area Under the Curve (AUC) values of 0.88-0.93. Hindcasting of the models from 1992 to 2006 revealed a similar high performance (AUC of 0.83-0.91), indicating that our maps provide representative estimates of current agricultural conversion potential provided that the drivers underlying agricultural expansion patterns remain the same. Our maps can be used to downscale projections of global land change models to more fine-grained patterns of future agricultural expansion, which is an asset for global environmental assessments. Data description: We provide here raster maps of agricultural expansion potential for three categories of agriculture - (i) cropland only, (ii) mosaics with >50% crops, and (iii) mosaics with <50% crops. The source for delineating categories was the ESA CCI land cover data. ESA CCI land cover data recognizes additional categories of agricultural land, however some of them have limited spatial coverage. For that reason, we merged the rainfed cropland and irrigated cropland categories into a single category - cropland only, where a grid cell is largely dominated by crops. Rainfed croplands account for 87% of the this category, while irrigated croplands account for the remaining 13%. Mosaic categories were defined in the same way as in the ESA CCI land cover dataset. Numerical designations of these categories in the ESA CCI land cover dataset are 10, 20, 30, and 40 for rainfed, irrigated, mosaics with >50% crops, and mosaics with <50% crops, respectively. Global maps are provided at the spatial resolution of 10 arc-seconds (~300 meters at the equator). These files are available for three categories in the main folder with the filename prefix "Agri_potential_mosaic_*". The numerical value in the file name refers to the agricultural category type (10 - cropland only, 30 - mosaics with >50% crops, and 40 - mosaics with <50% crops). In addition to the 10 arc-second layers, we provide aggregated layers with the spatial resolution of 30 arc-seconds, 5 and 10 arc-minutes, for coarse-grained applications and less computationally-intensive analyses. We provide the aggregated layer maps for the minimum, median, mean/average, and maximum values of the aggregated 10 arc-seconds values within the coarser cells. There are in total 9 files provided for each of the aggregated spatial resolutions. Repository content: Full resolution layers: - “Agri_potential_mosaic_10.tif” is the global raster map for cropland only category at the spatial resolution of 10 arc-seconds. - “Agri_potential_mosaic_30.tif” is the global raster map for mosaics with >50% crops category at the spatial resolution of 10 arc-seconds. - “Agri_potential_mosaic_40.tif” is the global raster map for mosaics  with <50% crops category at the spatial resolution of 10 arc-seconds. - "readme.txt" is the text file with the basic description and the metadata for the repository. Aggregated layers: This folder contains files with a different spatial resolution (30s, 5m, 10m; see argument "RESL" below). File names for the aggregated maps contain the following information: “Agri_potential_aggregated_RESL_TYPE_CATG.tif” - "RESL" is the spatial resolution of the layer. Value is either "30s", "5m", or "10m", corresponding to spatial resolution of 30 arc-second, 5 arc-minutes, and 10 arc-minutes. - "TYPE" is the type of aggregated values. Value is either "min", "avg", "med", or "max", corresponding to the minimum, mean, median, and maximum values of the aggregated 10 arc-seconds values within the coarser cells. - "CATG" is the category of agricultural land. Value is either "10", "30", or "40", where category 10 is cropland only, category 30 is mosaics with >50% crops, and category 40 is mosaics with <50% crops. Raster metadata: Driver: GTiff Projection proj4string: +proj=longlat +ellps=WGS84 +no_defs Notes on use: Our conversion potential maps are useful for researchers and practitioners interested in downscaling projections of global land change models to a more fine-grained patterns of future agricultural expansion, or interested in assessing the locations and effects of future agricultural expansion, for example in integrated assessment modelling or biodiversity impact modelling. When coupling outputs with integrated assessment modelling, our maps need to be combined with estimates of the expected future demands for agricultural land per socio-economic region. In such a coupled approach, our global conversion potential maps can be used to spatially allocate the additional agricultural land demands. In this context, it is important to note that the modelled relationships between the agricultural conversions and our set of predictors may result in non-zero probabilities also in areas that are highly unlikely to be converted into agriculture, such as urban areas or strictly protected nature reserves. This implies that users of our maps may need to implement an additional map layer that masks areas unavailable for agricultural expansion. We also stress that our maps represent agricultural conversion potential conditional on the predictor variables that we included, implying that our maps do not capture the possible influences of other potentially relevant predictors. For example, our conversion potential models and maps do not account for permafrost, which may pose significant challenges to possible agricultural expansion to higher latitudes in response to climate change.