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opencc-by-4.0Dec 2021View details →

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-4.0Dec 2017View details →

Data for: Presence-absence of marine macrozoobenthos does not generally predict abundance and biomass

This repository contains data for the paper: Bijleveld, A. I. et al. (2018) Presence-absence of marine macrozoobenthos does not generally predict abundance and biomass. Scientific Reports 8, 3039, doi:10.1038/s41598-018-21285-1.

opencc-by-4.0Sep 2024View details →

Electron-spin decoherence in trityl radicals in the absence and presence of microwave irradiation

Experimental data sets on bare-spin and dressed-spin decoherence of some trityl radicals, DFT-predicted hyperfine couplings and atom coordinates, simulation scripts, and simulated data.

opencc-by-4.0Jan 2023View details →

A striatal circuit balances learned fear in the presence and absence of sensory cues

This repository stores the raw data that gave rise to the study by Kintscher et al. 2023, published by eLife (eLife2023;12:e75703; DOI:https://doi.org/10.7554/eLife.75703), as well as a preprint at bioRxiv (doi: 10.1101/2021.12.09.471922). Please refer to the original publication regarding experimental design and methodological details of data acquisition and analysis. Below we supply information on the provided metadata files which, in turn, refer to individual raw data files. General structure of the repository: <ul> <li>the raw data is organized in 22 subsets defined by the figures or supplementary figures they contribute to. Each subset is documented by its own metadata file;</li> <li>the metadata files listing names of the individual data files were made in “.csv” format, one per data subset. Field separator: comma;</li> <li>all metadata are summarized in the main metadata file “metadata_master.csv”. Field separator: comma;</li> <li>the dataset expands into the second repository accessible at the following doi: 10.5281/zenodo.7522958</li> </ul>   Description of the non-textual data formats: <ul> <li>due to its excessive volume, the raw data for in-vivo microendoscopic Ca2+ recordings is not present in the current repository but will be made available upon request from the corresponding author;</li> <li>certain blocks of data, such as the output of CaImAn package, or slide scanner imaging projects, contained multiple smaller files and thus were compressed into encryption- and password-free .zip archives;</li> <li>video recordings of animal behavior during the fear conditioning protocol were provided as unmodified “.wmv” files created by the VideoFreeze acquisition software (Med Associates Inc). Video stream parameters: wmv3 codec, color space yuv420p, 320x240 pixels, 30 fps, bitrate 300 kb/s. Alternatively (Figure 4 – figure supplement 3), videos were stored by EthoVisionXT software as “.mpg” files with the following parameters: AVI container, mpeg4 codec, color space yuv420p, 1280x1024 pixels, 30 fps, bitrate 1206 kb/s;</li> <li>widefield fluorescent images of single coronal sections were either uploaded as original unmodified data in a proprietary .vsi format of Olympus slide scanning microscope (for Figure 1 – figure supplement 1, Figure 3 – figure supplement 1, Figure 7, Figure 7 – figure supplement 1), or were first binned and saved as a composite TIFF format at a resolution ~10.3 micrometers/pixel (for all the post-hoc images showing the virus expression and the fiber/lens placement). Both “.vsi” and TIFF formats are readable by Bioformats plugin working under open-source free software packages FIJI/ImageJ (<a href="https://fiji.sc/">https://fiji.sc/</a> , <a href="https://imagej.net/Fiji/Downloads">https://imagej.net/Fiji/Downloads</a>), or QuPath (<a href="https://qupath.github.io/">https://qupath.github.io/</a>). Imaging metadata such as pixel resolution is embedded inside the individual .vsi or TIFF files. Correspondence between the fluorescent signals and image color channels is provided in the .csv metadata files;</li> <li>confocal fluorescent image stack underlying images in Figure 7B is provided in original proprietary “.lsm” format (Carl Zeiss). The imaging metadata is embedded in .lsm format, which can be read by the Bioformats plugin under FIJI/ImageJ or QuPath;</li> <li>data for patch clamp recordings (Figure 5 – figure supplement 2, Figure 8, Figure 8 – figure supplement 1, Figure 9) are provided as original .dat files from the acquisition software PatchMaster (HEKA Elektronik, Germany). These files can be imported using an Igor Pro extension “bpc_ReadHeka.xop” (for 32-bit Igor Pro versions 5.xx - 6.37) by Holger Taschenberger (<a href="https://www.wavemetrics.com/project/bpc_ReadHeka">https://www.wavemetrics.com/project/bpc_ReadHeka</a>), or using a Python script by Luke Campagnola (<a href="https://github.com/campagnola/heka_reader">https://github.com/campagnola/heka_reader</a>), or using a Matlab script “HEKA PatchMaster Importer” by Christian Keine (<a href="https://github.com/ChristianKeine/HEKA_Patchmaster_Importer">https://github.com/ChristianKeine/HEKA_Patchmaster_Importer</a>).</li> </ul>

opencc-by-nc-sa-4.0Mar 2019View details →

Modular organization of the murine locomotor pattern in the presence and absence of sensory feedback from muscle spindles

In this study, we made use of non-negative matrix factorization (NMF) to extract muscle synergies from electromyographic (EMG) data. We implemented the NMF algorithm in R version 3.5.1 (R Foundation for Statistical Computing, R Core Team, Vienna, Austria), a programming language available in a free software environment. However, even if the software does not require a paid license, often researchers are either not confident with or prefer not to spend time writing the code required to perform NMF. We make available, as we recently did with human data (Santuz et al., 2018), an example open access data set of EMG and muscle synergy data for murine walking and swimming. The data presented in this supplementary information part is available in three formats: 1) the raw EMG of two example trials (one recorded during walking and the other during swimming in a wild type animal, six muscles), unprocessed together with the touchdown and lift-off timings of the recorded limb for walking and the cycle timings for swimming; 2) the filtered and time-normalized EMG and 3) the muscle synergies extracted via NMF. Moreover, we provide the R code for obtaining the results described in the previous three points. We do not report any metadata, since trials are relative to a single representative animal. The R code is profusely commented.

Fig. 3 - a-c in Ant diversity on the largest Mediterranean islands: on the presence or absence of 28 species in Sicily (Hymenoptera, Formicidae)

Fig. 3 - a-c) Plagiolepis schmitzii. Workers / operaie. Mondello (ESC). d-e) Ponera testacea. Worker / operaia. Pizzo di Pietralunga (ESC). f-j) Solenopsis orbula. Queens / regine. Messina. A specimen from De Stefani's collection / esemplare dalla collezione De Stefani (f,h,j) and a recently collected one / ed uno recentemente raccolto (g,i) (ESC). Scale bar / scala grafica: 0.5 mm.

Fig. 4 - a-b in Ant diversity on the largest Mediterranean islands: on the presence or absence of 28 species in Sicily (Hymenoptera, Formicidae)

Fig. 4 - a-b) Temnothorax clypeatus. Worker / operaia. Bosco di Santo Pietro (AAC). c-d) Temnothorax nylanderi. Worker / operaia. Lago Maulazzo (ESC). e-f) Temnothorax ravouxi. Queen / regina. Madonna delle Grazie (AAC). Scale bar / scala grafica: 0.5 mm.

Fig. 2 - a-d in Ant diversity on the largest Mediterranean islands: on the presence or absence of 28 species in Sicily (Hymenoptera, Formicidae)

Fig. 2 - a-d) Camponotus ruber. a-b) major worker / operaia maggiore; c-d) minor worker / operaia minore. Monte Pellegrino. Scale bar / scala grafica: 1 mm (ESC). e-f) Lasius myops. Worker / operaia. Pineta di Linguaglossa. Scale bar / scala grafica: 0.5 mm (AAC). g-h) Lasius platythorax. Worker / operaia. Monte Cofano. Scale bar / scala grafica: 0.5 mm (AAC).

Fig. 1 in Ant diversity on the largest Mediterranean islands: on the presence or absence of 28 species in Sicily (Hymenoptera, Formicidae)

Fig. 1 - Sampling effort across Sicily (circum-Sicilian islands excluded) according to the number of samples from AAC and ESC. / Sforzo di campionamento in Sicilia (isole circumsiciliane escluse) basato sul numero di campioni in AAC e ESC.

Fig. 5 - Solenopsis latro sicula. a-b,e in Ant diversity on the largest Mediterranean islands: on the presence or absence of 28 species in Sicily (Hymenoptera, Formicidae)

Fig. 5 - Solenopsis latro sicula. a-b,e) Queen / regina; c-d,f) male / maschio. Monte Pellegrino. Scale bar / scala grafica: 0.5 mm (ESC).

Fig. 3 in Attraction of Rhagoletis indifferens (Diptera: Tephritidae) to white light in the presence and absence of ammonia

Fig. 3. Mean numbers ± SE of Rhagoletis indifferens flies caught on clear styrene trap with 60 W halogen bulb and no ammonia lure at different light intensities (lx) shown along the x-axis. Light intensities inside test cage were 90 to 250 lx. Maximum number of flies was 60. Means with same letters are not significantly different (P> 0.05, Tukey's HSD test).

Fig. 4 in Attraction of Rhagoletis indifferens (Diptera: Tephritidae) to white light in the presence and absence of ammonia

Fig. 4. Mean numbers ± SE of Rhagoletis indifferens flies caught on sticky yellow traps with ammonia lure in control (no heat) and heat treatments, using a painted 75 W halogen bulb: (A) 76.3 °C black bulb; (B) 105.8 °C black bulb; (C) 65.0 °C white bulb; (D) 90.5 °C white bulb. Light was provided to each trap by using a 45 W LED. Top temperature inside bar is that on the bulb surface; lower temperature is that at 2.5 cm from the bulb. Light intensities inside the test cage were 90 to 250 lx. Maximum number of flies was 60.

Fig. 2 in Attraction of Rhagoletis indifferens (Diptera: Tephritidae) to white light in the presence and absence of ammonia

Fig. 2. Mean numbers ± SE of Rhagoletis indifferens flies caught on sticky yellow traps with no ammonia lure at different light intensities (lx) shown along the x-axis: (A) experiment 3, 60 W halogen bulb and (B) experiment 4, 60 W LED bulb. Light intensities inside test cage were 90 to 250 lx. Maximum number of flies was 60. Means with same letters are not significantly different (P> 0.05, Tukey's HSD test).

Fig. 1 in Attraction of Rhagoletis indifferens (Diptera: Tephritidae) to white light in the presence and absence of ammonia

Fig. 1. Mean numbers ± SE of Rhagoletis indifferens flies caught on sticky yellow traps with ammonia lure at different light intensities (lx) shown along the x-axis: experiment 1: (A) test 1A, 60 W halogen bulb; (B) test 1B, 75 W halogen bulb; experiment 2: (C) test 2A, 60 W LED bulb; (D) test 2B, 60 W LED bulb. Light intensities inside test cage were 90 to 250 lx. Maximum number of flies was 60. Means with same letters are not significantly different (P> 0.05, Tukey's HSD test).

Fig. 5 in Attraction of Rhagoletis indifferens (Diptera: Tephritidae) to white light in the presence and absence of ammonia

Fig. 5. Mean numbers ± SE of Rhagoletis indifferens flies caught on sticky yellow traps with no ammonia lure: (A) experiment 7 using a black bulb: control (light off) and heated (light on); (B) experiment 8 using an iron: control (iron off) and heated (iron on). No bulb light was provided in either experiment. Top temperature above bar is that on the surface; lower temperature is that at 2.5 cm from the heat source. Light intensities inside the test cage were 90 to 250 lx. Maximum number of flies was 60.

opencc-by-4.0Jul 2020View details →

Fig 2 in Benthic macroinvertebrate communities in sites with native forest presence and absence in north Patagonia

Fig 2. Results of UPGMA for autumn (left) and winter (right) data of macroinvertebrates studied at Araucaria Region, Chile.