Find research datasets worth reusing

opencc-by-4.0Dec 2020View details →

Fig. 2 in Hoverflies (Diptera: Syrphidae) of El Ventorrillo Biological Station, Madrid province, Spain: a perspective from a late twentieth century inventory

Fig. 2. El Ventorrillo in summer (Sierra de Guadarrama, Spain). Malaise trap placed in grassland/mix-forest edge.

opencc-by-4.0Dec 2020View details →

Fig. 6 in Hoverflies (Diptera: Syrphidae) of El Ventorrillo Biological Station, Madrid province, Spain: a perspective from a late twentieth century inventory

Fig. 6. Hoverfly species from Sierra de Guadarrama. (A) Milesia crabroniformis, female, Puerto de Navacerrada. (B) Sericomyia hispanica, female, Puerto de Navacerrada. (C) Chrysotoxum sp. near vernale, male, Puerto de la Morcuera. (D) Dasysyrphus albostriatus, female. Photos A, B, D by Piluca Álvarez-Fidalgo, C by Marián Álvarez-Fidalgo.

opencc-by-4.0Feb 2022View details →

Global inventory of potentially cultivable land and potentially available cropland under different scenarios and policies

Global inventory of potentially cultivable land and potentially available cropland under different scenarios and policies To identify and investigate potential land-use conflicts and emerging trade-offs between different Sustainable Development Goals, such as food security, climate protection and biodiversity conservation, it is important to identify where land-use change and particularly the expansion of cropland could potentially take place in the future. Therefore, we provide a consistent global dataset of land potentially cultivable and potentially available for agricultural use for past and future time periods from 1980 until 2100. Based on the agricultural suitability of land for 23 globally important food, feed, fiber and first- and second-generation bioenergy crops, and high resolution land cover data, the potentially cultivable land is defined by its agricultural suitability and the (technical) feasibility of agriculture. The potentially available cropland additionally considers potential nature protection policies restricting agriculture in forests, wetlands and strictly protected areas, thereby reflecting key aims of the Sustainable Development goals and recent efforts to stop deforestation, protect the climate and preserve biodiversity. The spatially explicit global datasets of potentially cultivable land (pcl) and potentially available cropland (pac) are available for four different time periods (1980-2009, 2010-20,39, 2040-2069, 2070-2099) under RCP2.6 and RCP8.5. The impact of irrigation on the agricultural suitability is considered by referring to current irrigations patters. However, to enable different assumptions on the irrigation of land potentially cultivable or available for cropland use, all datasets are also available for rainfed and irrigated conditions separately. Moreover, we provide a subset-version of all dataset which excludes land that is solely suitable for second-generation bioenergy crops. All datasets are available at 30 arc-seconds and 30 arc-minutes spatial resolution and aggregated at country level to enable the application in models that use aggregated data. By serving as an input for land-use models, the data could improve the comparability of the models and their output, and increase the consistency within interdisciplinary research and integrated model coupling approaches that investigate land-use change.   Further information: A detailed description on the methods and underlying data is available in: Schneider. J.M., Zabel, F., Mauser, W. (2022): Global inventory of suitable, cultivable and available cropland under different scenarios and policies. Scientific Data. <a href="https://doi.org/10.1038/s41597-022-01632-8">https://doi.org/10.1038/s41597-022-01632-8</a> Contact: Please contact: Julia M. Schneider (Schneider.ju@lmu.de) Department of Geography, Ludwig-Maximilians-Universität München (LMU), Munich, Germany.

opencc-by-4.0Jan 2022View details →

samples inventory - single-stringer coupons for EoL research - ReINTEGRA

Open access to experimental data generated by the ReINTEGRA project (GA 886609, H2020, European Union, through Clean Sky 2 JU) along the research, at single-stringer coupon level, of the End-of-Life of novel welded Al-Li aerostructures. Research pertaining to Task 1.1 (WP1), Deliverable D1. Processed data originated from calliper and precision balance measurements conducted by AZTERLAN, to determine weight and dimensions of coupons supplied by ecoTECH partners. The file contains the physical description of samples from 15 references of coupons, being each reference a unique combination of stringer and skin alloys, welding technique, stringer configuration, coating and FSW sealant. The coupon samples have been used for investigating cutting strategies and decoating methods (WP2), remelting set-up (WP3) and pre-scrap characterisation protocols (WP4). The experimental plan, with estimation of number of samples required of each reference, was outlined as part of Task 1.1 (WP1).

opencc-by-4.0Feb 2022View details →

ECHAM6-HAM2 nudged simulation daily data using SMOGv1 India inventory

The variables mentioned below are taken from present-day and pre-industrial simulations. Hence the computations are applicable for both of them All the input variables are extracted to the Indian region during the south Asian monsoon season (JJAS)  1. Precipitation daily data is prepared by adding the variables like 'aprl' and 'aprc' from echam.nc model output  2. cloud droplet effective radiyus (CDER) at 850hPa is taken from model output 'filename_activ.nc' 3. Cloud liquid water path (CLWP) is computed as the sum of cloud liquid water from the surface to top of the atmosphere and their units (kg. Kg-1) converted into (Kg/m3)  4. Cloud condensation nuclei at 850hPa is extracted from the file 'file_activ.nc' 5. Lower tropospheric stability computed as the difference of potential temperature from the pressure levels 1000-700hPa 6. The variables like specific humidity, u wind and v wind is used for computing vertically integrated moisture flux 7. Convective available potential energy (CAPE) is computed using specific humidity (q), surface temperature, geopotential height, surface pressure  8. Black carbon concentration is computed as below   step.1: Add BC_KS+BC_AS+BC_CS+BC_KI   Step.2: Converting (kg. Kg-1) into (Mg/m3)   Step.3: Integrate the above values for the pressure levels 1000 to 850hPa 9. Same as followed for SO4

opencc-zeroFeb 2022View details →

APPENDIX 1 in Inventory of Cenozoic radiolarian species (Class Polycystinea) - 1834-2020

APPENDIX 1. — Cenozoic part of the International Chronostratigraphic, showing the subseries/subepoch scheme for the Paleogene and for the Neogene. Despite the wide use in the Cenozoic literature, the subseries/subepochs ranks are not yet officially accepted by the International Commission on Stratigraphy (with the exception for the Holocene and Pleistocene series). Since the beginning of this revision work, we have preferred to use the rank of "subseries" for the ages of the genera in this genera catalogue, as well as for the species in the appendices. We think that there are many solid reasons to keep using the subseries; even though, they have yet to be formally defined. Modified from Head et al. 2017 (see there a good discussion and proposal) and from the ICS International Chronostratigraphic Chart, July 2021 (http://www.stratigraphy.org/ICSchart/ChronostratChart2021-07.pdf). Abbreviations:L/E, Lower/Early;M, Middle;U/L, Upper/Late;e, early;l, late. Bold stages/ages are ratified by the Global Boundary Stratotype Section and Points (GSSP). Italic fonts indicate informal units and placeholders for unnamed units.

opencc-by-4.0Feb 2022View details →

A high-resolution gridded inventory of coal mine methane emissions for India and Australia

The dataset contains the high-resolution gridded coal mine methane emissions file (.csv) for India and Australia. The emissions are estimated for the year 2018 at a resolution of 0.1° × 0.1°. The emission unit is ton/grid/year.

Sample Inventory Data

This is an experimental publication of a sample data set. The goal is to test out low-threshold options for university collections to generate persistent identifiers in situations where no formal digital infrastructure is available.

opencc-by-4.0Sep 2021View details →

An Updated Inventory of Retrogressive Thaw Slumps Along the Vulnerable Qinghai-Tibet Engineering Corridor

An inventory of 875 retrogressive thaw slumps over a landscape of 54000 km2, along the Qinghai-Tibet Engineering Corridor underlain by permafrost, was compiled using remote sensing and DeepLabv3+, a kind of deep learning model. The file in the format of Geopackage/GPKG contains the boundary of each retrogressive thaw slump as vectors in the Coordinate Reference System of EPSG:32646 - WGS 84. The associated attribute table includes probability, time of the satellite images, source of the satellite images, the near roads labels, year of initiation, longitude and latitude, area (units: m2), Deep Learning model. The corresponding names for the table fields are ‘Probability’, ‘Year-month’, ‘Source Image’, ‘Near roads’, ‘Initial year’, ‘Longitude’, ‘Latitude’, ‘Area’, ‘Deep Learning model’. The ‘Probability’, having values of ‘High’, ‘Medium’ and ‘Low’, measures how much we are sure about the mapped RTSs.

Data from 'Disparate inventories of hypoxia gene sets across corals align with inferred environmental resilience'

Aquatic deoxygenation has been flagged as an overlooked but key factor driving mass bleaching-induced coral mortality as oxygen supplies lower to concentrations that can elicit an aerobic metabolic crisis i.e., hypoxia. Surprisingly little is known of the fundamental hypoxia responsive gene set inventory corals possess to respond to deoxygenation. It is unclear whether variation in gene copy number across species exist that potentially affect gene expression with subsequent differences in the effectiveness of a given stress response. Here, we used an ortholog-based meta-analysis to investigate how hypoxia gene inventories differed amongst coral species to assess putative copy number variation (CNV) across 24 coral protein sets from species with a sequenced genome that span corals from the robust and complex clade. We found approximately a third of the investigated genes exhibited copy number differences, and these differences were species-specific rather than the robust-complex split. Zipped folders of OrthoFinder results: 'Results_Feb16' contains results including all 24 coral species from 7 genera (Acropora, Pocillopora, Stylophora, Montastrea, Montipora, Obricella, Porites). 'gene_sets_acropora_acuminata_only' contains results including just one species per genera with Acropora acuminata. 'gene_sets_acropora_cytherea_only' contains results including just one species per genera with Acropora cytherea. 'gene_sets_acropora_digitifera_only' contains results including just one species per genera with Acropora digitifera.   Results and Interpretations from these analyses are published open access here: <a href="https://doi.org/10.3389/fmars.2022.834332">https://doi.org/10.3389/fmars.2022.834332</a> Full citation: Alderdice R, Hume BCC, Kühl M, Pernice M, Suggett DJ, Voolstra CR. Disparate inventories of hypoxia gene sets across corals align with inferred environmental resilience. Front Mar Sci. 2022;9. doi:10.3389/fmars.2022.834332 Scripts are available here: <a href="https://zenodo.org/record/6396671#.YoYpoS8RoZg">https://github.com/didillysquat/alderdice_2021</a>

i-SoMPE metadata of the data base of the inventory (A and B)

i-SoMPE metadata of the data base of the inventories A and B: main questions and all variables

i-SoMPE Inventory A: List and description of 58 innovative soil management practices

List and description of 58 innovative soil management practices (inventory A)

i-SoMPE Inventory A: Adoption rate of 58 innovative soil management practices

Adoption rate of 58 innovative soil management practices (maps of inventory A)

i-SoMPE Inventories A and B: open factor data

Open data (factor data) of the inventory (A and B)

opencc-by-4.0Jul 2021View details →

Multi-taxa environmental DNA inventories reveal distinct taxonomic and functional diversity in urban tropical forest fragments

Urban expansion and associated habitat transformation drives shifts in biodiversity, with declines in taxonomic and functional diversity. Forests fragments within urban landscapes offer a number of ecosystem services, and help to maintain biodiversity and ecosystem functions. Here, we focus on a tropical forest environment, and on the soil biota. Using eDNA metabarcoding, we compare forest fragments within the city of Cayenne, French Guiana, with a neighbouring continuous undisturbed forest. We wished to determine if urban forest fragments conserve high levels of alpha and beta diversity as well as similar functional composition for plants, soil animals, fungi and bacteria. We found that alpha diversity is similar across habitats for plants and fungi, lower in urban forests for metazoans and higher for bacteria. We also found that urban forests communities differ from undisturbed forests in their taxonomic composition, with urban forests exhibiting greater turnover between fragments potentially caused by ecological drift and limited dispersal. However, their functional composition exhibited limited differences, with an enrichment of palms, arbuscular mycorrhizal fungi and bacteria and a depletion of climber plants and termites. Thus, although urban forest fragments do shelter soil biodiversity that differs from native forests, the losses of soil functions may be relatively limited. This study demonstrates the strong potential of a multi-taxa eDNA approach for rapid inventories across taxonomic kingdoms, in particular for cryptic soil diversity. It also demonstrates the key role of urban forest fragments in conserving biodiversity and ecosystem function, and points to a need for more systematic monitoring of these areas in urban management plans. For each of the 16 samples per plot, 15 g of soil was used for eDNA analyses. Extracellular DNA was extracted as described previously (Zinger et al., 2016; 2019), where each soil sample is added to 15ml of saturated phosphate buffer (Na2HPO4; 0.12m; pH ≈8) in 50ml Falcon tubes. This is placed in an agitator for 15 minutes, before a 2ml aliquot of the soil/phosphate buffer mixture is pipetted into an Eppendorf tube and centrifuged for five minutes at 13000 rcf. 500μL of the resulting supernatant is then recovered and used for the next extraction steps that are carried out with a commercial kit for soil DNA (NucleoSpin® Soil; Macherey-Nagel, Düren, Germany), skipping the lysis step and following manufacturer’s instructions. The DNA extract was recovered in 100 μL and diluted 10 times before being used as PCR template.  For each plot one DNA extraction negative control was performed adding up 17 extractions per plot. PCR amplifications were then conducted for four DNA molecular markers, with primers targeting either Viridiplantae (subsequently referred to as plants), Eukaryotes, Fungi or Bacteria (Table 1). For each marker, PCR amplification of samples occurred across 12 plates. Each PCR reaction was performed in a total volume of 20 μl and comprised 10 μl of AmpliTaq Gold Master Mix (Life Technologies, Carlsbad, CA, USA), 5.84 μl of Nuclease-Free Ambion Water (Thermo Fisher Scientific, Massachusetts, USA), 0.25 μM of each primer, 3.2 μg of BSA (Roche Diagnostic, Basel, Switzerland), and 2 μl of DNA template that was before 10-fold diluted to reduce the amounts of PCR inhibitors. Thermocycling conditions for each primer pair are indicated in Table 1.  A negative extraction control per site and a negative PCR control per PCR plate were amplified and sequenced in parallel with the regular samples. Positive controls were also included and consisted of mock communities of plants and fungi DNA (no mock communities were built for bacteria or eukaryotes here), which were used to guide choices in our data curation process. Two PCR replicates were performed for each sample and control. Amplification was conducted using a double indexing system strategy (Binladen et al. 2007) using a system of 32 by 36 octamers with at least five differences between them located at the 5’ end of each primer (Coissac 2012). In doing so, each PCR product had a unique combination of tags for both forward and reverse primers, allowing for the retrieval of sequence data for each sample. Ten wells per PCR plate were left empty to act as sequencing controls (non-used tag combinations) for downstream data curation (see below). PCR products were pooled and sequencing libraries were constructed using the Illumina TruSeq NanoPCRFree kit following the supplier’s instructions (Illumina Inc., San Diego, California, USA), except that the ligation product was not PCR amplified to limit tag-jump biases (Taberlet et al 2018). The libraries were then sequenced on different Illumina platforms (San Diego, CA, USA) depending on the marker considered (Table S1), using the paired-end technology. Bioinformatic analyses were performed on the GenoToul bioinformatics platform (Toulouse, France), with the OBITOOLS package (Boyer et al. 2016). First, ‘illuminapairedend’ was used to assemble paired-end reads. This algorithm is based on an exact alignment algorithm that considers the quality scores at all positions during the assembly process. Subsequently, we used the ‘ngsfilter’ command to identify and remove the primers and tags on each read, and assign reads to their respective samples. This program was used with its default parameters tolerating two mismatches for each of the two primers and no mismatch for the tags. Following this, sequencing reads were dereplicated using the ‘obiuniq’ command. Sequences of low quality (containing Ns or with paired-end alignment scores below 50) were excluded using the ‘obigrep’ command. The same command was used to exclude sequences represented by only one read (singletons) as they are more likely to be molecular artefacts (Taberlet et al. 2018). Sequences outside of the preset range were also discarded (Table 1). To remove PCR/sequencing errors as well as intraspecific variability, we built OTUs (Operational Taxonomic Units) using the ‘sumaclust’ clustering algorithm (Mercier et al. 2013), which considers the most abundant sequence of each cluster as the cluster representative.  OTUs were set at a sequence similarity threshold of 97% for eukaryotes, fungi and bacteria following the standards in microbial ecology, but this was lowered to 95% for plants since the eDNA target region is shorter (typically around 50 base pairs), where one mismatch inherently results in a lower percentage of similarity. To assign a taxon to plant and fungal OTUs, we built two reference sequence databases, one global, using the ecoPCR programme (Ficetola et al. 2010) and the plant / fungi specific markers on the European Molecular Biology Laboratory (EMBL; release 141), a second local, generated from specimens of fungi (Jaouen et al. 2019) and plants (see Zinger et al. 2019) collected in French Guiana. OTUs were then assigned a taxonomy, using OBITOOL’s ecotag programme (Boyer et al. 2016), which performs a global alignment of each OTU sequence (the query) against each reference. The reference taxon assigned to each OTU corresponds to the Last Common Ancestor of all the best-match sequences for the query. For taxonomic assignment of bacteria and eukaryote OTUs, the SILVA taxonomic database was used (version 1.3; Quast et al., 2012). Classification was performed by a local nucleotide BLAST search against the non-redundant version of the SILVA SSU Ref dataset (release 132; http://www.arb-silva.de) using blastn (version 2.2.30+; http://blast.ncbi.nlm.nih.gov/Blast.cgi) with standard settings (Camacho et al., 2009).  Eukaryote derived metazoan OTUs were then further assigned a taxonomy for Phyla identified at the Arthropoda, Annelida and Nematoda level using reference sequence databases built as above for these groups using the ecoPCR programme on EMBL release 141. Datasets were subsequently filtered to remove contaminants as well as artefacts such as PCR chimeras and remaining sequencing errors, following Zinger et al. (2019) and using routines now implemented in the metabaR R package (Zinger et al 2020b), in R version 3.6.1 (R Development Core Team, 2013). The filtering process consisted of four steps: (i) a negative control-based filtering. OTUs whose maximum abundance was found in extraction/PCR negative controls were removed from the dataset, as they were likely to be reagent/aerosol contaminants, better amplified in the absence of competing DNA fragments as it is the case in biological samples. (ii) a reference-based filtering. OTUs which are too dissimilar from sequences available in reference databases are potential chimeras generated during sequencing and amplification. In this study, we chose to set similarity thresholds at 95% for plants, 80% for bacteria and eukaryotes and due to the marker being more polymorphic, 65% for fungi. For plants and fungi, the remaining assignment was then verified with the local database, to confirm if assigned taxa also occurred in the local dataset, with preference given to local assignment. In addition, we removed all taxa that are not targeted by the primer used. (iii) an abundance-based filtering. This procedure targets incorrect assignment of a few numbers of sequences corresponding to true OTUs occurring to the wrong sample, a phenomenon called “tag-switching” (Esling et al. 2015), “tag jumps” (Schnell et al. 2015) or “cross-talk” (Edgar 2018). It consists in setting OTUs abundances to 0 in samples where their abundance represents < 0.03% of the total OTU abundance in the entire dataset. (iv) Finally, we conducted a PCR-based filtering by considering any PCR reaction that yielded less than 100 reads for plants, 1000 reads for fungi, bacteria and eukaryotes as non-functional, and removed them from the dataset. Data provided consists of 4 x OTU tables for each of the markers used to target different components of the soil biota, with rows representing each OTU, and columns the features of the OTU within the dataset, namely their id code, the number of read counts in the analysed dataset, their similarity score against the taxonomic dataset used to identify them, and when possible, a functional group assignment used in the manuscript. Details of these can be found above and in the manuscript and supplementary information. For each of the four datasets, we also provide a .rds file, corresponding to the processed dataset used in manuscript preparation. This is in the format of a metabaR list which includes PCR, Sample, Read count and the seperately provided OTU datasets. To facilitate interpretation, please refer to Zinger, L., Lionnet, C., Benoiston, A.S., Donald, J., Mercier, C. and Boyer, F., 2021. metabaR: an R package for the evaluation and improvement of DNA metabarcoding data quality. Methods in Ecology and Evolution, 12(4), pp.586-592. For the fungal (ITS) data, we also provide :  - the R1/R2 raw fastq files of the samples used in the paper + experimental controls - a tsv file containing the tag combinations corresponding to the samples/PCR replicates, to enable demultiplexing of data. - a csv file containing the description of each sample.

Online Supplement for An Arabic Version of The Visual Aesthetics of Websites Inventory (AR-VisAWI): Translation and Psychometric Properties

This is the online supplement for a translation of the Visual Aesthetics of Websites Inventory into Arabic (AR-VisAWI).  In the field of human-computer interaction, the concept of visual aesthetics gained popularity after researchers started to recognize its merits and effects on user experience. Yet, no proper instrument exists to assess the visual aesthetics of websites that are intended for users who speak Arabic as their native language. As such, the aim of this study was to develop and evaluate an Arabic version of the Visual Aesthetics of Websites Inventory (VisAWI, Moshagen & Thielsch, 2010) and its short version (VisAWI-S, Moshagen & Thielsch, 2013). For this purpose, participants were asked to evaluate a randomly assigned website with the AR-VisAWI and with different validating instruments. A final sample of 223 participants was included in the analyses. This online supplement includes <ul> <li>a codebook describing all instructions and items</li> <li>raw data (anonymised) and analysis script (Note: The raw data contains only the information of persons who have agreed to be included in the analysis. Some demographic information was deleted to ensure anonymity.)</li> <li>Questionnaire template and scoring instructions</li> </ul>

opencc-by-4.0Jul 2022View details →

Biodata Resource Inventory Training Stats ( prelim)

Each file contains training statistics on the training and validation sets. These are preliminary results. They are being uploaded to perform proof of concept of using Binder to allow the R Markdown notebooks to be run in the browser.

opencc-by-4.0Aug 2022View details →

India Lightning Inventory

India Lightning Inventory - Paper under review

Patagonian Andes landslides inventory

We present the dataset developed in the research "Patagonian Andes landslides inventory: The deep learning's way to their automatic detection", submitted to the journal Remote Sensing (<a href="https://doi.org/10.3390/rs14184622">https://doi.org/10.3390/rs14184622</a>). “Landslide_database” folder: contains two ESRI shapefile-type vector files, the first (Ground_Truth_database), corresponds to the manually outlined landslides for training the deep learning model. The file contains the Sentinel 2 tile number, area, and perimeter. The second (Ground_Truth_database_centroid) corresponds to the centroid of the outlined landslides, in addition to the previously mentioned fields, it contains the X and Y coordinates. “Model results” folder: contains an ESRI shapefile vector file (Pred_T18GYS), corresponding to the landslides detected and segmented by the deep learning algorithm in the Sentinel-2 test tile. “Model validation” folder: contains multiple ESRI shapefile vector files used during model validation. Study area (Study_Area), roads (Roads) and populated areas (Localities). Contains the predicted landslides in the study area (Predict_T18GYS_SA), the randomly selected predicted landslides (Predict_T18GYS_Random) and their geometries (Predict_T18GYS_Geometry). The folder also contains manually delineated landslides (Groud_Truth_T18GYS) constrained to the extent of the assessed mosaic and delineated landslides that spatially match the predicted landslides (Ground_Truth_Random). Finally, true positives (TP_T18GYS), false positives (FP_T18GYS), and false negatives (FN_T18GYS) are provided separately.