Find research datasets worth reusing

opencc-by-4.0Aug 2020View details →

"Mapping specific mental content during musical imagery" - Musical stimuli

Six instrumental melodies composed by Joe Hisaishi, used as stimuli in the paper "Mapping the contents of consciousness during musical imagery"

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Data from: A new approach to map landscape variation in forest restoration success in tropical and temperate forest biomes

1. A high level of variation of biodiversity recovery within a landscape during forest restoration presents obstacles to ensure large scale, cost-effective, and long-lasting ecological restoration. There is an urgent need to predict landscape variation in forest restoration success at a global scale. 2. We conducted a meta-analysis comprising 135 study landscapes to predict and map landscape variation in forest restoration success in tropical and temperate forest biomes. Our analysis was based on the amount of forest cover within a landscape – a key driver of forest restoration success. We contrasted 17 generalized linear models measuring forest cover at different landscape sizes (with buffers varying from 5 to 200 km radii). We identified the most plausible model to predict and map landscape variation in forest restoration success. We then weighted landscape variation by the amount of potentially restorable areas (agriculture and pasture land areas) within the same landscape. Finally, we estimated restoration costs of implementing Bonn Challenge commitments in three specific temperate and tropical forest biome types in USA, Brazil and Uganda. 3. Landscape variation decreased exponentially as the amount of forest cover increased in the landscape, with stronger effects within a 5 km radius. Thirty-eight percent of forest biomes have landscapes with more than 27% of forest cover and showed levels of landscape variation below 10%. Landscapes with less than 6% of forest cover showed levels of variation in forest restoration success above 50%. 4. At the biome level, Tropical and Subtropical Moist Broadleaf Forests had the lowest (12.6%), while Tropical and Subtropical Dry Broadleaf Forests had the highest (22.9%) average of weighted landscape variation in forest restoration success. Our approach can lead to a reduction in implementation costs for each Bonn Challenge commitment between US$ 973 Mi and 9.9 Bi. 5. Policy implications. Our approach identifies landscape characteristics that increase the likelihood of biodiversity recovery during forest restoration – and potentially the chances of natural regeneration and long-term ecological sustainability and functionality. Identifying areas with low levels of landscape variation can help to reduce the risks and financial costs associated with implementing ambitious restoration commitments.

opencc-zeroAug 2020View details →

opencc-by-4.0Jul 2018View details →

Fig. 1 in Malacological mapping in Austria distribution of the Austrian spring snail Bythinella austriaca (v. F , 1857) in the federal state of Salzburg

Fig. 1: Geographical map of the federal state of Salzburg including all sample points with registered occurrence of the Austrian spring snail B. austriaca. Open circles mark sample locations published in literature, whereas filled circles represent sample points of own field investigations.

opencc-by-4.0Aug 2020View details →

21 cm intensity mapping: a 900 - 1300 MHz full-sky simulation

This HDF5 file stores the simulation used and described in <a href="http://dx.doi.org/10.1093/mnras/staa2854">Recovery of 21 cm intensity maps with sparse component separation</a> (<a href="https://arxiv.org/abs/2006.05996">arXiv:2006.05996</a>). It is a collection of intensity maps in units of mK, full-sky, in HEALPIX format with nside=256. There are 5 components: 1. 21cm cosmological signal, 2. galactic free-free diffuse emission, 3. galactic synchrotron diffuse emission, 4. extragalactic point sources and background and 5. instrumental polarization leakage. For each component, there are 400 maps, corresponding to 400 channels of 1 MHz from 900 to 1300 MHz. In <a href="https://doi.org/10.1093/mnras/staa2854">the main paper</a> the simulation is used to test the algorithm GMCA as a foreground removal method for 21cm intensity mapping. A practical example of how to use these data and apply GMCA on them is shown at this GitHub <a href="https://github.com/isab3lla/gmca4im">repository</a>. If you are using this dataset or the tools developed to work with it, please do not forget to cite the dataset itself as well as the relevant publication.

opencc-by-4.0Sep 2020View details →

Flux estimates for the Fermi map (Bayesian GCNN vs. NPTFit)

Prediction of the Bayesian graph-convolutional neural network (GCNN) for the Fermi photon-count map. The shaded regions show the predictive (aleatoric and epistemic summed in quadrature) 1σ uncertainty. The markers with error bars (68% credible intervals) indicate the Non-Poissonian template fit (NPTFit) estimates for comparison. The NN predictions for the GCE flux in the Fermi map are similar in magnitude to those of NPTFit, but the GCE is almost entirely attributed to the smooth dark matter template. To view the plot, open it with a browser such as Google Chrome or Firefox. Templates can be (de-)activated by clicking on the colored rectangles. For zooming and panning, click on the buttons in the lower left corner.

opencc-by-4.0Jul 2019View details →

SCRUM framework adaptations - dataset of systematic mapping study, EclipseIDE project

Dataset from the SCRUM framework adaptations systematic mapping study

opencc-by-4.0Oct 2020View details →

Sistematic Mapping review of literature about public policies innovation and reading competences

The systematic mapping method makes possible to identify what is known about a topic, what has been researched, what aspects remain unknown, and to obtain information about current trends and future challenges regarding this topic. In this Excel , the following articles were included: studies on innovative public policies related to reading skills, those published only in journals that can be found in two databases: Web of Science (WOS) and Scopus, published from January 2015 to December 2019, and related to the educational sector (social sciences, psychology, multidisciplinary, educational research, medical education, environmental education).

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Mapped read data and files and scripts from: Vicariance followed by secondary gene flow in a young gazelle species complex

Grant's gazelles have recently been proposed to be a species complex comprising three highly divergent mtDNA lineages (Nanger granti, N. notata and N. petersii). The three lineages have non-overlapping distributions in East Africa, but without any obvious geographical divisions, making them an interesting model for studying the early stage evolutionary dynamics of allopatric speciation in detail. Here we use genomic data obtained by restriction site-associated (RAD) sequencing of 106 gazelle individuals to shed light on the evolutionary processes underlying Grant's gazelle divergence, to characterize their genetic structure and to assess the presence of gene flow between the main lineages in the species complex. We date the species divergence to 134,000 years ago, which is recent in evolutionary terms. We find population subdivision within N. granti, which coincides with the previously suggested two subspecies, N.g. granti and N.g. robertsii. Moreover, these two lineages seem to have hybridized in Masai Mara. Perhaps more surprisingly given their extreme genetic differentiation, N. granti and N. petersii also show signs of prolonged admixture in Mkomazi, which we identified as a hybrid population most likely founded by allopatric lineages coming into secondary contact. Despite the admixed composition of this population, elevated X-chromosomal differentiation suggests that selection may be shaping the outcome of hybridization in this population. Our results therefore provide detailed insights into the processes of allopatric speciation and secondary contact in a recently radiated species complex.

opencc-zeroOct 2020View details →

Kenya Cropland Map and Non-Crop Labelled dataset

This dataset provides a 10m resolution map of cropland in Kenya for the 2019-2020 growing season (kenya_cropland_binary_2019.tif.zip) and a 10m resolution map of cropland in Busia County, Kenya for the 2020-2021 growing season (busia_cropland_binary_2020.tif.zip). Each pixel has a binary value, 0 if it does not contain crops and 1 if it does. These values were obtained by thresholding the predictions of an LSTM classifier trained on multi-spectral time series of Sentinel-2 satellite observations. A thresholding value of 0.5 was used. This dataset also provides the hand-labelled non-crop points used for training, which were created by labelling high-resolution satellite imagery in QGIS and Google Earth Pro. For more information, or if you use any part of this dataset, please refer to / cite the following paper: Gabriel Tseng, Hannah Kerner, Catherine Nakalembe and Inbal Becker-Reshef. 2020. Annual and in-season mapping of cropland at field scale with sparse labels. Tackling Climate Change with Machine Learning workshop at NeurIPS ’20: December 11th, 2020

Maps of interpolated paleotemperatures in Western Europe from MIS 14 to MIS 11

To support the ecological model of the study Rodríguez et al. (2020, in review), five BIOCLIM variables (BIO1, BIO6, BIO10, BIO11 and BIO12) were computed from the Oscillayers dataset, for 11 subdivisions of the  Marine Isotope Stages MIS 14 to MIS 11, as defined in (Rodríguez et al 2020, in review). Oscillayers is a global‐scale and region‐specific BIOCLIM paleoclimatic datasets with high temporal resolution spanning the Plio‐Pleistocene, facilitating the study of climatic oscillations during the last 5.4 million years at high spatial (2.5 arc‐minutes) and temporal (10 kyr time periods) resolution (Gamisch, 2019). BIOCLIM is a model designed for Species Distribution Modelling (SDM) that defines a set of 19 bioclimatic variables derived from monthly temperature and rainfall values in order to obtain biologically meaningful variables that are commonly used in ecology to model species or biome distributions (Booth et al., 2014; Nix, 1986). The GIS computation was conducted using GRASS GIS map algebra (Shapiro & Westervelt, 1991) scripted via its Python API. The according Python scripts are attached to this dataset.

Maps of interpolated paleotemperatures in Western Europe from MIS 14 to MIS 11

To support the ecological model of the study Rodríguez et al. (2020, in review), five BIOCLIM variables (BIO1, BIO6, BIO10, BIO11) were computed from the Oscillayers dataset, for 11 subdivisions of the  Marine Isotope Stages MIS 14 to MIS 11, as defined in (Rodríguez et al 2020, in review). Oscillayers is a global‐scale and region‐specific BIOCLIM paleoclimatic datasets with high temporal resolution spanning the Plio‐Pleistocene, facilitating the study of climatic oscillations during the last 5.4 million years at high spatial (2.5 arc‐minutes) and temporal (10 kyr time periods) resolution (Gamisch, 2019). BIOCLIM is a model designed for Species Distribution Modelling (SDM) that defines a set of 19 bioclimatic variables derived from monthly temperature and rainfall values in order to obtain biologically meaningful variables that are commonly used in ecology to model species or biome distributions (Booth et al., 2014; Nix, 1986). The GIS computation was conducted using GRASS GIS map algebra (Shapiro & Westervelt, 1991) scripted via its Python API. The according Python scripts are attached to this dataset.

opencc-by-4.0Dec 2020View details →

Disease and lesion maps - part of the Scientific opinion on the evaluation of public and animal health risks in case of a delayed post-mortem inspection in ungulates

EFSA was requested to assess the impact on effectiveness of post-mortem inspection in terms of any change in the sensitivity of detection of animal diseases of domestic and wild ungulates listed according to Article 5 of Regulation (EC) No 2016/429 and septicaemia, pyaemia, toxaemia or viraemia, when carried out after up to 24 hours or up to 72 hours after slaughter in comparison to when it is carried out immediately after slaughter. In order to identify the main lesions associated with the target diseases, a so called “disease map” was built, with the information about the clinical forms of the diseases  (acute, subacute, chronic, or latent forms) and clinical signs, as well as potential lesions that could be observed on the respective diseased animals. The following information is indicated for each disease/condition in the disease map: <ol> <li>The susceptible animal species</li> <li>Whether there is any surveillance programme in place in the EU</li> <li>The signs associated with the disease that could be detected at ante mortem inspection;</li> <li>The lesions associated with the disease that could be detected at post mortem inspection</li> <li>The probability of detecting the disease during the PMI as normally carried out.</li> <li>Whether carcass swabbing and/or laboratory tests are normally carried out.</li> </ol> From the disease map, the list of organs to be considered at post mortem inspection and the lesions, a “lesion map” was built by connecting animal species with organs, lesions and corresponding disease. This was done to facilitate the construction of a questionnaire where, for each organ and the five types of lesions, the respondents (meat inspectors) had to provide a numerical answer about how many carcasses out of 100 with the given lesion, will be still detected after 24- or 72-h of refrigerated storage.

opencc-by-4.0Dec 2020View details →

Landsat-based maps of irrigated dry season cropping in Southeastern Anatolia, Turkey

Landsat-based maps of irrigated dry-season cropping in Southeastern Anatolia, Turkey Long-term monitoring of the extent and intensity of irrigation systems is needed to track crop water consumption and to optimize land use in a changing climate. We mapped the expansion and land use intensity of irrigated dry season cropping in Turkey´s Southeastern Anatolia Project annually from 1990 to 2018 using Landsat time series and Google Earth Engine. This dataset includes multiple maps documenting the expansion and land use intensity of irrigated dry season cropping in Turkey´s largest irrigation scheme. We aggregated all Landsat imagery acquired during the July through September for the period 1990 to 2018 into spectral-temporal metrics and predicted dry season cropping annually using a machine learning classifier. We performed several post-processing steps to derive multiple map products for all areas with at least two dry season cropping cycles in the study period. The dataset comes in .zip format and includes the following map products: <ul> <li>gap_dsc_fst.tif: first year of dry season cropping</li> <li>gap_dsc_yrs.tif: number of years with dry season cropping</li> <li>gap_dsc_frq.tif: dry season cropping frequency (% of years since first dry season cultivation):</li> <li>gap_dsc_trd.tif: five-year dry season cropping frequency trend magnitude</li> <li>gap_dsc_pvl.tif: significance level (p-values)</li> </ul> Spatial coverage The maps come in 30m spatial resolution and cover the Southeastern Anatolia Project (Güneydoğu Anadolu Projesi, GAP) region. The region consists of nine provinces which account for approximately 10% of the Turkish land area.  Temporal coverage The analyses cover the period 1990-2018. The first year of dry season cropping and the number of years with dry season cropping represent the time period 1990-2017. The temporal coverage of dry season cropping frequency varies on a pixel level, depending on the initial year of dry-season cultivation. The temporal coverage of the trend indicators also vary on a pixel level and furthermore have a constrained maximum temporal coverage of 1992-2012 due to the post-processing steps involved. Data format The data are delivered as 16bit single layer GeoTIFFs in EPSG:3035 projection. The images are LZW compressed, and have NoData value 0. Publication & further information Please see the publication for further information on the methodology and accuracy of the map products: Rufin, P.; Müller, D.; Schwieder, M.; Pflugmacher, D.; Hostert, P. (2020): Landsat time series reveal simultaneous expansion and intensification of irrigated dry season cropping in Southeastern Turkey. Journal of Land Use Science. DOI: http://dx.doi.org/10.1080/1747423X.2020.1858198 Acknowledgments This research contributes to the Landsat Science Team 2018-2023 (http://www.usgs.gov/land-resources/nli/landsat/landsat-science-teams) and the Global Land Programme (https://glp.earth/). We gratefully acknowledge the open cloud processing platform provided by Google.

FIG. 7. — A in The zoomorphic representations of the Pîrî Reis map (1513)

FIG. 7. — A dragon from the Marvels of Things Created by al-Qazwīnī (1283). (Source: Library of Congress, World Digital Library).

FIG. 1 in The zoomorphic representations of the Pîrî Reis map (1513)

FIG. 1. — Carte de L'Atlantique, or "Pîrî Reis World Map" (1513). Parchment, 90 × 63 cm. Library of Topkapi Sarayi Muzesi, Istanbul.

FIG. 3. — A, Detail with a in The zoomorphic representations of the Pîrî Reis map (1513)

FIG. 3. — A, Detail with a parrot of Portrait of Paolina Adorno Brignole-Sale (1627), Anthony van Dyck (Museum of Palazzo Rosso, Genoa); B, Detail with a parrot from the painting Renaissance Interior With Banqueters (c. 1618-1620), Bartholomeus van Bassen's (Source: Google Art Project).

FIG. 2.— A in The zoomorphic representations of the Pîrî Reis map (1513)

FIG. 2.— A Medieval drawing of a whale. Sailors mistake the whale for an island (Source: British Library, Harley MS 4751, Folio 69r).

Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union in 2019 - high quality maps

Here you can find the high quality maps published in 'Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union in 2019' by EFSA.

opencc-by-4.0Dec 2020View details →

Point, polygon, or marker? In search of the best geographic entity for mapping Cultural Ecosystem Services using the online PPGIS tool, "My Green Place."

Excel files include the raw database and the processed data that led to the quadrat analyses. The "Matrix_raw data" file includes the raw data as downloaded from the server. This data was cleaned and organized for its posterior use. "Quadrat analyzes "file includes all the quadrat analyses resulting in each research question in the paper except question four. Question 4 can be seen in the file "Water analysis_Blaarmeersen." All excel files come with a "CODE" tab that describes each of the codes used, their meaning, and ways that were calculated where necessary. Two zip files include all the GIS files. The first one includes the GIS files from which "Matrix_raw data" was built from. The second folder includes the resulting maps from the quadrat analyses. In order to visualize them as in the paper, configure the symbology tab at the GIS software in quantile and the categories number, as shown in the paper. The production of the files in the "GIS_Processed data" folder was done via a repetitive line of commands in ArcGIS pro. The same process was followed for each one of the quadrat analysis described in the paper. Refer to "<a href="https://zenodo.org/api/files/72403e0b-78a9-4bbf-8dca-ed9b8702e3ae/Reproduction%20commands%20and%20parameters.pdf">Reproduction commands and parameters.pdf</a>" for further information.