Find research datasets worth reusing

opencc-by-4.0Dec 2023View details →

Maldivian seagrass aerial extent raster layers 2021 - 2000

Contemporary Seagrass Map (2021) The contemporary product was derived from Sentinel-2 satellite imagery, operated by the European Space Agency (ESA). The imagery, with a spatial resolution of 10 meters was pre-processed in Google Earth Engine (GEE) following established methods for retrieval of benthic signals. A support vector machine (SVM) classifier was used for classification. Training data encompassed three classes: seagrass, non-seagrass (including coral reefs, mangroves, sand/rubble, and macroalgal beds), and optical-deep water (ODW), totaling 25,463 training pixels. Important: the classification output is a binary (seagrass/non-seagrass) class. Validation of the map was conducted independently using 1,019 in-situ field survey points collected from 2017-2023. Mapping accuracy was assessed through an error matrix. Overall accuracy = 82% Historical Seagrass Maps (2000-2021) The historical mapping product is derived from Landsat data spanning 2000 to 2021. The Landsat missions, operated by the United States Geological Survey (USGS) in collaboration with NASA, provide satellite data with a spatial resolution of 30 meters. There are no suitable data for 2010-2011. Each composite, representing a two-year period, underwent radiometric normalisation relative to a reference image from 2020-2021. Training and validation data were designated using an identical methodology as the contemporary maps, with 823 validation points utilised for accuracy assessment from 2017-2023. A fixed pixel approach was adopted to assess accuracy across the entire time series, involving the manual delineation of seagrass and non-seagrass areas. Overall accuracy was >89% in all cases.   These data represent GeoTIFF files of seagrass habitat extent (binary classification). Contemporary data come from habitat classification of Sentinel-2 imagery (10 m pixel size). Historical maps come from habitat classification of Landsat data (30 m pixel size). For further details of workflow and data specifications please see the original publication DOI: 10.1038/s41598-024-61088-1

opencc-by-3.0-atMar 2023View details →

Assessment of the spatial extent of local active noise control - dataset

Experiment setup, data and supplementary scripts for the listening experiment "Assessment of the spatial extent of local active noise control" in Feb. 2023 at the Institute of Electronic Music and Acoustics in Graz, Austria A listening experiment has been conducted, trying to find the spatial extents of local active noise control (ANC). Participants were instructed to move their heads along the x-axis or rotate clock- and counterclockwise (yaw). A button had to be pressed if the perceived zone of comfort is left, and the current position was recorded. This task was repeated for different controlled bandwidths, signals and source directions. Additionally, scripts to generate the ANC filters, a brief statistical analysis and recordings at several positions for different stimuli/conditions are provided. Acoustic measures at the point of cancellation and at various other positions are further analyzed with and without ANC.

opencc-by-4.0Oct 2023View details →

Alaska Landfast Ice Extent and Breakup Timing over 2000-2022

This dataset contains a landfast ice climatology calculated from MODIS imagery. For a complete description of the methods, please see Cooley and Ryan (2024), Community-scale changes to landfast ice along the coast of Alaska over 2000-2022, Environmental Research Letters, <a href="https://iopscience.iop.org/article/10.1088/1748-9326/ad1c7b">doi.org/10.1088/1748-9326/ad1c7b</a> This dataset contains a shapefile “landfast_ice_edge_2000_2022.shp” of maximum ice edge positions derived from MODIS imagery. The ‘time period’ column refers to the 30-day period over which the edge position was calculated, specifically 1: March 1st to April 1st, 2: March 15th to April 15th and 3: April 1st to May 1st. For years with multiple lines and/or multiple time periods present in the dataset, we retain the maximum position in our analysis; in other words, we calculate the ice edge position based on the furthest line from the coast. This dataset also contains time series of seaward landfast ice edge (SLIE) position (distance to the ice edge from the coastline, in km) and breakup timing (in day of year). The ice edge position is calculated as the maximum ice edge distance from coastline using the ice edge shapefile described above. The breakup date is defined as the day when 75% of the area contained within the maximum ice edge position for that year reaches open water. This is corrected for cloud cover by assigning the actual breakup date as the midpoint between the first day when the ice was open water and the previous cloud-free observation. This uncertainty (in days) is recorded as “Breakup Uncertainty”.  Any usage of this data should also cite Cooley and Ryan (2024). For any questions about the dataset, please reach out to Sarah Cooley at scooley2@uoregon.edu

opencc-by-4.0Jan 2016View details →

Q3. To what extent that information from the Internet should be censored?-Computer-Mediated Security Threats into the Web 2.0 Society

Following this distribution, we can observe that a large part of the subjects (44,7%) consider that the information from the internet should be moderately censored (against various risks and social dangerous). Beside them, 26.6% evaluate that this process should be wider, and 28.7% think it should be lower. These two almost equal groups consolidate the central trend, and reflect a quite important worry concerning this perspective. The Internet has begun as an almost totally free of control medium, a quasi-anarchic structure, a symbol for the freedom of expression for any users. In time, it has become more and more accessible, and a large amount of information has become available inside the Info-sphere. Now, due to some negative consequences, it looks like starting to censure some information is not a prohibitive idea anymore, but rather a desirable one. We want to stress on the fact that the sample was formed by regular users that are not experts in security issues and thus, the recognizing of the importance of having a moderate censuring onto the Internet is a very significant conclusion. With an indirect connection to the previous item, the next question has directly approached a special topic related to the user’s knowledge about the dark web and/or deep web. These represent a quite particularly topic among the Internet users, and represent an area where the IP tracking is difficult or not-possible. Into this kind of sites and applications any user can remain anonymous, no matter what he/she does, posts or accesses.

opencc-by-4.0Feb 2019View details →

Data for: Extention at the coast of the Makran subduction zone (Iran)

This dataset comes from the study of normal faults at the coast of the Iranian Makran, near the city of Chabahar. The data is published in support for the paper mentioned in the title. The dataset contains: A - A series of fault measurements made in the region of Chabahar, with the following subgroups: a) Major Faults of Chabahar headland, b) minor faults of Chabahar headland, c) faults measured from the Konarak headland, d) faults measured in the Parak area, in the Makran ranges north of Chabahar, e) Bedrock bedding orientations. B - Shapefiles and KMZ file containing the structural measurements in A. C - Field pictures of normal faults and observed evidence of overpressure in Tertiary sediments.

opencc-by-4.0Mar 2019View details →

A Multi-Challenge Clustering Benchmark Dataset Embedding Large Differences in Spatial Extent

This artificial clustering benchmark dataset was designed manually and draws its inspiration from structural aspects that can be seen in principal component plots of hyperspectral image data. Distance-separated, density-separated, gradient-separated as well as connected clusters have been placed into the dataset. Following the notion that clusters may vary significantly with respect to their spatial extent the respective separability problems are scaled at different levels and only become visible by magnifying certain parts of the dataset. Another special aspect of this dataset is that cluster borders have been kept rather ambiguous which, in our opinion, better resembles the situation in spectroscopic data.

opencc-by-4.0Jul 2021View details →

Extention to the TNBC dataset, brain section and cell type

This dataset is partly an extension of the TNBC dataset that can be found in the following link: https://zenodo.org/record/1175282#.Ws2n_vkdhfA In particular, we give the cellular type annotations of each annotated cell. In addition to the TNBC, we added 18 annotated images of brain section downloaded from the TCGA. This is still officially unpublished and has been submitted. Currently, if you use this dataset please our paper "Scale dependant layer for self-supervised nuclei encoding" by Peter Naylor, Yao-Hung Hubert Tsai, Marick Laé and Makoto Yamada.

Figure 6 in Maxent modeling for predicting potential distribution of goitered gazelle in central Iran the effect of extent and grain size on performance of the model

Figure 6. Total cross-validation AUC (CV-AUC) and spatial congruence AUC (SC- AUC) for a range of grain sizes.

Figure 4 in Maxent modeling for predicting potential distribution of goitered gazelle in central Iran the effect of extent and grain size on performance of the model

Figure 4. Divergence between the uncorrelated and pruned models estimated through Parolo divergence index at 250-m resolution. As is shown, there was little divergence (0– 0.2) between models in most of the study area.

Figure 1 in Maxent modeling for predicting potential distribution of goitered gazelle in central Iran the effect of extent and grain size on performance of the model

Figure 1. The location of the study area on a map of western Asia (right). Inset shows DEM of study area with polygons indicating the protected areas where populations of goitered gazelle occur.

Figure 5 in Maxent modeling for predicting potential distribution of goitered gazelle in central Iran the effect of extent and grain size on performance of the model

Figure 5. The change in performance index (AUC, left) and habitat suitability area (right) of the output model with increasing extent size (open circles) and grain size (filled circles) from 250 to 3000 m.

Figure 3 in Maxent modeling for predicting potential distribution of goitered gazelle in central Iran the effect of extent and grain size on performance of the model

Figure 3. Goitered gazelle distribution maps based on the uncorrelated model (left) and the pruned model (right) for the 250-m grid size.

Figure 5 in To what extent do pine and oak clear-cut stumps support mite (Acari: Mesostigmata) communities in temperate forests?

Figure 5. Proportional abundance of dominant mite species in CWD and adjacent litter in Scots pine (A) and sessile oak (B) forest.

Figure 6 in To what extent do pine and oak clear-cut stumps support mite (Acari: Mesostigmata) communities in temperate forests?

Figure 6. DCA biplot species data for the different microhabitat of the forest floor. Microhabitat are marked as: tree species [SP - Scots pine and SO - sessile oak] and 5 microhabitat types: [(CWD - coarse woody debris, ecotone, and litter at distances of 0.5, 1.0, and 1.5 m)].

Figure 3 in To what extent do pine and oak clear-cut stumps support mite (Acari: Mesostigmata) communities in temperate forests?

Figure 3. Species accumulation curves (A, B) and proportional abundance (C, D) of Gamasina and Uropodina in litter/soil and CWD (stumps) in Scots pine and sessile oak forest.

Figure 1 in To what extent do pine and oak clear-cut stumps support mite (Acari: Mesostigmata) communities in temperate forests?

Figure 1. Sampling design in sessile oak and Scots pine forest. The oak and pine stumps were sampled in three directions from five microhabitats.

Fig. 5 in The extent of the pterosaur flight membrane

Fig. 5. Photographs and line drawings of selected specimens showing a pronounced contraction of the wing. A. Rhamphorhynchus longicaudus (Münster, 1839), BSPG 1938 I 503a, Solnhofen Limestone (Upper Jurassic), Germany; photograph (A1), explanatory drawing (A2). B. Jeholopterus sp. (Ji and Yuan 2002), Lower Yixian Formation (Early Cretaceous), China; photograph (B1), explanatory drawing (B2). Tissue belonging to the wings is marked in bold outlines in A2 and B2.

Fig. 6. Two contrasting pterosaur phylogenies. A in The extent of the pterosaur flight membrane

Fig. 6. Two contrasting pterosaur phylogenies. A. Phylogeny from Lü et al. (2009). B. A simplified version of that presented by Wang et al. (2005). Thick black lines indicate species or groups of taxa for which the fossil evidence supports an ankle attachment of the wing.

Fig. 4 in The extent of the pterosaur flight membrane

Fig. 4. Line drawings of the pterosaur specimens depicted in Fig. 3 or specific points of interest. Tissue belonging to the wings is marked in bold outlines. A. Rhamphorhynchus muensteri, the "Dark Wing", JME SOS 4784, Eichstätt region (Upper Jurassic), Germany. B. Jeholopterus ningchengensus, IVPP V 12705, Lower Yixian Formation (Early Cretaceous), China. C. Pterodactylus kochi, "Vienna specimen", NHMW 1975/1756, Solnhofen Limestone (Upper Jurassic), Germany. D. Anurognathus ammoni, private specimen (Bennett 2007), Solnhofen Limestone (Upper Jurassic), Germany. E. Eosipterus yangi, GMV 2117, Lower Yixian Formation (Early Cretaceous), China. F. Sordes pilosus, PIN 2585/3, Karatau Formation (Upper Jurassic), Kazakhstan. G. Tapejarid indet., SMNK PAL 3830, Crato Formation (Early Cretaceous), Brazil.

Fig. 3 in The extent of the pterosaur flight membrane

Fig. 3. Selected photographs of pterosaur specimens displaying soft tissue preservation around the hind limbs and/or ankles. A. Rhamphorhynchus muensteri (Goldfuss, 1831), the "Dark Wing", JME SOS 4784, Eichstätt region (Upper Jurassic), Germany. B. Jeholopterus ningchengensus Wang, Zhou, Zhang, and Xu, 2002, IVPP V 12705, Lower Yixian Formation (Early Cretaceous), China. C. Pterodactylus kochi (Wagner, 1837), "Vienna specimen", NHMW 1975/1756, Solnhofen Limestone (Upper Jurassic), Germany. D. Anurognathus ammoni Döderlein, 1923, private specimen (Bennett 2007b), Solnhofen Limestone (Upper Jurassic), Germany. E. Eosipterus yangi Ji and Ji, 1997, GMV 2117, Lower Yixian Formation (Early Cretaceous), China. F. Sordes pilosus Sharov, 1971, PIN 2585/3, Karatau Formation (Upper Jurassic), Kazakhstan. G. Tapejarid indet., SMNK PAL 3830, Crato Formation (Early Cretaceous), Brazil.