Find research datasets worth reusing

opencc-by-4.0Aug 2023View details →

SinoLC-1: the first 1-meter resolution national-scale land-cover map of China created with the deep learning framework and open-access data (User guide V2.4)

The User Guide V2.4 of the SinoLC-1 land-cover product. The SinoLC-1 was created by the Low-to-High Network (L2HNet), which can be found at: <a href="https://doi.org/10.1016/j.isprsjprs.2022.08.008">L2HNet</a>. A more detailed description of the data can be found in the <a href="https://doi.org/10.5194/essd-15-4749-2023">paper</a>. More related work can be found at my <a href="https://lizhuohong.github.io/lzh/">homepage</a>. <a href="https://zenodo.org/search?q=parent.id%3A7707461&f=allversions%3Atrue&l=list&p=1&s=10&sort=version">Click to check all the data versions and download the data (点击查看/下载所有数据版本)</a> NOTE: If you have any data needs, questions, or technical issues, contact us at <a href="http://ashelee@whu.edu.cn">ashelee@whu.edu.cn</a> (Zhuohong Li, 李卓鸿). The land-cover mapping method with Python code is open-access at <a href="https://github.com/LiZhuoHong/Paraformer/">Code link</a>. You can now update the high-resolution land-cover map by yourself with the code! The updated method is accepted by CVPR 2024 (<a href="https://arxiv.org/abs/2403.02746">Paper link</a>). 我们的最新制图算法被计算机视觉顶会CVPR2024接收(<a href="https://arxiv.org/abs/2403.02746">Paper link</a>),代码开源在:<a href="https://github.com/LiZhuoHong/Paraformer/">Code link</a>,您可以利用该代码高效地更新自己数据集的高分土地覆盖图。 Citation format of the paper: Li, Z., He, W., Cheng, M., Hu, J., Yang, G., and Zhang, H.: SinoLC-1: the first 1 m resolution national-scale land-cover map of China created with a deep learning framework and open-access data, Earth Syst. Sci. Data, 15, 4749–4780, 2023.  Li, Z., Zhang, H., Lu, F., Xue, R., Yang, G. and Zhang, L.: Breaking the resolution barrier: A low-to-high network for large-scale high-resolution land-cover mapping using low-resolution labels, ISPRS Journal of Photogrammetry and Remote Sensing. 192, pp.244-267, 2022. BibTex format of the paper: <blockquote> <pre>@article{li2023sinolc, title={SinoLC-1: the first 1 m resolution national-scale land-cover map of China created with a deep learning framework and open-access data}, author={Li, Zhuohong and He, Wei and Cheng, Mofan and Hu, Jingxin and Yang, Guangyi and Zhang, Hongyan}, journal={Earth System Science Data}, volume={15}, number={11}, pages={4749--4780}, year={2023}, publisher={Copernicus Publications G{\"o}ttingen, Germany} }</pre> <pre>@article{li2022breaking, title={Breaking the resolution barrier: A low-to-high network for large-scale high-resolution land-cover mapping using low-resolution labels}, author={Li, Zhuohong and Zhang, Hongyan and Lu, Fangxiao and Xue, Ruoyao and Yang, Guangyi and Zhang, Liangpei}, journal={ISPRS Journal of Photogrammetry and Remote Sensing}, volume={192}, pages={244--267}, year={2022}, publisher={Elsevier} }</pre> </blockquote>

opencc-by-nc-4.0Aug 2023View details →

Dataset for Deep learning solutions for mapping contour levee rice production systems from very high resolution imagery

This dataset contains the two datasets detailed in "Deep learning solutions for mapping contour levee rice production systems from very high resolution imagery" by D.S. Dale Et al. (2023).  The file "LonokeComplete.zip" file contains 16 .lif files that were used in the training and testing phase of the study.  The "55tilesComplete.zip" file contains 110 .tif files (55 image and 55 label). These images were used to assess the models spatial transferability.  Both file configurations are processed by the code linked in the paper.

opencc-zeroAug 2023View details →

Estonian National Bibliography - maps

A subset of maps metadata from the Estonian national bibliography. The Estonian national bibliography (Eesti rahvusbibliograafia - ERB) covers publications in all languages in Estonia and abroad, regardless of the medium and date of publication. The database of the national bibliography is constantly updated with newly published works. A separate part of the national bibliography is the Estonica collection, which includes both publications in foreign languages by Estonian authors and publications about Estonia and Estonians published abroad. The database is managed by the National Library of Estonia, which manages the Estonian national bibliography in accordance with the relevant laws. For more info, see <a href="https://digilab.rara.ee">https://digilab.rara.ee</a>.

opencc-by-3.0-usAug 2023View details →

Foliar functional trait maps for six sites along the Western Ghats of India

Maps of seven key foliar functional traits for six sites along the Western Ghats of India, derived using field-sampled data and hyperspectral images acquired by NASA's Airborne Visible/ Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG). Traits in this dataset include:  Leaf mass per area (LMA) in g/m2, foliar nitrogen in % dry mass (%dm), foliar fiber (%dm), foliar lignin (%dm), foliar sugars (%dm), foliar calcium (%dm), and total phenolics (%dm).

opencc-zeroAug 2023View details →

NOAA NCCOS Assessment: Agency priorities for mapping coral reef ecosystems in Guam and the Commonwealth of the Northern Mariana Islands, 2023-02-22 to 2023-06-12

Description: NOAA's Coral Reef Conservation Program (CRCP has identified a need for priority locations based on emerging management requirements in shallow coral reef areas (up to 40 meters) surrounding Guam and the Commonwealth of the Northern Mariana Islands (CNMI). The priorities provided by participating agencies will inform research and monitoring activities, address current and future management needs, and maximize opportunities to leverage and complement existing regional efforts. To meet this need, NOAA’s National Centers for Coastal Ocean Science (NCCOS) developed a systematic, quantitative approach and online GIS application to gather seafloor mapping priorities from researchers and coral reef managers. Participants placed virtual coins into a grid overlaid on the project area to express the location of their mapping priorities. They also used pull-down menus to indicate specific mapping data needs and the rationale for their selections. Participants’ inputs were compiled and analyzed to identify high priority areas along with their justifications and requirements. A total of seven participant groups entered their mapping priorities into the online tool for Guam and ten participant groups for CNMI. Identifying these high priority areas provide a critical spatial framework for prioritizing mapping efforts in shallow coral reef ecosystems in Guam and CNMI. Purpose: The overall goal of the project was to systematically gather and quantify suggestions for mapping needs to support management of shallow coral reef ecosystems along the coasts of the Guam and CNMI. This dataset supports these goals by compiling input from a diversity of regional experts on their recommended priorities for mapping data collection. Methods: An advisory group was established which included individuals from NOAA CRCP and NOAA Fisheries. This advisory team customized the prioritization process specifically to meet the needs of CRCP and local coral reef manager priorities. In the online prioritization tool, the Guam study area was divided into 153 hexagonal grid cells 2.6 km2 in size. The CNMI study area was divided into 330 hexagonal grid cells 2.6 km2 in size. Existing relevant spatial datasets (e.g., bathymetry, Sanctuary Protection Areas, etc.) were provided as a digital atlas to help participants understand information and data gaps within the project area and to identify locations they wanted to prioritize for future data collections. Each Guam participant was provided with 50 virtual coins to place into grid cells that they wished to prioritize. Each CNMI participant was provided with 110 coins. They were instructed to place more coins in grid cells that were higher priorities. A maximum of 5 coins could be placed into an individual grid cell in Guam by each respondent, and a maximum of 11 coins could be place into an individual grid cell in CNMI. Respondents also reported why these locations were important by selecting a minimum of one, and a maximum of two, management uses from the following list: endangered species management (e.g.,), habitat restoration, monitoring, coastal vulnerability planning, watershed management, fisheries management, consultations and permitting, emergency response, and spatial protection and management. Respondents also reported requirements of data were needed in priority cells. A minimum of one, to a maximum of two choices were selected from the following list: delineations of large topographic features, delineations of hard vs. soft bottom, models of habitat suitability for key taxa or communities, delineations of substrate type (e.g. sand, mud, coral, rock), models of presence/absence or density of corals, identification of coral species and their local environments, documentation of individual specimen condition. Coin values were summarized and mapped to identify high priority areas, reasons for those priorities, and information needs. This ESRI shapefiles contain the 2.6 km2 grid cells used in this prioritization and their associated coin values overall, as well as by management use, data product, and mapping methodology. Other summary values include the number of participants, number of participating groups, number of management uses, and number of data requirements. Additionally, coins for microscale (identification of coral species and their local environments and documentation of individual specimen condition), mesoscale (delineations of substrate type, models of presence/absence/density of corals), and regional (delineations of topographic features, delineations of hard vs. soft bottom, models of habitat suitability) requirements were summarized. Also included is a ranking of each grid cell based on the total number of coins, management uses, and participating groups allocating coins in the respective cell. For a complete description of the process and analysis see: Hile et al. 2023, in prep.

opencc-by-4.0Aug 2023View details →

Fine mapping of 5 chronic pain types

Fine mapping was run with CAUSALdb-finemapping-pip using clumped GWAS data of 5 chronic pain types: chronic neck, back, hip and knee pain and chronic headaches.

opencc-by-4.0May 2023View details →

Maps of solar wind plasma precipitation onto Mercury's surface: a geographical perspective

Data Archive to accompany: "Maps of solar wind plasma precipitation onto Mercury’s surface: a geographical perspective." Federico Lavorenti, Elizabeth A. Jensen, Sae Aizawa, Francesco Califano, Mario D’Amore, Deborah Domingue, Pierre Henri, Simon Lindsay, Jim M. Raines, and Daniel Wolf Savin. Submitted 2023 May to Planetary Science Journal. The files contained in this archive comprise the values shown in Figures 3 & 5.

opencc-by-4.0Oct 2022View details →

Finding Aid for Harold Garfinkel Papers on "Occasion Maps"

Finding Aid for Harold Garfinkel Papers on "Occasion Maps"

opencc-by-4.0Oct 2022View details →

Integrative QTL mapping and selection signatures in Groningen White Headed cattle inferred from whole-genome sequences

Here, we aimed to identify and characterize genomic regions that differ between Groningen White Headed (GWH) breed and other cattle, and in particular to identify candidate genes associated with coat color and/or eye-protective phenotypes. Firstly, whole genome sequences of 170 animals from eight breeds were used to evaluate the genetic structure of the GWH in relation to other cattle breeds by carrying out principal components and model-based clustering analyses. Secondly, the candidate genomic regions were identified by integrating the findings from: a) a genome-wide association study using GWH, other white headed breeds (Hereford and Simmental), and breeds with a non-white headed phenotype (Dutch Friesian, Deep Red, Meuse-Rhine-Yssel, Dutch Belted, and Holstein Friesian); b) scans for specific signatures of selection in GWH cattle by comparison with four other Dutch traditional breeds (Dutch Friesian, Deep Red, Meuse-Rhine-Yssel and Dutch Belted) and the commercial Holstein Friesian; and c) detection of candidate genes identified via these approaches. The alignment of the filtered reads to the reference genome (ARS-UCD1.2) resulted in a mean depth of coverage of 8.7X. After variant calling, the lowest number of breed-specific variants was detected in Holstein Friesian (148,213), and the largest in Deep Red (558,909). By integrating the results, we identified five genomic regions under selection on BTA4 (70.2–71.3 Mb), BTA5 (10.0–19.7 Mb), BTA20 (10.0–19.9 and 20.0–22.7 Mb), and BTA25 (0.5–9.2 Mb). These regions contain positional and functional candidate genes associated with retinal degeneration (e.g., CWC27 and CLUAP1), ultraviolet protection (e.g., ERCC8), and pigmentation (e.g. PDE4D) which are probably associated with the GWH specific pigmentation and/or eye-protective phenotypes, e.g. Ambilateral Circumocular Pigmentation (ACOP). Our results will assist in characterizing the molecular basis of GWH phenotypes and the biological implications of its adaptation.

Map of public and private policies and governance mechanisms

This Excel database maps key public and private policies and governance mechanisms regulating legal and sustainable forest and ecosystem-risk biomass value chains across demand and supply-side countries and regions.

SDS_Benchmark: a testbed for shoreline mapping algorithms using satellite imagery

This is an archived copy of the following Github repository: https://github.com/SatelliteShorelines/SDS_Benchmark

Soil texture dataset from the publication: "Machine learning applied for Antarctic soil mapping: Spatial prediction of soil texture for Maritime Antarctica and Northern Antarctic Peninsula'

Clay, silt and sand distribution in Antarctic soils modeled and predicted through Machine Learning approaches, legacy soil data and environmental covariates. The coefficient of variation and quantile data represent the spatial uncertainty of the predictions. For more information about the methodology used, users are referred to the article:  Siqueira, R.G., Moquedace, C.M., Francelino, M.R., Schaefer, C.E.G.R., Fernandes-Filho, E.I., 2023. Machine learning applied for Antarctic soil mapping: Spatial prediction of soil texture for Maritime Antarctica and Northern Antarctic Peninsula. Geoderma 432, 116405. https://doi.org/10.1016/j.geoderma.2023.116405 The .zip file has the following folders: 1) soil_texture_antarctica: soil texture information containing clay, silt and sand contents 2) soil_texture_coefficient_variation: uncertainty from the coefficient of variation of the soil texture prediction 3) soil_texture_prediction_interval: uncertainty from the prediction interval 90% (Q95% - Q5%) of the soil texture prediction 4) soil_texture_quantile05: quantile 5% of the soil texture prediction 5) soil_texture_quantile95: quantile 95% of the soil texture prediction

Mapping Seyahatname-i Hudud_Baghdad and Basra Chapters

This dataset lists the location information of place names mentioned in the Baghdad and Basra chapters of Seyahatname-i Hudud.

Fig. 4. A in Good Reasons and Guidance for Mapping Planktonic Protist Distributions

Fig. 4. A variogram for the ciliate Pleuronema sp. (inset) abundance. The best fit to the data (points) provided a pure nugget model; i.e. the distribution is random at the measured scale (40 m), with no observed patchiness.

Fig. 3. A in Good Reasons and Guidance for Mapping Planktonic Protist Distributions

Fig. 3. A time series of Cyrtostrombidium sp. (inset) abundance during ~ 1 year at a fix point in a coastal lagoon. The autocorrelation function indicates positive spikes for weeks 2, 3 and 4 suggesting a persistence of Cyrtostrombidium bloom for ~ 1 month. Horizontal dashed lines indicate the ~ 95% confidence interval for the signifi- cance of each autocorrelation value.

Fig. 2 in Good Reasons and Guidance for Mapping Planktonic Protist Distributions

Fig. 2. Geostatistical analysis of Lohmaniella oviformis (inset in a) abundance (cells ml–1) produces: a) the variogram, b) the kriging map, and c) a map of the coefficient of variation (CV). A spherical model (a, line) is fit to the empirical variogram (a, points); the points account for different number of pairs of abundance averaged on a class distance (lag). Only half of the maximum distance was calculated and represented to avoid the edge effect, where there are fewer sampling points (see text). The model (a, line) is used to predict abundance at unsampled points and to assess characteristics of patches. The model is also used to map patches of L. oviformis abundance (b, grey areas) using the kriging interpolator; a patch is operationally defined as abundance in the upper quartile. On the CV map (c), grey areas (with lower abundance and closer to edges) have the highest coefficient of variation of the estimated distribution.

Fig. 1. A in Good Reasons and Guidance for Mapping Planktonic Protist Distributions

Fig. 1. A schematic description of establishing a variogram, modelling a function, and producing maps by kriging. Samples (e.g. to determine ciliate abundance) are collected at points of a sampling grid (a). Variance estimates of ciliate abundances at points separated by a common distance (lag, h) are calculated using the equation (explanations in the text); this is repeated for each lag (three examples of lags are illustrated in a). Each variance estimate is then plotted against its respective lag to produce an empirical variogram (points in b). Then, a model is fit to the variogram data (lines in b), and the model is used to predict abundance at unsampled points and to characterize patches. The parameters of the variogram models are the nugget, the range, and the sill (see text for their interpretation). Three models are the most common: the Gaussian, spherical and exponential (thick, medium, and thin lines, respectively, in b). Models are used to map ciliate abundance by the kriging procedure, with each model producing different predicted distributions (c, d, e): the spherical and exponential produce "fuzzier" images than the Gaussian.

Fig. 6 in Good Reasons and Guidance for Mapping Planktonic Protist Distributions

Fig. 6. Patches of total phytoplankton biomass (ng C ml–1, left) and total ciliate abundance (cells ml–1, right) in the Irminger Sea, North Atlantic. The spatial coincidence indicates a potential prey-predator relationship.

QPI mapping of PTCDA on Ag111 with sparse sampling

This measurement was initiated by Danyang Liu and implemented together with Jens Oppliger, Dr. Taner Esat, and Valentin Maissen in the lab of Prof. Ruslan Temirov at PGI-3, Forschungszentrum Jülich, co-supervised by Prof. Fabian Natterer from the University of Zurich. Copyright: danyang.liu@physik.uzh.ch