Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
2,107
datasets available to search
ShareScore release 0.9.0
Dataset results
2,107 results for “margin;”
Input data for predicted sediment accumulation rates on the Norwegian continental margin
<p>Input data relating to R workflow for predicting sediment accumulation rates on the Norwegian continental margin (https://github.com/diesing-ngu/SedRates). The following files are included:</p><p><strong>norway_sar_2023-08-28.csv</strong> - Data on sediment accumulation rates from the <a href="https://doi.org/10.5194/essd-15-4105-2023">MOSAIC v2.0</a> database</p><p><strong>predictors_ngb.tif </strong>- Multi-band georeferenced TIFF-file of predictor variables</p><p><strong>predictors_description</strong>.txt - Information on variables stored in predictor_ngb.tif including units, statistics, time period and sources.</p><p><strong>GrainSizeReg_folk8_classes_2023-06-28.tif</strong> - Georeferenced TIFF-file of predicted substrate classes. Used to update the area of interest (exclude areas mapped as Rock and boulders).</p><p><strong>mud_2023-06-30.tif </strong>- Georeferenced TIFF-file of predicted mud content. Used as an additional predctor.</p><p><strong>SedEnv3_probabilities_2023-07-01.tif </strong>- Georeferenced Tiff-file of the prediction probabilities of the predicted sedimentary environments. Used as additional predctors.</p><p><strong>SedEnv3_max_probabilities_2023-07-01.tif </strong>- Georeferenced Tiff-file of the maximum probabilities, i.e., the probability of the class that was mapped.</p><p><strong>SedEnv3_AOA_2023-07-01.tif </strong>- Georeferenced Tiff-file of the area of applicability of the model <a href="https://doi.org/10.1111/2041-210X.13650">(Meyer & Pebesma, 2021)</a></p>
Input data for predicted dry bulk densities on the Norwegian continental margin
<p>Input data relating to R workflow for predicting dry bulk densities on the Norwegian continental margin (https://github.com/diesing-ngu/DBD). The following files are included:</p><p><strong>DBD_2023-07-21.csv </strong>- Data on dry bulk densities in surface sediments</p><p><strong>predictors_ngb.tif </strong>- Multi-band georeferenced TIFF-file of predictor variables</p><p><strong>predictors_description</strong>.txt - Information on variables stored in predictor_ngb.tif including units, statistics, time period and sources.</p><p><strong>GrainSizeReg_folk8_classes_2023-06-28.tif</strong> - Georeferenced TIFF-file of predicted substrate classes. Used to update the area of interest (exclude areas mapped as Rock and boulders).</p><p><strong>GrainSizeReg_folk8_probabilities_2023-06-28.tif</strong> - Georeferenced TIFF-file of the prediction probabilities of the predicted substrate classes. Used as additional predctors.</p>
Input data for predicted substrate types on the Norwegian continental margin
<p>Input data relating to R workflow for predicting substrate types on the Norwegian continental margin (https://github.com/diesing-ngu/GrainSizeReg). The following files are included:</p><p><strong>AoI_Harris_mod </strong>- Polygon shapefile delimiting the area of interest</p><p><strong>GrainSize_4km_MaxCombArea_folk8_point_20230628</strong> - Point shapefile of the response data (substrate type). Note that these data points were derived from mapped products and are not sample points as such. </p><p><strong>predictors_ngb.tif </strong>- Multi-band georeferenced TIFF-file of predictor variables</p><p><strong>predictors_description</strong>.txt - Information on variables stored in predictor_ngb.tif including units, statistics, time period and sources.</p>
Predicted sediment accumulation rates on the Norwegian continental margin
<p>Output data relating to R workflow for predicting sediment accumulation rates on the Norwegian continental margin (https://github.com/diesing-ngu/SedRates). The following files are included:</p> <p><strong>SAR_median_2024-02-15.tif</strong> - Georeferenced Tiff-file of the oredicted sediment accumulation rates</p> <p><strong>SAR_PI90_2024-02-15.tif</strong> - Georeferenced Tiff-file of the 90% prediction interval. Can be used as a measure of uncertainty.</p> <p><strong>SAR_AOA_2024-02-15.tif </strong>- Georeferenced Tiff-file of the area of applicability of the model <a href="https://doi.org/10.1111/2041-210X.13650">(Meyer & Pebesma, 2021)</a></p> <p><strong>SAR_AOA_2024-02-15.shp</strong> - Same as above but as polygon shapefile</p>
Predicted sedimentary environments on the Norwegian continental margin
<p>Output data relating to R workflow for predicting substrate types on the Norwegian continental margin (https://github.com/diesing-ngu/SedEnv). The following files are included:</p><p><strong>SedEnv3_classes_2023-07-01.tif </strong>- Georeferenced Tiff-file of the predicted sedimentary environments</p><p><strong>SedEnv3_probabilities_2023-07-01.tif </strong>- Georeferenced Tiff-file of the prediction probabilities of the predicted sedimentary environments</p><p><strong>SedEnv3_max_probabilities_2023-07-01.tif </strong>- Georeferenced Tiff-file of the maximum probabilities, i.e., the probability of the class that was mapped. Can be used as an indicator of map confidence.</p><p><strong>SedEnv3_AOA_2023-07-01.tif </strong>- Georeferenced Tiff-file of the area of applicability of the model <a href="https://doi.org/10.1111/2041-210X.13650">(Meyer & Pebesma, 2021)</a></p><p><strong>SedEnv3_AOA_2023-07-01.shp</strong> - Same as above but as polygon shapefile</p>
Predicted dry bulk densities on the Norwegian continental margin
<p>Output data relating to R workflow for predicting dry bulk densities on the Norwegian continental margin (https://github.com/diesing-ngu/DBD). The following files are included:</p> <p><strong>DBD0-10cm_median_2024-02-16.tif</strong> - Georeferenced Tiff-file of the Predicted dry bulk densities in the upper 10 cm of the sediment column</p> <p><strong>DBD0-10cm_PI90_2024-02-16.tif</strong> - Georeferenced Tiff-file of the 90% prediction interval. Can be used as a measure of uncertainty.</p> <p><strong>DBD0-10cm_AOA_2024-02-16.tif </strong>- Georeferenced Tiff-file of the area of applicability of the model <a href="https://doi.org/10.1111/2041-210X.13650">(Meyer & Pebesma, 2021)</a></p> <p><strong>DBD0-10cm_AOA_2024-02-16.shp</strong> - Same as above but as polygon shapefile</p>
Input data for predicted organic carbon content on the Norwegian continental margin
<p>Input data relating to R workflow for predicting organic carbon content on the Norwegian continental margin (https://github.com/diesing-ngu/TOC). The following files are included:</p><p><strong>mosaic_2023-04-21.csv</strong> - Data on organic carbon content in surface sediments from the <a href="https://doi.org/10.5194/essd-15-4105-2023">MOSAIC v2.0</a> database</p><p><strong>predictors_ngb.tif </strong>- Multi-band georeferenced TIFF-file of predictor variables</p><p><strong>predictors_description</strong>.txt - Information on variables stored in predictor_ngb.tif including units, statistics, time period and sources.</p><p><strong>GrainSizeReg_folk8_classes_2023-06-28.tif</strong> - Georeferenced TIFF-file of predicted substrate classes. Used to update the area of interest (exclude areas mapped as Rock and boulders).</p><p><strong>mud_2023-06-30.tif </strong>- Georeferenced TIFF-file of predicted mud content. Used as an additional predctor.</p>
Input data for predicted mud content on the Norwegian continental margin
<p>Input data relating to R workflow for predicting mud content on the Norwegian continental margin (https://github.com/diesing-ngu/GSMgrids). The following files are included:</p><p><strong>gsm_data.csv</strong> - Data on gravel, sand and mud of surface sediments</p><p><strong>MGObsPkt_150_160_170.shp </strong>- Point shapefile of categorical samples</p><p><strong>predictors_ngb.tif </strong>- Multi-band georeferenced TIFF-file of predictor variables</p><p><strong>predictors_description</strong>.txt - Information on variables stored in predictor_ngb.tif including units, statistics, time period and sources.</p><p><strong>GrainSizeReg_folk8_classes_2023-06-28.tif</strong> - Georeferenced TIFF-file of predicted substrate classes. Used to update the area of interest (exclude areas mapped as Rock and boulders).</p><p><strong>GrainSizeReg_folk8_probabilities_2023-06-28.tif</strong> - Georeferenced TIFF-file of the prediction probabilities of the predicted substrate classes. Used as additional predctors.</p>
Predicted substrate types on the Norwegian continental margin
<p>Output data relating to R workflow for predicting substrate types on the Norwegian continental margin (https://github.com/diesing-ngu/GrainSizeReg). The following files are included:</p><p><strong>GrainSizeReg_folk8_classes_2023-06-28.tif </strong>- Georeferenced Tiff-file of the predicted substrate classes</p><p><strong>GrainSizeReg_folk8_probabilities_2023-06-28.tif </strong>- Georeferenced Tiff-file of the prediction probabilities of the predicted substrate classes</p><p><strong>GrainSizeReg_folk8_max_probabilities_2023-06-28.tif </strong>- Georeferenced Tiff-file of the maximum probabilities, i.e., the probability of the class that was mapped. Can be used as an indicator of map confidence.</p><p><strong>GrainSizeReg_folk8_AOA_2023-06-28.tif </strong>- Georeferenced Tiff-file of the area of applicability of the model <a href="https://doi.org/10.1111/2041-210X.13650">(Meyer & Pebesma, 2021)</a></p><p><strong>GrainSizeReg_folk8_AOA_2023-06-28.shp</strong> - Same as above but as polygon shapefile</p>
Data from: People's appreciation of colorful field margins in intensively used arable landscapes and the conservation of plants and invertebrates
<p>Sown field margins can improve the conservation of biodiversity in rural areas and can contribute to the aesthetics of rural landscapes, thereby potentially increasing public support for agri-environmental measures. However, these two functions do not necessarily coincide. This raises the question whether field margins that are appreciated for their contribution to landscape aesthetics also deliver on the conservation of biodiversity. We conducted choice experiments with different groups of citizens and collected biodiversity data in the Netherlands, to investigate if the number of colors and vegetation cover in field margins increased respondents' appreciation for them, and how these visual cues correlated with taxonomic diversity and abundance of plants and invertebrates in those field margins. Using manipulated photos, we also assessed whether the presence of colorful field margins in a range of different rural landscapes increased respondents' appreciation of those landscapes. Respondents preferred colorful margins with high vegetation cover and showed a preference for green rural landscapes with colorful field margins. The presence of colorful field margins increased landscape aesthetics most in the least appreciated landscapes. The number of colors correlated positively with the diversity of sown and spontaneous plant species, and overall invertebrate abundance and abundance of predatory invertebrates, but was not related to invertebrate diversity. Our results show for the first time that colorful field margins support both public appreciation and diversity of plants and abundance of ground-dwelling invertebrates, with potential advantages to farmers in terms of natural pest control, at least in intensively used agricultural landscapes. However, management practices to maintain a high number of colors over time may be detrimental for invertebrate diversity. To optimize the different functions, we recommend that field margin layouts should consist of a perennial part that is allowed to develop over time, in combination with a part that is managed for its colorfulness.</p>
Strain partitioning, transfer and implications for ongoing processes of the intra-continental graben formation in the NW margin of the Ordos block, China: insights from densified GNSS measurements
<p><strong><span>GNSS data and processing</span></strong></p> <p><strong><span>1. Intensive observations</span></strong></p> <p><span>In this study, in addition to GNSS data surveyed around the Ordos block provided by <span>Hao et al. (2021)</span>, we collected GNSS data surveyed west of 103°E from </span><span>Phases</span><span> I and II of <span>the Crustal Movement Observation Network of China (CMONOC)</span></span><span>, which</span><span> has conducted measurements every one or two years from 1999 to 2019.</span><span> In particular</span><span>, we also collected data from 70 GNSS sites </span><span>deployed by<span> <a name="OLE_LINK49"></a><a name="OLE_LINK50"></a><a name="OLE_LINK82"></a><span><span>the National Geodetic Control Network of China (NGCNC)</span></span>. These sites were distributed in the northwestern boundary of the Ordos block and the interior of the Alashan and Ejina blocks and </span></span><span>first</span><span> measured 72 hours of data in 2014 or 2015. From September to October 2021, we conducted the second set of measurements for these GNSS sites of 96 continuous hours.</span></p> <p><span> </span></p> <p><strong><span>2. Data processing</span></strong></p> <p><span>T</span><span>he GAMIT </span><span>package </span><span>(</span><span>Herring et al., 2015</span><span>a</span><span>) </span><span>was employed </span><span>to process</span><span> the </span><span>double-differenced carrier phase observations</span><span>, and daily </span><span>loosely constrained solutions</span><span> were obtained</span><span>. </span><span>The primary geophysical models and parameters used in the processing, such as</span><span> the </span><span>FES2004 tidal loading model, GPT2 global pressure and temperature model (</span><span>Lagler et al., 2013</span><span>), and VMF1 </span><span>mapping</span><span> function (</span><span>Boehm et al., 2006</span><span>),</span><span> are</span><span> the</span><span> same </span><span>as </span><span>those in </span><span>Hao et al. (2021)</span><span>. Instead of incorporating global daily solutions provided by the IGS analysis centers, we also processed data from ~</span><span>70 evenly distributed global International Terrestrial Reference Frame (ITRF) core tracking </span><span>GNSS sites</span><span> using the GAMIT package with the same models. </span><span>We estimated</span><span> daily coordinates and uncertainties by constraining the daily regional solutions with global solutions, and the daily free network solutions were transformed into </span><span>the </span><span>ITRF2014 (</span><span>Altamimi et al., 2017</span><span>) </span><span>reference frame by utilizing</span><span> the GLOBK package</span><span> (</span><span>Herring et al., 2015</span><span>b</span><span>)</span><span>.</span><span> For campaign GNSS sites, the weighted least-squares adjustment was used to solve the linear velocity with respect to the ITRF2014.</span></p> <p><span>To analyze the differential crustal motion between blocks, we selected the stable Ordos block as the regional reference frame and transformed the GNSS velocity field referenced to the ITRF2014 into the Ordos-fixed frame through Euler rotation. The absolute Euler pole of the Ordos block is located at 75.083°N ± 0.850°</span><span> and</span><span> 139.692°W ± 3.701°, with an angular rotation rate of 0.352 ± 0.</span><span>006°/Ma </span><span>(</span><span>Hao et al., 2021</span><span>).</span></p> <p><span> </span></p> <p><strong><span>References</span></strong></p> <p><span>Altamimi, Z., Métivier, L, Rebischung, P., Rouby, H., Collilieux, X., 2017. ITRF2014 plate motion model.Geophys. J. Int. 209:1906–1912</span></p> <p><span>Boehm, J., Werl, B., and Schuh, H. (2006). Troposphere mapping functions for GPS and very long baseline interferometry from European Centre for Medium-Range Weather Forecasts operational analysis data. Journal of Geophysical Research, 111, B0240</span><span><a href="https://doi.org/10.1029/2005JB003629"><span>. doi:10.1029/2005JB003629</span></a></span><span>.</span></p> <p><span>Hao, M., Wang, Q., Zhang, P., Li, Z., Li, Y., Zhuang, W., 2021.“Frame wobbling” causing crustal deformation around the Ordos block. Geophysical Research Letters 48, e2020GL091008. https://doi.org/10.1029/2020GL091008.</span></p> <p><span>Herring, T.A., King, R.W., McClusky, S.C., 2015b.GAMIT reference manual, global Kalman filter VLBI and GPS analysis program, Release 10.6.Massachusetts Institute of Technology, Cambridge.</span></p> <p><span>Herring, T.A., King, R.W.,McClusky, S.C., 2015a.GAMIT reference manual, GPS analysis at MIT, Release 10.6.Massachusetts Institute of Technology, Cambridge.</span></p> <p><span>Lagler, K., Schindelegger, M., Böhm, J., Kr</span><span>á</span><span>sn</span><span>á</span><span>, H., and Nilsson, T. (2013). GPT2: empirical slant delay model for radio space geodetic techniques. GeophysicalResearchLetters, 40, 1069-1073. doi: 10.1002/grl.50288.</span></p>
FIGURE 10. Posterior carapace margin. A in On the genus Lyphira Galil, 2009 (Crustacea: Brachyura: Leucosiidae), with descriptions of four new species from the West Pacific
FIGURE 10. Posterior carapace margin. A, Lyphira heterograna (Ortmann, 1892), lectotype male (12.4 × 12.2 mm) (MZS Cru 3191), Japan; B, L. heterograna (Ortmann, 1892), male (16.5 × 16.9 mm) (KPM NH 0130526), Japan; C, L. acutidens (Chen, 1987), male (9.3 × 9.1 mm) (ZRC 2002.0504), Yellow Sea; D, L. chomel n. sp., holotype male (10.1 × 10.3 mm) (ZRC 1999.9798), Malaysia; E, L. ngankee n. sp., holotype male (20.8 × 21.8 mm) (ZRC 1999.0456), Nanao, China; F, L. linda n. sp., holotype male (18.4 × 18.2 mm) (ZRC 1981.9.2.7), Singapore; G, L. linda n. sp., male (18.9 × 18.7 mm) (ZRC 1999.0507), Singapore; H, L. linda n. sp., male (18.1 × 18.5 mm) (ZRC 2011.0646), Thailand; I, L. bellagrana n. sp., holotype male (15.7 × 15.0 mm) (MZB Cru 5627), Papua, Indonesia; J, L. bellagrana n. sp., male (12.5 × 11.8 mm) (ZRC 2022.1031), Papua, Indonesia.
Annex G – Calculation of the combined margin of exposure (MOET)
<p><span>This Annex contains the calculations of the combined margin of exposure (MOET) for the risk characterisation of PBDEs related to the Update of the risk assessment of polybrominated diphenyl ethers (PBDEs) in food. </span></p>
FIGURE 1 in Inventorizing the modern benthic foraminiferal assemblage from marginal marine environments across the North West coast of Bay of Bengal
FIGURE 1. LOcATION OF THE SAMPLING STATIONS wITH RESPEcT TO EAcH HAbITAT. CO-ORdINATES OF THE SAMPLEd STATIONS HAVE bEEN dETAILEd IN SUPPLEMENTARy TAbLE S1.
Enhanced firn densification in high-accumulation shear margins of the NE Greenland Ice Stream
<p>Seismic data in support of the publication "Enhanced firn densification in high-accumulation shear margins of the NE Greenland Ice Stream" in the Journal of Geophysical Research. Details in README.txt</p>
FIGURE 2. Mantle margins. A in Glossing over cryptic species: Descriptions of four new species of Glossodoris and three new species of Doriprismatica (Nudibranchia: Chromodorididae)
FIGURE 2. Mantle margins. A. Glossodoris buko sp. nov., CASIZ-222107. B. Glossodoris pallida (Cuvier, 1804), CASIZ- 175554. C. Glossodoris bowanga sp. nov., CASIZ-194204, photo by Marina Poddubetskaia. D. Glossodoris andersonae sp. nov., CASIZ-192288. E. Glossodoris acosti sp. nov., CASIZ 191352, photo by Vanessa Knutson. F. Glossodoris sp. cf. cincta, CASIZ-199204. G. Glossodoris cincta Bergh, 1888, from Reunion, photo only, photo by Philibert Bidgrain. H. Doriprismatica balut sp. nov., CASIZ-182787. I. Doriprismatica rossi sp. nov. CASIZ-192281. J. Doriprismatica marinae sp. nov. CASIZ- 194050, Marina Poddubetskaia, all other photos by. T. Gosliner.
FIGURE 2. Reticulammina novaezelandica Tendal, 1972 in First record of a Xenophyophore (Rhizaria: Foraminifera) on the Chilean margin
FIGURE 2. Reticulammina novaezelandica Tendal, 1972 (MNHNCL PRO-15000). Details of the specimen: a, SEM view of lamellae and open spaces forming the reticulated structure. b, overview of agglutinated material. c, detail of agglutinated material showing foraminiferan and radiolarian tests and sponge spicule fragments. d, foraminiferan test. e–f, radiolarian tests; some scattered stercomata are visible in e.
FIGURE 1. Reticulammina novaezelandica Tendal, 1972 in First record of a Xenophyophore (Rhizaria: Foraminifera) on the Chilean margin
FIGURE 1. Reticulammina novaezelandica Tendal, 1972 (MNHNCL PRO-15000). a–d, four views of entire specimen. e, detail showing the abraded surfaces of the lamellae separated by open spaces. f, specimen attached to rock. Scale is 10 mm for a–d and f, and 2 mm for e.
Data for paper: Segmented Thrust Faulting: Example from the Northeastern Margin of the Tibetan Plateau
<p>If additional data is needed, please contact me by E-mail: skatetangshan@vip.126.com</p>
Dataset: Forced subduction at passive margin
<p>This dataset contains simulation results used for visualizing Figures in <br> “Forced subduction initiation at passive continental margins: velocity-driven versus stress-driven”</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.