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.
12,871
datasets available to search
ShareScore release 0.7.1
Dataset results
12,871 results for “aged”
Age and Size of Smith Firs at Treeline in Tibet 1700-2013
The most widespread response to global warming among alpine treeline ecotones is not an upward shift, but an increase in tree density. However, the impact of increasing density on interactions among trees at treeline is not well understood. Here, we test if treeline densification induced by climatic warming leads to increasing intraspecific competition. We mapped and measured the size and age of Smith fir trees growing in two treelines located in the southeastern Tibetan Plateau. We used spatial point-pattern and codispersion analyses to describe the spatial association and covariation among seedlings, juveniles, and adults grouped in 30-year age classes from the 1860s to the present. Effects of competition on tree height and regeneration were inferred from bivariate mark-correlations. Since the 1950s, a rapid densification occurred at both sites in response to climatic warming. Competition between adults and juveniles or seedlings at small scales intensified as density increased. Encroachment negatively affected height growth and further reduced recruitment around mature trees. We infer that tree recruitment at the study treelines was more cold-limited prior to 1950 and shifted to a less temperature-constrained regime in response to climatic warming. Therefore, the ongoing densification and encroachment of alpine treelines could alter the way climate drives their transitions towards subalpine forests.
Age of Nonstructural Carbohydrates in Four Tree Species at Harvard Forest 2015
We estimated the mean age of sugars within and between different organs of four temperate tree species using the radiocarbon (carbon-14) bomb spike approach. Radial patterns of carbon-14 in the stemwood and coarse roots showed that sugars tended to became older when moving towards the pith.
Influence of Little Ice Age on New England Vegetation from 2000 BP to Present
This multi-proxy study uses paleoecological, paleolimnological, and historical approaches to reconstruct climate, vegetation, and cultural dynamics over the past 1500 years at sites arrayed across the climatic and forest gradients of New England and to place these results in a regional framework through analysis of pollen records from the North American Pollen Database. High resolution records were obtained using pollen to interpret vegetation history; chironomids, stable isotopes, geochemistry, and diatoms, supplemented by historical reconstructions, to interpret climate history; charcoal and land-use data to document the human impacts; and Pb-210 and C-14 for chronological control. Results will provide: (1) an objective characterization of the Little Ice Age and climate history in New England, (2) comparison of pre- and post-European forest dynamics in relationship to independent environmental and land-use histories, (3) a reexamination of historical vegetation dynamics in light of prior climate an vegetation change, and (4) widespread availability of data and results through publications, symposium presentation, and the Harvard Forest Archives and web pages.
Concentration and Age of Nonstructural Carbon Reserves in Two Trees at Harvard Forest 2012
We know surprisingly little about whole-tree nonstructural carbon (NSC; primarily sugars and starch) budgets. Even less well understood is the mixing between recent photosynthetic assimilates (new NSC) and previously stored reserves. And, NSC turnover times are poorly constrained. We characterized the distribution of NSC in the stemwood, branches, and roots of two temperate trees, and we used the continuous label offered by the radiocarbon (14C) bomb spike to estimate the mean age of NSC in different tissues. NSC in branches and outermost stemwood growth rings had the 14C signature of the current growing season. However, NSC in older above- and below-ground tissues was enriched in 14C, indicating that it was produced from older assimilates. Radial patterns of 14C in stemwood NSC showed strong mixing of NSC across the youngest growth rings, with limited.
Dataset to Schiedung et al. (2024): Millennial-aged pyrogenic carbon in high-latitude mineral soils
<p>Dataset to Schiedung et al. (2024, Communications Earth & Environment): Pyrogenic Carbon is Aged at Millennial Scale in High-Latitude Mineral Soils</p> <p>DOI: <a href="https://doi.org/10.1038/s43247-024-01343-5">10.1038/s43247-024-01343-5</a></p> <p>This repository includes the following files: </p> <p><strong><em>dd_all.csv</em> </strong>- Includes all data for the individual samples that are presented in the manuscript.</p> <p><strong><em>Var_names_dd_all.csv</em> </strong>- Describes all variables in <em>dd_all</em> with corresponding unit </p> <p><strong><em>dd_site_average.csv</em></strong> - Includes all data that has been determined on composite samples for each site or the average of all samples per site </p> <p><strong><em>Var_names_dd_site_average.csv</em></strong> - Describes all variables in <em>dd_site_average.csv</em> with corresponding unit</p> <p>All .csv use "," as separator. </p> <p>This data set is also connected to Schiedung et al. (2022, Catena <a href="https://doi.org/10.1016/j.catena.2022.106194"> https://doi.org/10.1016/j.catena.2022.106194</a> ) and the corresponding repository: <a href="../records/10609291">https://zenodo.org/records/10609291</a></p>
Hoedjiespunt Middle Stone Age Dataset, Western Cape, South Africa
<p>This Middle Stone Age archaeological dataset from Hoedjiespunt 1 was collected in 2011 by a team from the Department of Early Prehistory and Quaternary Ecology of the University of Tübingen (Germany) headed by Nicholas J. Conard. South African and European researchers collaborated on this project, with John E. Parkington, Katherine Kyriacou, Deano Stynder, Graham Avery, and Chantal Tribolo making substantial contributions. The site is located within the property of Transnet National Ports Authority in the municipality of Saldanha, Western Cape, South Africa.</p> <p>The locality of Hoedjiespunt 1 was well known as a paleontological site since at least the 1990s, when the site yielded several important Middle Pleistocene hominin remains dated between 200,000 and 350,000 years. The paleontological site also yielded a well preserved assemblage of fauna, including terrestrial and marine mammals, shellfish and ostrich eggshell. The excavators interpreted the accumulation of these finds as the remains of a hyena den. Cultural remains such as lithic artifacts were absent from the paleontological site, which is situated immediately below the archaeological site.</p> <p>The 2011 field work at the archaeological site of Hoedjiespunt 1 took place with the help of students from the universities of Tübingen and Cape Town. The datasets are predominantly in English (with some parts in German) and include field data in the MAIN table. Further analytical data for several classes of artifacts include: LITHICS, FAUNA, OCHRE, and BUCKETS.</p> <p>All of the archaeological materials collected in 2011 are curated by the Department of Archaeology of the University of Cape Town in Rondebosch, South Africa. Funding for this research came mainly from the Heidelberg Academy of Sciences and Humanities and the University of Tübingen. Significant support was provided by the Department of Archaeology of the University of Cape Town and the Iziko South African Museums.</p> <p> </p> <p>Importnat references for the paleontological excavations are listed here, while the main publications associated with the 2011 excavations are presented below in the reference section: </p> <p>Berger, L.R. & Parkington, J.E. (1995). A new Pleistocene hominid-bearing locality at Hoedjiespunt, South Africa. American Journal of Physical Anthropology 98: 601-609. <a href="https://doi.org/10.1002/ajpa.1330980415">https://doi.org/10.1002/ajpa.1330980415</a></p> <p>Churchill, S.E., Berger, L.E. & Parkington, J.E. (2000). A Middle Pleistocene human tibia from Hoedjiespunt, Western Cape, South Africa. South African Journal of Science 96: 367-368. <a href="https://hdl.handle.net/10520/AJA00382353_8943">https://hdl.handle.net/10520/AJA00382353_8943</a> </p> <p>Stynder, D.D., Moggi-Cecchi, J. Berger, R.L. & Parkington, J.E. (2001). Human mandibular incisors from the late Middle Pleistocene locality of Hoedjiespunt 1, South Africa. Journal of Human Evolution 41: 369-383. <a href="https://doi.org/10.1006/jhev.2001.0488">https://doi.org/10.1006/jhev.2001.0488</a></p>
Graphs of data for elderly individuals (aged 60 to 120 years) with syphilis in Brazil
<p>A set of data graphs containing informations on elderly people with syphilis in Brazil, aged between 60-120 years with syphilis in Brazil, aged between 60-120 years and contains spreadsheet results of trend analysis of acquired syphilis, by regions of Brazil, in the period 2010-2020, referring to the article entitled "<strong>ACQUIRED SYPHILIS IN OLDER PEOPLE IN BRAZIL FROM 2010-2020".<br><br><br></strong>The dataset used to plot the graphs can be found at: <a href="https://doi.org/10.5281/zenodo.10086131">https://doi.org/10.5281/zenodo.10086131</a></p>
Data for "Profiling the transcriptomic age of single-cells in humans"
<p>This is a supplementary data for the article titled "Profiling transcriptomic age of human single-cells". Data created in this project is shared here for the scientific community. </p> <p>Here we used available scRNA-seq data of 1,058,909 blood cells of 508 healthy, human donors, for developing cell-type-specific single-cell transcriptomic clocks and predicting the age of human blood cells. We also applied our clocks to different external datasets and evaluated the age of single cells originated from COVID-19 patients and human embryos.</p> <p>For the description of the content of the dataset see the ReadMe file.</p>
Early EASE-GRID Sea Ice Age, 1978-1983
<p>Early spin-up period Arctic sea ice age data for 1978 through 1983. This product augments the NSIDC sea ice age product: "EASE-Grid Sea Ice Age, Version 4.1" (Tschudi et al., 2019a), which begins in January 1984. See the main product website for complete documentation. The age is estimated via Lagrangrian tracking based on the NSIDC sea ice motion product (Tschudi et al., 2019b), whose source data is primarily passive microwave brightness temperatures and drifting buoys. Age is estimated weekly as annual age categories. Values are: 1 for "first-year ice", ice that is 0-1 years old, and so on for older ice. The ice is "aged" once each year during the week of the annual sea ice minimum extent, generally sometime in September. </p> <p>In this product, the initialization of the field begins with the first available data in late-October 1978. For the existing ice at that time, age is initialized at the start of the product with age=1. The first week of the data, because it is after the minimum, the age of existing ice is augmented to age=2 and new ice is given age=1. So, the first field in 1978 has only two age categories of 1 (0-1 years old) or 2 (1-2 years old) and this continues through 1978. This means that the age of the ice that formed between the minimum in September and the beginning of the data in late-October 1978 is overestimated by one year. In subsequent years, the oldest ice category will continue to overestimate some of the ice pack until that initial ice either: (1) melts, (2) is transported out of the Arctic, or (3) reaches the maximum age in the product (16 years).<br> <br> Much of the the existing ice in 1978 may be older than 1-2 years old as ice may stay in the Arctic for 5 or more years, but the data availability and the Lagrangian methodology cannot give a specific until the product is fully "spun up". For each subsequent year, a one-year older age category is added in the week of each year's extent minimum. Note that due to the assumption made at the beginning of the product in 1978, the oldest ice category may overestimate the true age of some parcels by one year. </p> <p>Tschudi, M., W. N. Meier, J. S. Stewart, C. Fowler, and J. Maslanik. (2019a). EASE-Grid Sea Ice Age, Version 4 [Data Set]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/UTAV7490FEPB. Date Accessed 02-20-2023.</p> <p>Tschudi, M., W. N. Meier, J. S. Stewart, C. Fowler, and J. Maslanik. (2019b). Polar Pathfinder Daily 25 km EASE-Grid Sea Ice Motion Vectors, Version 4 [Data Set]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/INAWUWO7QH7B.</p>
Large SEM-BSE images of hydrated alite of ages from 1 day up to 1 year
<p>This dataset contains 8-Bit SEM-BSE images of commercially available tricalcium silicate (C<sub>3</sub>S; alite, MIII polymorph; Vustah, Czech Republic). The alite was mixed with a water/binder ratio of 0.5. The paste was the cast in small sealed containers, which were submersed with water. The specimens were stored at 22 ± 2°C.</p> <p>After the desired hydration times (1, 7, 14, 28, 84, 365 days) the hydration was stopped by immersing the prisms in isopropanol and drying them at 60°C for 12 hours. The dried prisms were then embedded in low viscosity epoxy resin and mechanically polished using diamond paste with a grain size down to 0.25 µm. Finally, the specimens were coatet with a thin layer of carbon to avoid charging.</p> <p>The images were acquired at 10 kV (7 days, smaller image), 12 kV (7 - 365 days) and 15 kV (1 days) using a CBS (concentric backscatter, 14-365 days) and a ABS (1 and 7 days) detector within a Thermofischer Helios G4 UX.</p> <p><strong>Table 1</strong>: Basic information like resolution, size and phase composition of the images.</p> <table> <tbody> <tr> <td><strong>file</strong></td> <td><strong>age</strong></td> <td><strong>size</strong></td> <td><strong>size</strong></td> <td><strong>area</strong></td> <td><strong>pores</strong></td> <td><strong>hydrates</strong></td> <td><strong>clinker</strong></td> </tr> <tr> <td> </td> <td>in days</td> <td>in px</td> <td>in µm</td> <td>in mm²</td> <td>area-%</td> <td>area-%</td> <td>area-%</td> </tr> <tr> <td>C3S 1d.tif</td> <td>1</td> <td>21179 x 21495</td> <td>749.9 x 749.9</td> <td>0.56</td> <td>38.5</td> <td>38</td> <td>23.9</td> </tr> <tr> <td>C3S 7d.tif</td> <td>7</td> <td>19433 x 19320</td> <td>390.9 x 390.9</td> <td>0.15</td> <td>32.2</td> <td>48.5</td> <td>19.4</td> </tr> <tr> <td>C3S 7d_2.tif</td> <td>7</td> <td>36864 x 36864</td> <td>1554.0 x 1554.0</td> <td>2.41</td> <td>28.5</td> <td>52.8</td> <td>19.1</td> </tr> <tr> <td>C3S 14d.tif</td> <td>14</td> <td>36864 x 36864</td> <td>1554.0 x 1554.0</td> <td>2.41</td> <td>22.2</td> <td>62.7</td> <td>15.4</td> </tr> <tr> <td>C3S 28d.tif</td> <td>28</td> <td>36864 x 36864</td> <td>1554.0 x 1554.0</td> <td>2.41</td> <td>16.9</td> <td>74.9</td> <td>8.3</td> </tr> <tr> <td>C3S 84d.tif</td> <td>84</td> <td>36864 x 36864</td> <td>1554.0 x 1554.0</td> <td>2.41</td> <td>21.7</td> <td>72.7</td> <td>5.7</td> </tr> <tr> <td>C3S 365d.tif</td> <td>365</td> <td>36864 x 36864</td> <td>1554.0 x 1554.0</td> <td>2.41</td> <td>14.8</td> <td>83.2</td> <td>2.0</td> </tr> </tbody> </table> <p>The proportions of pores, hydrates and unhydrated clinker shown in Table 1 are the result of manual thresholding of denoised versions of these images and may therefore differ to own measurements.</p> <p>The scaling is backed into the file and can be read using ImageJ/Fiji.</p> <p>The unstitched files are provded as 7z archives. The sub-images were arranged in a 10 x 10 grid, with the exception of the 7 days image, which was arranged in a 9x9 grid. The pixel scaling of these files is the same as in the larger files. The unstitched files for the 1 day specimen can be provided on request.</p> <p><strong>Internal note</strong></p> <p>These files are included in the following MAPS datasets:</p> <ul> <li>2019_04_15 FK C3S 1d</li> <li>2019_04_23 C3S 7d 15 BIB</li> <li>2023_05_24 C32-C2S 14-84 d</li> <li>2023_06_08 C2S-C3S 28d-1year</li> <li>2023_07_18 C3S 7d, C2S 1d, 7d, 3C3S-1C2S 7d</li> </ul> <p><strong>Changelog</strong></p> <ul> <li>2023-08-03, V1.1 Added new dataset (C3S 7d_2.tif).</li> <li>2024-02-07, V1.1 modified description (error in hydrate/C<sub>3</sub>S measurement for the 14 days dataset)</li> </ul>
Weight, sex, age, beam diameter, antler points and teat length for harvested deer from 1984-2025 in Black Rock Forest, Cornwall, NY.
Data from white-tailed deer harvested within Black Rock Forest, Cornwall, New York are collected annually. Trained staff measure mass, antler beam diameter, and teat length (since 2010), estimate age via dentition, count antler points, and assess sex on all field-dressed deer. Heart girth, measured as chest circumference, was recorded from 1984 to 1998.
Emotion regulation in the Ageing Brain, University of Reading, BBSRC
Open the record for dataset details and reuse information.
Protecting the Aging Brain, Case-Study
Open the record for dataset details and reuse information.
Synthesized anthropometric data for the German working-age population
<p>The anthropometric datasets presented here are virtual datasets. The unweighted virtual dataset was generated using a synthesis and subsequent validation algorithm (Ackermann et al., 2023). The underlying original dataset used in the algorithm was collected within a regional epidemiological public health study in northeastern Germany (SHIP, see Völzke et al., 2022). Important details regarding the collection of the anthropometric dataset within SHIP (e.g. sampling strategy, measurement methodology & quality assurance process) are discussed extensively in the study by Bonin et al. (2022).</p><p>To approximate nationally representative values for the German working-age population, the virtual dataset was weighted with reference data from the first survey wave of the Study on health of adults in Germany (DEGS1, see Scheidt-Nave et al., 2012). Two different algorithms were used for the weighting procedure: (1) iterative proportional fitting (IPF), which is described in more detail in the publication by Bonin et al. (2022), and (2) a nearest neighbor approach (1NN), which is presented in the study by Kumar and Parkinson (2018). Weighting coefficients were calculated for both algorithms and it is left to the practitioner which coefficients are used in practice. Therefore, the weighted virtual dataset has two additional columns containing the calculated weighting coefficients with IPF ("WeightCoef_IPF") or 1NN ("WeightCoef_1NN"). Unfortunately, due to the sparse data basis at the distribution edges of SHIP compared to DEGS1, values underneath the 5th and above the 95th percentile should be considered with caution.</p><p>In addition, the following characteristics describe the weighted and unweighted virtual datasets: According to ISO 15535, values for "BMI" are in [kg/m2], values for "Body mass" are in [kg], and values for all other measures are in [mm]. Anthropometric measures correspond to measures defined in ISO 7250-1. Offset values were calculated for seven anthropometric measures because there were systematic differences in the measurement methodology between SHIP and ISO 7250-1 regarding the definition of two bony landmarks: the acromion and the olecranon. Since these seven measures rely on one of these bony landmarks, and it was not possible to modify the SHIP methodology regarding landmark definitions, offsets had to be calculated to obtain ISO-compliant values. In the presented datasets, two columns exist for these seven measures. One column contains the measured values with the landmarking definitions from SHIP, and the other column (marked with the suffix "_offs") contains the calculated ISO-compliant values (for more information concerning the offset values see Bonin et al., 2022). The sample size is N = 5000 for the male and female subsets. The original SHIP dataset has a sample size of N = 1152 (women) and N = 1161 (men). Due to this discrepancy between the original SHIP dataset and the virtual datasets, users may get a false sense of comfort when using the virtual data, which should be mentioned at this point. In order to get the best possible representation of the original dataset, a virtual sample size of N = 5000 is advantageous and has been confirmed in pre-tests with varying sample sizes, but it must be kept in mind that the statistical properties of the virtual data are based on an original dataset with a much smaller sample size.</p>
Multi-faceted analyses of Poland's Bronze and Early Iron Age hoards: Fig.5. Pottery (A, C), animal bones (B), a human skull (C, D), and a flint tool (D) excavated from underneath the stone layer in Kaliszany (archaeological site no. 3)
<p>The set contains a figure, with with photographs that show examples of finds discovered during excavations at archaeological site 3 in Kaliszany, Wągrowiec commune, Poland. It is a stone and earth structure in which a hoard of metal objects dating to the Late Bronze Age was discovered in 1943. The photo is from the 2022 survey, when the south-western part of the structure was explored. <br><br>The paper and data were prepared as part of a project funded by the National Science Centre, Poland: <em>A Biography of Late Bronze and Early Iron Ages Hoards. A Multi-Faceted Analysis of Metal Objects Related to Monumental Constructions in Poland</em> (UMO-2021/41/B/HS3/00038)</p>
Multi-faceted analyses of Poland's Bronze and Early Iron Age hoards: Fig.1. Location of hoards mentioned in the text: white dots represent locations of hoards examined in the Biography of Hoards project; black dots represent locations of hoards examined in other multi-faceted projects
<p>The set contains a figure, with data, on the location of the hoards included (described in the related paper).<br><br>The paper and data were prepared as part of a project funded by the National Science Centre, Poland: <em>A Biography of Late Bronze and Early Iron Ages Hoards. A Multi-Faceted Analysis of Metal Objects Related to Monumental Constructions in Poland</em> (UMO-2021/41/B/HS3/00038)</p>
Multi-faceted analyses of Poland's Bronze and Early Iron Age hoards: Fig.3. Workflow in the Biography of Hoards project
<p>The set contains a figure and editable files associated with the figure.</p> <p>Figure presenting workflow of the project described in the related paper.</p> <p>The paper and data were prepared as part of a project funded by the National Science Centre, Poland: <em>A Biography of Late Bronze and Early Iron Ages Hoards. A Multi-Faceted Analysis of Metal Objects Related to Monumental Constructions in Poland</em> (UMO-2021/41/B/HS3/00038)</p>
Martian crater ages and crater counting - Does the impact flux of small and large asteroids varied through time on Mars, the Earth and the Moon?
<ul> <li>The SM_mars_crater_dating.xlsx table contains all the information used to date the 49 martian impact craters considered in this study (< 600 Ma). </li> </ul> <ol> <li>CRATER ID </li> <li>CRATER NAME</li> <li>DIAM KM </li> <li>LAT </li> <li>LONG </li> <li>DEPTH RIM KM </li> <li>DEPTH SURF KM </li> <li>DEPTH FLOOR KM </li> <li>NUMBER LAYER</li> <li>MORPHO EJECTA </li> <li>PRESERVATION </li> <li>COUNT AREA KM2: counting area from ejecta banket mapping </li> <li>COUNT AREA ASCI* KM2: counting area after removal of surfaces contaminated by secondary craters </li> <li>THRESHOLD AREA KM2: minimum size of Voronoi polygon area below which all associated detected craters are considered of secondary origin</li> <li>NB SEC: number of secondary craters dentified by ASCI </li> <li>PERCENT SEC</li> <li>NB CRAT 100M: total number of craters > 100 m detected by the CDA** on the CTX global mosaic*** over the counting area</li> <li>NB PRIM 100M: number of craters identified as primaries by ASCI</li> <li>TURNOFF DIAM KM: minimum crater diameter used to fit the crater-size frequency distribution (CSFD) with an isochron</li> <li>NB CRAT FIT: number of craters used to fit the CSFD with an isochron</li> <li>AGE GA: model age based on Hartmann (2005) chronology model**** and Michael et al. (2016) fitting technique*****</li> <li>AGE MAX GA</li> <li>AGE MIN GA</li> <li>N(1): equivalent number of accumulated craters >1km per km2</li> <li>N(1) MAX</li> <li>N(1) MIN</li> </ol> <p>*ASCI: Automatic Secondary Crater Identification: A. Lagain, K. Servis, G. K. Benedix, C. Norman, S. Anderson, P. A. Bland, Model Age Derivation of Large Martian Impact Craters, Using Automatic Crater Counting Methods, Earth and Space Science 8 (2) (2021). doi:10.1029/2020EA001598.</p> <p>**CDA: Crater Detection Algorithm: G. K. Benedix, A. Lagain, K. Chai, S. Meka, S. Anderson, C. Norman, P. A. Bland, J. Paxman, M. C. Towner, T. Tan, Deriving Surface Ages on Mars Using Automated Crater Counting, Earth and Space Science 7 (3) (2020). doi:10.1029/2019EA001005.</p> <p>*** CTX global mosaic: Context Camera global mosaic: J. L. Dickson, L. A. Kerber, C. I. Fassett, B. L. Ehlmann, A Global, Blended CTX Mosaic of Mars with Vectorized Seam Mapping: A New Mosaicking Pipeline Using Principles of Non-Destructive Image Editing, in: Lunar and Planetary Science Conference (2018), p. 2480.</p> <p>**** W. K. Hartmann, Martian cratering 8: Isochron refinement and the chronology of Mars, Icarus 174 (2) (2005) 294–320. doi:10.1016/j.icarus.2004.11.023.</p> <p>***** G. G. Michael, T. Kneissl, A. Neesemann, Planetary surface dating from crater size-frequency distribution measurements: Poisson timing analysis, Icarus 277 (2016) 279–285. doi:10.1016/j.icarus.2016.05.019.</p> <ul> <li>The crater_counting.csv table contains the location and size of impact craters used to derive the ages of the 49 craters younger than 600 Ma old presented in this study. </li> </ul>
Genetic association analysis of anti-VEGF treatment response in neovascular age-related macular degeneration
<p>Summary statisics of an association study of 6,908,005 genetic variants with anti-VEGF nAMD treatment response in 179 treatment-naïve nAMD probands. This dataset supplements the publication "Genetic Association Analysis of Anti-VEGF Treatment Response in Neovascular Age-Related Macular Degeneration" (DOI: 10.3390/ijms23116094). Details regarding the methods and version numbers can be found in the corresponding manuscript.</p>
A Global Data Set of Present-Day Oceanic Crustal Age and Seafloor Spreading Parameters
<p>Datasets of present-day oceanic crustal age and seafloor spreading parameters from Seton et al. (2020).</p> <p>This dataset contains:</p> <ul> <li>Animations: animations of the present-day age grid and seafloor spreading parameters in both low and high resolution</li> <li>Feature Data: GPlates compatible files (*.gpml and *.rot) consistent with and used to create this dataset. Preferred magnetic anomaly picks are also included.</li> <li>Grids: Gridded datasets (netCDF-4 and netCDF-3) of present-day age, rate, asymmetry, direction, obliquity, confidence, and age misfit (in v1.1 only) in 6 minute resolution. Age grids are also provided in 1 and 2 minute resolution as netCDFs, and as 6 minute xyz files.</li> <li>Images: Images of the present-day age grid and seafloor spreading parameters</li> <li>Workflows: the latest workflow to create the present-day age grid can be found on GitHub: https://github.com/EarthByte/presentday-agegridding </li> </ul> <p>These files can also be downloaded from the EarthByte website <a href="https://earthbyte.org/webdav/ftp/earthbyte/agegrid/2020/">here</a>, and the global plate motion model can be found online <a href="https://www.earthbyte.org/webdav/ftp/Data_Collections/Muller_etal_ 2019_Tectonics">here</a>.</p> <p><strong>Please cite the dataset as:</strong><br> Seton, M., Müller, R. D., Zahirovic, S., Williams, S., Wright, N. M., Cannon, J., et al. (2020). A global data set of present‐day oceanic crustal age and seafloor spreading parameters. <em>Geochemistry, Geophysics, Geosystems</em>, 21, e2020GC009214. https://doi.org/10.1029/2020GC009214</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.