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.
363
datasets available to search
ShareScore release 0.9.0
Dataset results
363 results for “stack”
The best of two worlds: using stacked generalisation for integrating expert range maps in species distribution models
Open the record for dataset details and reuse information.
MPM-334-1: Fossil basicranium and endocranial volumes and CT-stack (Enantiornithes, Avialae)
Open the record for dataset details and reuse information.
Confocal image stacks of GFP expression in Drosophila forelegs driven by Gal4 driver expression in foreleg motor neurons
Open the record for dataset details and reuse information.
Stacking microscopy images of the pteropod Limacina bulimoides
Open the record for dataset details and reuse information.
Image stack, PLY-files and a NEX-file accompanying: A new symmoriiform from the Late Devonian of Morocco: novel jaw function in ancient sharks
Open the record for dataset details and reuse information.
Hubbard Brook site, stations Stack 1Stack 2, study of soil temperature at 8 cm depth (mean) in units of celsius on a monthly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Hubbard Brook (HBR) contains soil temperature at 8 cm depth (mean) measurements in celsius units and were aggregated to a monthly timescale.
Hubbard Brook site, stations Stack 1Stack 2, study of soil temperature at 8 cm depth (mean) in units of celsius on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Hubbard Brook (HBR) contains soil temperature at 8 cm depth (mean) measurements in celsius units and were aggregated to a yearly timescale.
Confocal microscopy image stacks from "Temporal integration of auxin information for the regulation of patterning"
<p>This dataset contains raw images in CZI format (Zeiss) of shoot apical meristems (SAM) from <em>Arabidopsis thaliana </em>transgenic lines <strong>qDII-pCLV3-pDR5</strong> or <strong>qDII-pCLV3-PIN1</strong>. See <em>(Galvan-Ampudia and Cerutti et al.) </em>for detailed information. This data constitutes the input of the <strong>sam_spaghetti</strong> pipeline (<a href="https://gitlab.inria.fr/mosaic/publications/sam_spaghetti">https://gitlab.inria.fr/mosaic/publications/sam_spaghetti</a>) and can be processed using the scripts and examples provided in the package.</p> <p> </p> <p><strong>File information:</strong></p> <p>File names containing qDII-CLV3-DR5 have the following data:</p> <ul> <li>Channel 1: <em>DII-VENUS-N7</em></li> <li>Channel 2: <em>pDR5:2xmTurquoise2</em></li> <li>Channel 3: <em>pRPS5a:TagBFP-SV40</em></li> <li>Channel 4: <em>pCLV3:mCherry-N7</em></li> </ul> <p>File names containing qDII-CLV3-PIN1-PI have the following data:</p> <ul> <li>Channel 1: <em>DII-VENUS-N7</em></li> <li>Channel 2: <em>pPIN1:PIN1-GFP</em></li> <li>Channel 3: <em>Propidium Iodide (cell walls)</em></li> <li>Channel 4: <em>pRPS5a:TagBFP-SV40</em></li> <li>Channel 5: <em>pCLV3:mCherry-N7</em></li> </ul> <p>Time-lapse sequences are identified as follows:</p> <ul> <li><strong>qDII-CLV3-DR5-E27-LD-SAM7.czi</strong></li> <li><strong>qDII-CLV3-DR5-E27-LD-SAM7-T5.czi</strong></li> <li><strong>qDII-CLV3-DR5-E27-LD-SAM7-T10.czi</strong></li> </ul> <p>where:</p> <ul> <li><strong>qDII-CLV3-DR5</strong> indicates the line</li> <li><strong>E$$-LD</strong> (e.g. E25-LD, E27-LD, etc) indicates independent biological replicas</li> <li><strong>SAM$</strong> is the meristem (technical replica)</li> <li><strong>T$</strong> indicates the time elapsed after the first image (in hours)</li> </ul> <p>For example <strong>qDII-CLV3-DR5-E27-LD-SAM7-T5.czi</strong> is an image of the 7th SAM of the set E27, acquired 5 hours after the first image.</p>
NLP2TestableCode Filtered Stack Overflow Dataset
<p>This dataset is for use in NLP2TestableCode, XML files containing Stack Overflow questions and answers tagged with Java. This dataset comes from a March 2019 upload of SO data from https://archive.org/details/stackexchange. Please extract and place in the data folder. The uncompressed dataset size is 6.84GB.</p>
InSAR stack of the 2008 Wells, Nevada earthquake from Envisat descending track 399 processed with Gamma
<p>A stack of unwrapped interferograms on Wells, Nevada for the <a href="https://earthquake.usgs.gov/earthquakes/eventpage/nn00234425/executive">2008 Wells earthquake</a>.</p> <p>Sensor: Envisat ASAR descending track 399 frame 2871</p> <p>Time: 2007.07.09 - 2008.09.01, 11 acquisitions, 51 interferograms</p> <p>Processor: GAMMA</p> <p>A minimum coherence of 0.3 is chosen during phase unwrapping, thus the low coherent pixels are assigned the zero phase value (green in the unwrapPhase_wrap.png).</p> <p>Tropospheric delay estimated from ERA-5 using PyAPS is attached.</p> <p>This is an input dataset for the time series analysis with <a href="https://github.com/insarlab/MintPy">MintPy</a>.</p>
Stacked Dense Denoise-Segmentation FBP synthetic reconstruction from 3601 projection without ring artefacts
<p>The FBP recontruction without ring artefacts from the 3601 projection of the synthetic dataset used in the Stacked Dense Denoise-Segmentation Network. For the reconstruction the TomoPhantom software was used (<a href="https://doi.org/10.5281/zenodo.2546856">https://doi.org/10.5281/zenodo.2546856</a>). We acknowledge Diamond Light Source for the time on I13-2 under proposal mt9396.</p> <div> </div>
Data from: Predicting spatial patterns of plant species richness: a comparison of direct macroecological and species stacking modelling approaches
PLEASE NOTE, THESE DATA ARE ALSO REFERRED TO IN TWO OTHER PUBLICATIONS. PLEASE SEE http://dx.doi.org/10.1111/j.1365-2486.2008.01766.x AND http://dx.doi.org/10.1111/2041-210X.12222 FOR MORE INFORMATION. Aim: This study compares the direct, macroecological approach (MEM) for modelling species richness (SR) with the more recent approach of stacking predictions from individual species distributions (S-SDM). We implemented both approaches on the same dataset and discuss their respective theoretical assumptions, strengths and drawbacks. We also tested how both approaches performed in reproducing observed patterns of SR along an elevational gradient. Location: Two study areas in the Alps of Switzerland. Methods: We implemented MEM by relating the species counts to environmental predictors with statistical models, assuming a Poisson distribution. S-SDM was implemented by modelling each species distribution individually and then stacking the obtained prediction maps in three different ways – summing binary predictions, summing random draws of binomial trials and summing predicted probabilities – to obtain a final species count. Results: The direct MEM approach yields nearly unbiased predictions centred around the observed mean values, but with a lower correlation between predictions and observations, than that achieved by the S-SDM approaches. This method also cannot provide any information on species identity and, thus, community composition. It does, however, accurately reproduce the hump-shaped pattern of SR observed along the elevational gradient. The S-SDM approach summing binary maps can predict individual species and thus communities, but tends to overpredict SR. The two other S-SDM approaches – the summed binomial trials based on predicted probabilities and summed predicted probabilities – do not overpredict richness, but they predict many competing end points of assembly or they lose the individual species predictions, respectively. Furthermore, all S-SDM approaches fail to appropriately reproduce the observed hump-shaped patterns of SR along the elevational gradient. Main conclusions: Macroecological approach and S-SDM have complementary strengths. We suggest that both could be used in combination to obtain better SR predictions by following the suggestion of constraining S-SDM by MEM predictions.
Data from: Testing species assemblage predictions from stacked and joint species distribution models
Aim: Predicting the spatial distribution of species assemblages remains an important challenge in biogeography. Recently, it has been proposed to extend correlative species distribution models (SDMs) by taking into account (a) covariance between species occurrences in so-called joint species distribution models (JSDMs) and (b) ecological assembly rules within the SESAM (spatially explicit species assemblage modelling) framework. Yet, little guidance exists on how these approaches could be combined. We, thus, aim to compare the accuracy of assemblage predictions derived from stacked and from joint SDMs. Location: Switzerland Taxon Birds, tree species Methods: Based on two monitoring schemes (national forest inventory and Swiss breeding bird atlas), we built SDMs and JSDMs for tree species (at 100m resolution) and forest birds (at 1km resolution). We tested accuracy of species assemblage and richness predictions on holdout data using different stacking procedures and ecological assembly rules. Results Despite minor differences, results were consistent between birds and tree species. Cross-validated species-level model performance was generally higher in SDMs than JSDMs. Differences in species richness and assemblage predictions were larger between stacking procedures and ecological assembly rules than between stacked SDMs and JSDMs. On average, predictions were slightly better for stacked SDMs compared to JSDMs, probabilistic stacks outperformed binary stacks, and ecological assembly rules yielded best predictions. Main conclusions: When predicting the composition of species assemblages, the choice of stacking procedure and ecological assembly rule seems more decisive than differences in underlying model type (SDM vs. JSDM). JSDMs do not seem to improve community predictions compared to SDMs or improve predictions for rare species. Still, JSDMs may provide additional insights into community assembly and may help deriving hypotheses about prevailing biotic interactions in the system. We provide simple rules of thumb for choosing appropriate modelling pathways. Future studies should test these preliminary guidelines for other taxa and biogeographic realms as well as for other JSDM algorithms.
Wulfenite (stacked pictures comparison)
Stacked macro-photogrammetry of a Wulfenite from Mexico. Specimen is about 2 cm. Made with Reality Capture (40 pictures, each one is a focus stack of 10 pictures). Source: Objaverse 1.0 / Sketchfab
100 Dollar Bill Stack
This is a stack of dollar bills, there is 50 bills in the stack. Source: Objaverse 1.0 / Sketchfab
Replication Package for the Paper: "How Do Users Revise Architectural Related Questions on Stack Overflow: An Empirical Study"
<p>This is the replication package for the paper: "How Do Users Revise Architectural Related Questions on Stack Overflow: An Empirical Study". In the following, we provide a brief description of the folders and files:</p> <p><strong>(1) raw data</strong></p> <p>The raw data folder contains the retrieved 36,417 posts and the SQL query used for retrieving ARPs from Stack Overflow through the query interface provided by Stack Exchange.</p> <p><strong>(2) filtered ARPs</strong></p> <p>The filtered ARPs folder contains 13,205 filtered candidates ARPs from the retrieved 36,417 posts and the results of data analysis for the first RQ (i.e., RQ1).</p> <p><strong>(3) randomly selected posts and labeling results</strong></p> <p>The randomly selected posts and labeling results folder contains 1,068 randomly selected posts and their labeling results (i.e., 21 ARPs, wherein 14.3%, 3 out of 21 ARPs, do not contain “architect*” terms and 85.7%, 18 out of 21 APRs, contain “architect*” terms).</p> <p><strong>(4) relevant ARPs for answering RQs</strong></p> <p>The relevant ARPs for answering RQs folder contains 4,114 ARPs with revision information for answering the last three RQs (i.e., RQ2, RQ3, and RQ4).</p> <p><strong>(5) interview responses</strong></p> <p>The interview responses folder contains 11 collected interview responses from software practitioners. These responses were gathered to evaluate the identified categories related to ARQ revisions.</p> <p><strong>(6) data extraction and analysis</strong></p> <p>The data extraction and analysis folder contains the MAXQDA file. Data Labeling & Encoding for RQs.mx20 is the results of data labeling and encoding for RQ2, RQ3, and RQ4, which were analyzed by the MAXQDA tool. This file can be opened by MAXQDA 2020 or higher versions, which are available at https://www.maxqda.com/ for download. You may also use the free 14 days trial version of MAXQDA 2020, which is available at https://www.maxqda.com/trial for download.</p>
stack traces Infologic
Open the record for dataset details and reuse information.
Confocal image stack of aPKC/FoxP co-staining
<p>Confocal image stacks of whole mount preparations of central nervous systems of adult Drosophila.</p><p>Genotype: aPKC-Gal4>CD8::GFP, red - FoxP-LexA>CD8::RFP; D: green - D42-Gal4>CD8::GFP, red - FoxP-LexA>CD8::RFP. Confocal image stacks available at: </p>
Application of Machine Learning on MARCUS Aerosols to Remove Ship Stack Contamination
<p>contact qingniu@ou.edu for more info please</p>
Stack Overflow Duplicate Post Dataset
<p>As a part of the supplimentary material for the paper "Refining GPT-3 Embeddings with a Siamese Structure for Technical Post Duplicate Detection".</p> <p>dup_post_csv.tar.gz: The dataset contains all duplicate post pairs from Stack Overflow up to December 2022, with an 80%/20% split between the training and test sets.</p> <p>CQADupStack.tar: The CQADupStack Benchmark dataset. The package contains training and test sets from nine sub-domains. GPT-3 embeddings for all posts are appended.</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.