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.
19,505
datasets available to search
ShareScore release 0.9.0
Dataset results
19,505 results for “advanced;”
Dataset: iShares ESG Advanced MSCI EM ETF (EMXF) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: iShares ESG Advanced MSCI EAFE Index ETF (DMXF) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Advanced Micro Devices, Inc. (AMD) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Advanced Energy Industries, Inc. (AEIS) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Advanced Micro Devices, Inc. (AMD) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Advanced Health Intelligence Ltd (AHI) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Advanced Energy Industries, Inc. (AEIS) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: iShares ESG Advanced MSCI USA ETF (USXF) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: iShares ESG Advanced High Yield Corporate Bond ETF (HYXF) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Figure 2 in A workshop to advance invasive species early detection capacity of The Rapid Environmental DNA Assessment and Deployment Initiative & Network (READI-Net)
Figure 2. The Rapid Environmental (e)DNA Assessment and Deployment Initiative & Network (READI-Net) project components being developed to support molecular detection of invasive species. Molecular tools, like eDNA sampling, are sensitive and costeffective for early detection of invasive species and are one component of the National Early Detection Rapid Response framework.
Figure 1 in A workshop to advance invasive species early detection capacity of The Rapid Environmental DNA Assessment and Deployment Initiative & Network (READI-Net)
Figure 1. The National Early Detection Rapid Response framework developed by the U.S. Department of the Interior Invasive Species Task Force. Open circles represent the components to be enacted for effective early detection and rapid response of invasive species. The associated commentary reflects the primary questions, observations, and directives that the process from one component to the next. At the core of the process, are the informational inputs necessary for management decision-making.
Advanced PySPAM: An Infrastructure to Constrain Underlying Interacting Galaxy Parameters Synthetic Results
<p>This database contains the results for the Chapter 3 of DOR's thesis. For a full description of these results and the way they were built, please see <em>Link to be added on publication</em>.</p> <p>The aim of this Chapter was to use MCMC methods with a fast, efficient simulation algorithm (APySPAM) to constrain the underyling parameters of observed interacting galaxy systems. This algorithm used a Chi-Squared distance minimisation between morphology distributions of observed and simulated images to constrain 13 underlying parameters of galaxy interaction. We applied our algorithm to to 50 of the 62 systems described in <a href="https://ui.adsabs.harvard.edu/abs/2016MNRAS.459..720H/abstract">Holincheck et al. (2016).</a></p> <p>We opted to use the Holincheck et al. sample as the underlying parameters of these systems had already been constrained using a Citizen Science project named <a href="https://mergers.galaxyzoo.org/">Galaxy Zoo: Mergers</a>. This gave us a ground truth to which compare our constraints to. We created synthetic observations of each image, and then ran our MCMC over them, achieving constraint across the sample and parameter space. However, when applied to observational data (we opted to use SDSS images of these systems) we are unable to constrain the full parameter space. This is particularily true of the orientations of the interacting system and their relative sizes.</p> <p>Exploring using velocity information in our constraints find that we improve almost all our constrains considerably. Therefore, adding in spectroscopic information to this method could drastically improve it. The main limitation of this approach, however, is computation time with each system taking approximately 20 hours on a well parallelised HPC to converge. Alternatives to improve performance lie in simulation based inference (SBI, an introduction can be found <a href="https://arxiv.org/pdf/2009.08459">here</a>) or including the use of GPUs (such as done by NVIDEA in fluid dynamics <a href="https://developer.nvidia.com/blog/ai-powered-simulation-tools-for-surrogate-modeling-engineering-workflows-with-siml-ai-and-nvidia-modulus/">here</a>)</p> <p>The results are portrayed as corner plots, with contour plots showing the distribution of likelihoods found in each MCMC run and the histograms on the side showing the marginalised posterior distributions.</p>
Shaping History: Advanced Machine Learning Techniques for the Analysis and Dating of Cuneiform Tablets over Three Millennia - Datasets
<p>Our research leverages advanced deep learning methods to classify cuneiform tablets by their historical periods, focusing on shape analysis rather than textual content. Utilizing a dataset of over 94,000 images from the Cuneiform Digital Library Initiative, we introduce a novel toolset powered by Variational Auto-Encoders (VAEs) to enhance model interpretability. By highlighting the predictive power of tablet silhouettes for historical period classification with a ResNet model reaching 61\% macro-F1 score,, our approach allows researchers to explore changes in tablet shapes across different eras. This methodology not only complements traditional archaeological methods but also enriches the field of document analysis and diplomatics, offering valuable tools for historians and epigraphists to understand ancient Mesopotamian cultures.</p> <p>The Datasets attached include:</p> <ul> <li>All CDLI IDs used and their URLs on CDLI</li> <li>All tablet silhouettes - Black and white representations</li> <li>A height-to-width ratio table, of the largest component extracted from the silhouettes.</li> <li>VAE encodings per tablet, extracted from the VAE model</li> </ul>
Tissue-aware interpretation of genetic variants advances the etiology of rare diseases
<p>Pathogenic variants underlying Mendelian diseases often disrupt the normal physiology of a<br>few tissues and organs. However, variant effect prediction tools that aim to identify<br>pathogenic variants are typically oblivious to tissue contexts. Here we report a machine-<br>learning framework, denoted ‘Tissue Risk Assessment of Causality by Expression for<br>variants’ (TRACEvar, https://netbio.bgu.ac.il/TRACEvar/), that offers two advancements.<br>First, TRACEvar predicts pathogenic variants that disrupt the normal physiology of specific<br>tissues. This was achieved by creating 14 tissue-specific models that were trained on over<br>14,000 variants and combined 84 attributes of genetic variants with 495 attributes derived<br>from tissue omics. TRACEvar outperformed 10 well-established and tissue-oblivious variant<br>effect prediction tools. Second, the resulting models are interpretable, thereby illuminating<br>variants' mode-of-action. Application of TRACEvar to variants of 52 rare-disease patients<br>highlighted pathogenicity mechanisms and relevant disease processes. Lastly, interpretation<br>of large-scale models revealed that top-ranking determinants of pathogenicity included<br>attributes of disease-affected tissues, particularly cellular process activities. Hence, tissue<br>contexts and interpretable machine-learning models can greatly enhance the etiology of rare<br>diseases.</p> <p>Article link: https://www.embopress.org/doi/full/10.1038/s44320-024-00061-6</p> <p> </p>
Fig. 1 in Recent advances in our knowledge of Australian anisakid nematodes
Fig. 1. Countries where human cases of anisakidosis have been reported (Gorokhov et al., 1999; Smith, 1999; Lopez-Serrano et al., 2000; Chai et al., 2005; Kapral et al., 2009; Shamsi and Butcher, 2011).
Fig. 2 in Recent advances in our knowledge of Australian anisakid nematodes
Fig. 2. Number of published articles on anisakid nematodes (key word: Anisakidae) until 2013; based on the search on Web of Science and CABI (Gorokhov et al., 1999; Smith, 1999; Lopez-Serrano et al., 2000; Chai et al., 2005; Kapral et al., 2009; Shamsi and Butcher, 2011).
Fig. 1 in Invasion ecology meets parasitology: Advances and challenges
Fig. 1. Number of articles published per year, between 1980 and 2016, on either biological invasions in general, or more specifically on parasitism in the context of biological invasions, from a search of the Web of Science ® (see text). Note the different scales on the y-axes for the two article counts.
Fig. 4 in Morphological keys to advance the understanding of protostrongylid biodiversity in caribou (Rangifer spp.) at high latitudes
Fig. 4. Line drawings of caudal extremities of Parelaphostrongylus andersoni and Varestrongylus eleguneniensis dorsal spine larvae from Prestwood (1972) and Verocai et al. (2014). The line drawings clearly show the characteristic morphological features identified in this study.
Fig. 3 in Morphological keys to advance the understanding of protostrongylid biodiversity in caribou (Rangifer spp.) at high latitudes
Fig. 3. Map showing the location of samples and results from the fecal survey. Each point on the map represents a group of 2‾10 samples.
Fig. 2 in Morphological keys to advance the understanding of protostrongylid biodiversity in caribou (Rangifer spp.) at high latitudes
Fig. 2. Identification guide for differential diagnosis of dorsal spine larvae of Parelaphostrongylus andersoni and Varestrongylus eleguneniensis.
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.