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1,063 results for “search”
Driving Traffic to Institutional Repositories: How Search Engine Optimization can Increase the Number of Downloads from IR
<p>The success of institutional repositories (IR) is measured in large part by the number of file downloads they sustain. Most traffic to IR is referred by Internet search engines, but referrals are hindered when IR are not properly optimized for search engine harvesting and indexing, leading to low visitation and downloads. This presentation will discuss search engine optimization techniques, especially for Google Scholar, which can be responsible for the majority of referrals that result in IR file downloads. The presentation will also introduce a new web service called RAMP (Repository Analytics & Metrics Portal) that accurately counts file downloads from IR and requires no installation or training.</p>
ZINC15 for Drug Similarity Search
<p><strong>Abstract:</strong> This dataset is a subset of the ZINC15 database, specifically filtered and processed for molecular similarity search applications using MegaMolBART embeddings. The subset focuses on drug-like molecules with specific physicochemical and purchasability properties.</p> <p><strong>Keywords:</strong> ZINC15, Molecular Similarity Search, MegaMolBART, Drug Discovery, Cheminformatics.</p> <p><strong>Background:</strong> The ZINC15 database is a comprehensive collection of commercially available compounds for virtual screening. This subset was created to facilitate the development of machine learning models for drug discovery, particularly those based on molecular embeddings.</p> <p><strong>Methodology:</strong> The ZINC15 database was queried using the following criteria:</p> <ul> <li>Molecular weight <= 500 Daltons</li> <li>LogP <= 5</li> <li>Reactivity level = "reactive"</li> <li>Purchasability = "annotated" </li> </ul> <p>The resulting dataset was then processed to extract MegaMolBART embeddings for each molecule.</p> <p><strong>Data Description:</strong> </p> <p>The dataset is organized into three folders:</p> <ul> <li><code><strong>/data/project/ubrite/drg-depot/zinc15-similarity-search/raw-data/</strong></code> (66 GB): This folder contains the raw data files obtained from the ZINC15 database after applying the filtering criteria.</li> <li><code><strong>/data/project/ubrite/drg-depot/zinc15-similarity-search/processed-data/</strong></code> (13 GB): This folder contains the processed data, including the extracted MegaMolBART embeddings for each molecule. </li> <li><code><strong>/data/project/ubrite/drg-depot/zinc15-similarity-search/query/</strong></code>: This folder contains sample SMILES strings and their corresponding embeddings for performing similarity searches.</li> </ul> <p><strong>Technical Specifications:</strong> </p> <ul> <li><strong>Format:</strong> SMILES strings, numerical data (embeddings)</li> <li><strong>Size:</strong> 79 GB (total)</li> <li><strong>License:</strong> This dataset is derived from the ZINC15 database and processed using MegaMolBART. It is subject to the licenses of both the ZINC15 database and the MegaMolBART model. <ul> <li>ZINC15 Database: ZINC15 data is made available under the Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license. For more information, please visit the ZINC15 website.</li> <li>MegaMolBART: The MegaMolBART model and its associated data are copyrighted by AstraZeneca and NVIDIA. The usage of MegaMolBART is subject to the terms and conditions specified by the copyright holders.</li> </ul> </li> </ul> <p>By using this dataset, you agree to comply with the licenses and conditions imposed by the ZINC15 database and MegaMolBART.</p> <p><strong>Access and Usage:</strong> </p> <p>The dataset is available for download through Zenodo. Users are encouraged to acknowledge this dataset and the corresponding Zenodo entry in any publications or research projects that utilize the data. </p> <p><strong>Contact:</strong> Fuad Al Abir, fuad021@uab.edu</p>
Exploring the Search Space of Neural Network Combinations obtained with Efficient Model Stitching - Results Data
Open the record for dataset details and reuse information.
Data used for analysis in "Calibrating tropical forest coexistence in ecosystem demography models using multi-objective optimization through population-based parallel surrogate search"
Open the record for dataset details and reuse information.
Search Strategies for the Generic Justification Evidence Synthesis: magnetic resonance guided radiotherapy
<p>The dataset includes the complete, reproducible search strategies for all bibliographic databases searched during this project. The search strategies were designed to answer the following research questions: </p> <p><strong>RQ1.</strong> In patients with cancer requiring radiotherapy, does the choice of image-guided modality (MRgRT versus other) result in a difference in clinical effectiveness?</p> <p><strong>RQ2. </strong>In patients with cancer requiring radiotherapy, does the choice of image-guided modality (MRgRT versus other) result in a difference in potential harms?</p> <p> </p>
Data for "Beyond theory driven discovery: hot random search and datum derived structures"
<p>Data associated with "Beyond theory driven discovery: hot random search and datum derived structures"</p> <p>contributed to: FD 2025: Data-driven discovery in the chemical sciences</p> <p><code>.</code><br><code>├── README</code><br><code>├── b</code><br><code>│ ├── collection</code><br><code>│ ├── eddp</code><br><code>│ ├── test-105-111</code><br><code>│ ├── test-192</code><br><code>│ └── test-56</code><br><code>├── c</code><br><code>│ ├── collection</code><br><code>│ ├── collection-0.5eV</code><br><code>│ ├── collection-1eV</code><br><code>│ ├── manifest-dia</code><br><code>│ ├── test-dia-25-48</code><br><code>│ ├── test-dia-8-24</code><br><code>│ └── test-dia-nosymm-8</code><br><code>└── pyrope</code><br><code> ├── airss</code><br><code> ├── collection-gamma-relax</code><br><code> ├── manifest</code><br><code> └── test</code></p> <p><br>C J Pickard, 2024<br>Cambridge</p>
Data from project 'Stabilising heritage: Iron gall ink corrosion on paper and the eternal search for an ideal treatment', RAÄ-2023-0820
<p>Data from the project "Stabilising <span>heritage: Iron gall ink corrosion on paper and the eternal search for an ideal treatment</span>" (Diarienummer RAÄ-2023-0820) including files for sample descriptions. Data from the following analytical techniques is included: 1. Scanning electron microscopy with energy dispersive x-ray spectroscopy (SEM-EDS); 2. X-ray fluorescence spectroscopy (XRF). Instrument reports are also included. The intention of the project was to develop a non-aqueous treatment method for documents containing iron gall ink which could provide both mechanical stability and reduce the corrosion rate. SEM-EDS mapping was performed to observe the penetration depth of the ink and treatment materials in the paper samples while XRF lines scans were performed to better understand the phenomenon of a "halo" effect around certain ink samples after treatment. All data was collected as part of the master's thesis "Stabilising <span>heritage: Iron gall ink corrosion on paper and the eternal search for an ideal treatment</span>: " by Anna Lagerqvist Alidoost, Department for Conservation, University of Gothenburg (https://hdl.handle.net/2077/82110).</p>
Search Strategies for Teledermatology for triage of primary care referrals: a health technology assessment
<p>The dataset includes the complete, reproducible search strategies for all bibliographic databases searched during this project. The search strategies were designed to answer the following research question: What is the clinical efficacy, effectiveness and safety of teledermatology fortriage from primary care referrals compared to traditional face-to-face dermatology consultations?</p>
Data release of the Swift-LVK subthreshold search during the third LIGO-Virgo-KAGRA observing run
<div> <div>Here we describe the structure of the data release.</div> <br> <div>1. The folder fits contains all the upper limit maps in the for of fits files. Run the code plot_maps_paper.py to obtain the figures 3 and 4 of the paper</div> <br> <div>2. The folder pdf contains all the upperlimit maps, with the GW sky localizations, in pdf format</div> <br> <div>3. The file lum.csv contains the data to reproduce Fig. 6. If the 'cred' colums is empty, then the event is only detected in low latencyl. If 'no' then the event has p_astro<0.5, otherwise it has p_astro>0.5.</div> <br> <div>4. The file joint_far.cvs contains the data to reproduce the Figure 7. Columns are self explanatory</div> <br> <div>5. The file data_BBH.txt, data_BBH_highpastro.txt, data_BBH_allreal.txt, data_BBH_allreal_earth.txt contain the likelihood to produce Figs 8-9-10-11. The plots are produced runnning read.py and read_allreal.py</div> <br> <div>6. All the tables are provided in cvs format</div> </div>
Search strings for systematic review and meta-analysis
<p>These are the search strings used in PubMed, Embase, and Web of Science to identify possible studies on the sensitivity of PAS and GMS for identifying cutaneous and subcutaneous fungal infections in derrmatopathology.</p>
Data for "Expanding the scope of a catalogue search to bioisosteric fragment merges using a graph database approach"
<p>Data for "Expanding the scope of a catalogue search to bioisosteric fragment merges using a graph database approach"</p> <p>Contains files with the SMILES retrieved from the database (fragnet_query_outputs.zip) and sdf files containing the full lists of scored compounds for Fragalysis target test cases (scored_output_sdfs.zip). The compound sets used in the bioisosteric and perfect comparisons are saved in a separate directory.</p>
Search-for-Barium-Candidates-in-LAMOST-using-the-MEASNet-model
<p>This repository is for the paper MEASNet: A Model for Barium Stars Identification and s-process Abundance Estimation from LAMOST DR10 Low-resolution Survey. I. by Shengjun Song. It is associated with the manuscript number AAS51558.</p>
The SLR Search Results
<p>The uploaded bib files are the search results used for our systematic literature review paper: Latency-Aware Enactment of Adaptation in Data Stream Processing - A Systematic Literature Review.</p> <p>The search results retrieved from ACM: ACM_Search_Result.bib</p> <p>The search results retrieved from ScienceDirect: ScienceDirect_Search_Result.bib</p>
Dataset for publication: 'Search Space Representation and Reduction Methods to Enhance Multi-Objective Water Supply Monitoring Design'
<p>This is the dataset the publication "Search Space Representation and Reduction Methods to Enhance Multi-Objective Water Supply Monitoring Design" is based on.</p>
FIGURE 11 in In search of the real Pseudomallada prasinus (Neuroptera, Chrysopidae)
FIGURE 11. Multiple Correspondence Analysis (MCA) based on 10 factors isolates the "marianus" morph in both sexes from the two other morphs.
FIGURE 6 in In search of the real Pseudomallada prasinus (Neuroptera, Chrysopidae)
FIGURE 6. Portrait of a "marianus" female without any coloration of the subantennal suture above the black genal stripe.
FIGURE 5 in In search of the real Pseudomallada prasinus (Neuroptera, Chrysopidae)
FIGURE 5. Different portions of black on the three veins surrounding the intramedian cell (im cell). In im 5 two or three sides contain black or at least speckled parts. Im 6 was observed only in P. abdominalis.
FIGURE 9 in In search of the real Pseudomallada prasinus (Neuroptera, Chrysopidae)
FIGURE 9. "Sulfurhead" male with yellow vertex and large twin dots (brown and black) on mesothorax. All males in the prasinus group have a broad, almost trapezoid pronotum.
FIGURE 4 in In search of the real Pseudomallada prasinus (Neuroptera, Chrysopidae)
FIGURE 4. Double number of setae on costal cross veins in "furwing" males (4a), as compared to similar numbers in "nonfurwing" males (4b) and females.
FIGURE 3 in In search of the real Pseudomallada prasinus (Neuroptera, Chrysopidae)
FIGURE 3. Different portions of black in costal cross veins. Ccv4 is typical for P. ventralis, ccv5 for P. abdominalis and P. zelleri.
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.