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.
7,515
datasets available to search
ShareScore release 0.7.1
Dataset results
7,515 results for “screenings”
Figure 2. Screen capture of the current application-INTELLIGENT AGENT FOR ACQUISITION OF THE MOTHER TONGUE VOCABULARY
<p>From this screen capture (figure 2), you can observe that, for example, the noun car (masina)<br> is in a great correspondence with the correct word car(masina), and in the same correspondence<br> with the incorrect word small (mic). But this is due to the fact that the system worked only with few<br> examples. After a training with much more examples, the system will increase the correspondence<br> between the concept/object car and the word car, and the correspondence between the object car and<br> the word little will remain smallest.</p>
Figure 5: Screen shot of the generated collaborative virtual environment
<p>The output of the design is an xml file. The technical user has to customize<br> the designed session in order to put in the virtual environment engine in order<br> to generate the session. The customization regards technical parameters (for<br> example spatial coordinate, intensity of the light and so on). A screen shot of<br> the generated session is in figure 5.</p>
Figure 4 in Bioactivity and chemical screening of endophytic fungi associated with the seaweed Ulva sp. of the Bay of Bengal, Bangladesh
Figure 4: Isolate UE-5 (Aspergillus terreus). (A) Surface of colony, on potato dextrose agar after 6 days culture at 28 °C. (B) Reverse of colony. (C) Mycelia, conidiophores and conidia after 5 days culture. (D) Conidiophore with conidia. (E) Phylogenetic tree inferred from internal transcribed spacer sequences using maximum likelihood method.
Figure 3 in Bioactivity and chemical screening of endophytic fungi associated with the seaweed Ulva sp. of the Bay of Bengal, Bangladesh
Figure 3: Isolates UE-3 (Curvularia sp., A–C) and UE-4 (Curvularia moringae, D–H). (A) Surface of colony, on potato dextrose agar (PDA) after 6 days culture at 28 °C. (B) Reverse of colony. (C) Mycelia and conidia after 7 days culture. (D) Surface of colony, on PDA after 12 days culture at 28 °C. (E) Reverse of colony. (F) Mycelia and conidia after 7 days culture. (G) Conidium. (H) Phylogenetic tree inferred from internal transcribed spacer sequences using maximum likelihood method.
Figure 6 in Bioactivity and chemical screening of endophytic fungi associated with the seaweed Ulva sp. of the Bay of Bengal, Bangladesh
Figure 6: Antimicrobial activity of the crude extracts obtained from marine endophytic fungi associated with Ulva sp. against five bacteria (Staphylococcus aureus, Bacillus megaterium, Escherichia coli, Salmonella typhi, Pseudomonas aeruginosa) and one fungus (Aspergillus flavus). Values are mean ± standard deviation, n = 3. Bars with different letters are significantly different according to Tukey's post hoc test at p = 0.05. Note: The solvent control (dichloromethane) showed no inhibition (0 mm). The strongest inhibitory effects were observed with the positive controls kanamycin (S1) and ketoconazole (S2).
Figure 2 in Bioactivity and chemical screening of endophytic fungi associated with the seaweed Ulva sp. of the Bay of Bengal, Bangladesh
Figure 2: Isolate UE-2 (Nigrospora magnoliae). (A) Surface of colony, on potato dextrose agar after 6 days culture at 28 °C. (B) Reverse of colony. (C) Mycelia and conidia after 21 days culture. (D) Conidiogenus cells with conidia. (E) Phylogenetic tree inferred from internal transcribed spacer sequences using maximum likelihood method.
Figure 5 in Bioactivity and chemical screening of endophytic fungi associated with the seaweed Ulva sp. of the Bay of Bengal, Bangladesh
Figure 5: Isolate UE-6 (Collariella sp.). (A) Surface of colony, on potato dextrose agar after 12 days culture at 28 °C. (B) Reverse of colony. (C) Terminal ascomatal hairs with ascospores after 45 days culture. (D) Phylogenetic tree inferred from internal transcribed spacer sequences using maximum likelihood method.
Figure 1 in Bioactivity and chemical screening of endophytic fungi associated with the seaweed Ulva sp. of the Bay of Bengal, Bangladesh
Figure 1: Isolate UE-1 (Chaetomium globosum). (A) Surface of colony, on potato dextrose agar after 6 days culture at 28 °C. (B) Reverse of colony. (C) Ascomata after 28 days culture. (D) Asci.(E) Ascus with ascospores. (F) Ascospores. (G) Phylogenetic tree inferred from internal transcribed spacer sequences using maximum likelihood method.
The Pan-Canadian Chemical Library: A Mechanism to Open Academic Chemistry to High-Throughput Virtual Screening
<h1>Pan-Canadian Chemical Library</h1> <p>This Zenodo repository contains the cheap and druglike subset of the Pan-Canadian Chemical Library (PCCL) project. For more information, visit <a href="https://pccl.thesgc.org/" rel="nofollow">https://pccl.thesgc.org</a>.</p> <h2>PCCL library</h2> <p>The PCCL library is splitted by reaction, then by number of heavy atoms. Two types of files are available in zip archives:</p> <ul> <li>The SMILES format files, with the SMILES string and their product name,</li> <li>The CSV format file, with all the information generated during their enumeration: reagents, druglike properties, etc.</li> </ul> <p>Note: Purchasability is defined according to two integers: 1 for products only composed of BB-50 reagents, and 2 for products composed of BB-40 or BB-50 reagents. Read more about the meaning of these reagents groups in the article below.</p> <h2>Citation</h2> <p>If you find the PCCL useful or if you use it, please cite our paper:</p> <p>Bedart, C. <em>et al.</em> The Pan-Canadian Chemical Library: A mechanism to open academic chemistry to high-throughput virtual screening. Scientific Data 11, (2024).<br>doi: <a title="10.1038/s41597-024-03443-5" href="https://www.nature.com/articles/s41597-024-03443-5">10.1038/s41597-024-03443-5</a></p> <p> </p> <p> </p>
Datasets of DFT adsorption energies of H and for O and OH on different pure metals and binary intermetallic compounds considering the application of elastic strains and lists of candidates for screening
<p>This resource contains two datasets and two lists of candidates for screening in JSON format. Also It contains ZIP folders with all Quantum Espresso Inputs and outputs from which the JSON datasets were obtained. All Quantum Espresso outputs will be later added to Catalysis Hub (https://www.catalysis-hub.org/). The file "QuantumEspresso_versions" is a text file contaning the information of the Quantum Espresso versions employed for obtaining the dataset.</p> <p>The datasets contain the adsorption energies for surface slabs of a large number of binary intermetallic compounds with different compositions and lattices (for instance, A3B fcc, A3B hpc, AB bcc, etc.). Adsorption energies were computed for different adsorbates (H, O, and OH) on distinct adsorption sites (e.g., fcc AAB, fcc AAA, hcp AAA, hcp AAB, on-top A, and on-top B) and minimum energy surfaces. In addition, different elastic strains (biaxial tension, biaxial compression) were applied to assess their effect on adsorption energies. All calculations were carried out using DFT approximations as implemented in the Open-source software Quantum Espresso. Besides the adsorption energies, the datasets also contain relevant geometric and electronic descriptors (PSI, cell volume, weighted atomic radius, generalized coordination number, weighted electronegativity, weighted first ionization energy, outer electrons, and biaxial strain) calculated to feed them as features in the training of ML models. The datasets with the tag "scaled" on its name have the descriptors scaled following a MinMax scaling and are given in xlsx format.</p> <p>The lists for screening contain candidates not included in the dataset for which Random Forest predictions of the Eads were obtained. The lists contain the geometric and electronic descriptors of all screening candidates, as well as the predicted adsorption energy (Eads_RF).</p> <p>A GitHub repository is linked to this dataset (https://github.com/vvassilevg/HighHydrogenML). The repository contains two Python scripts:</p> <p>1) Script for creating a dataset from QuantumEspresso outputs, where all relevant descriptors are computed. It outputs a pickle and json files that can be later converted to any other desired format (like xlsx).</p> <p>2) Script for training a Random Forest model for the prediction of adsorption energies (the datasets with the "scaled" tag must be used for the script to work correctly).</p> <p> </p> <p>The dataset, ML model and screening have been accepted for publication in Catalysis Science & Technology DOI: DOI:<a title="Link to landing page via DOI" href="https://doi.org/10.1039/D4CY00491D">10.1039/D4CY00491D</a>. The accepted Manuscript and the Supplementary information are avilable within this repository.</p> <p> </p> <p>If you use this dataset or any of the files within this repository, please cite the original publication (<a title="Link to landing page via DOI" href="https://doi.org/10.1039/D4CY00491D">10.1039/D4CY00491D)</a> in your work.</p>
Fig. 4 in Molecular screening of tsetse flies and cattle reveal different Trypanosoma species including T. grayi and T. theileri in northern Cameroon
Fig. 4 Dcmtlcbutcon of Trypanosoma mpeccem cn tmetme flcem. a Relatcve abundance of tlspanomomal DNA bs mpeccem cn the gut. b Relatcve abundance of tlspanomomal DNA bs mpeccem cn plobomccm. c Collelatcon of tlspanomomal DNA cn gut and plobomccm. Abbreviations: Tg, T. grayi; Tc, T. congolense; Tb, T. brucei mmp.; Tv, T. vivax. If no amplccon wam detected, the fls wam conmcdeled to be negatcve
Fig. 3 in Molecular screening of tsetse flies and cattle reveal different Trypanosoma species including T. grayi and T. theileri in northern Cameroon
Fig. 3 The mequence of an amplccon obtacned flom tmetme fls gut wcth plcmelm mpeccfcc fol T. grayi. Speccfcc plcmelm (TGR-In plcmel met) talgeted agacnmt T. grayi amplcfced a 525 bp flagment (MG234546, Addctconal fcle 1: Table S4) flom tmetme fls gut mample (ID 237-51-00211-1-40-10, G. tachinoides, Addctconal fcle 1: Table S4). The flagment wam mequenced and alcgned wcth the collempondcng flagment of genomcc DNA flom T. grayi ANR4 (JMRU01000589)
Microscopic images of screen-printed conductive layers on polymer fabrics
<p>The data collection includes data from SEM and optical microscope of screen-printed conductive layers on polymer fabrics (PET and cotton). The results of the work related to the attached data and detailed description of the layer production procedure were published in Rac-Rumijowska, O., Pokryszka, P., Rybicki, T., Suchorska-Woźniak, P., Woźniak, M., Kaczkowska, K., & Karbownik, I. (2024). Influence of Flexible and Textile Substrates on Frequency-Selective Surfaces (FSS). Sensors, 24(5), 1704.</p> <p> </p> <p>Description of the included files:</p> <p><strong><span>PET_Ag_PE672_cross_section_1</span></strong><span> – microscopic image of a cross-section of PET fabric covered with DuPoint PE672 silver paste – sample 1</span></p> <p><strong><span>PET_Ag_PE672_cross_section_2</span></strong><span> – microscopic image of a cross-section of PET fabric covered with DuPoint PE672 silver paste – sample 2</span></p> <p><strong><span>PET_Ag_PE674_cross_section_1</span></strong><span> – microscopic image of a cross-section of PET fabric covered with DuPoint PE674 silver paste – sample 1</span></p> <p><strong><span>PET_Ag_PE674_cross_section_2</span></strong><span> – microscopic image of a cross-section of PET fabric covered with DuPoint PE674 silver paste – sample 2</span></p> <p><span> </span><strong><span>SEM_PET_Ag_1W (1-9)</span></strong><span> – microscopic SEM image of a PET fabric covered with 1 layer of DuPoint PE672 silver paste – image 1-9</span></p> <p><strong><span>SEM_PET_Ag_2W (1-6)</span></strong><span> – microscopic SEM image of a PET fabric covered with 2 layers of DuPoint PE672 silver paste – image 1-6</span></p> <p><strong><span>SEM_PET_Ag_2W (1-6)</span></strong><span> – microscopic SEM image of a PET fabric covered with 3 layers of DuPoint PE672 silver paste – image 1-6</span></p> <p><strong><span>SEM_BAWELNA_Ag_2W (1-6)</span></strong><span> – microscopic SEM image of a cotton fabric covered with 2 layers of DuPoint PE672 silver paste – image 1-6</span></p> <p><strong><span>SEM_BAWELNA_Ag_3W (1-6)</span></strong><span> – microscopic SEM image of a cotton fabric covered with 3 layers of DuPoint PE672 silver paste – image 1-6</span></p> <p><strong><span>SEM_BAWELNA_Ag_4W (1-6)</span></strong><span> – microscopic SEM image of a cotton fabric covered with 4 layers of DuPoint PE672 silver paste – image 1-6</span></p> <p><strong><span>BAWELNA </span></strong><span><span> </span>– microscopic image of a cotton fabric</span></p> <p><strong><span>BAWELNA_Ag_4w (1-3) </span></strong><span><span> </span>– microscopic image of <span> </span>cotton fabric covered with 4 layers DuPoint PE674 silver paste – image 1-3</span></p> <p><strong><span>PET </span></strong><span><span> </span>– microscopic image of a PET fabric</span></p> <p><strong><span>PET_Ag_1W (1-2)</span></strong><span> – microscopic image of a PET fabric covered with 1 layer of DuPoint PE672 silver paste – image 1-2</span></p> <p><strong><span>PET_Ag_2W (1-3)</span></strong><span> – microscopic image of a PET fabric covered with 2 layers of DuPoint PE672 silver paste – image 1-3</span></p> <p><strong><span>PET_Ag_2W (1-2)</span></strong><span> – microscopic image of a PET fabric covered with 3 layers of DuPoint PE672 silver paste – image 1-2</span></p>
Figure 2 in Risk screening of non-native freshwater fishes in Yunnan Province, China
Figure 2. Risk screening scores for the non-native fish species screened with the AS-ISK: (A) basic risk assessment (BRA) for Yunnan Province; (B) BRA plus climate-change assessment (BRA+CCA) for Yunnan Province; dashed lines indicate thresholds for different intrusion risk levels (see thresholds in Table 2)
Figure 1 in Risk screening and management of alien terrestrial planarians in The Netherlands
Figure 1. Photographs of the alien terrestrial planarian species found indoors and outdoors in The Netherlands: Anisorhynchodemus sp., found in greenhouses in Rotterdam, Amsterdam and Arnhem (A, Photo by Roy Kleukers); Bipalium kewense found in greenhouses Amsterdam, Utrecht and Leiden (B, Photo by Pierre Gros); Parakontikia ventrolineata found in a garden in Amsterdam Noord (C, Photo by Roy Kleukers); Caenoplana coerulea found in a greenhouse in Nijmegen (D, Photo by Roy Kleukers); Caenoplana variegata found in gardens in Castricum, Bleiswijk, Hillegersberg, Zwijndrecht, Zaandam and Heemstede (E, Photo by Roy Kleukers); Caenoplana cf. micholitzi found in a greenhouse in Arnhem (F, Photo by Roy Kleukers); Dolichoplana sp. found in greenhouses in Amsterdam and Arnhem, (G, Photo by Roy Kleukers); Marionfyfea adventor found in gardens in Goes, Schiedam and Beek-Ubbergen (H, Photo by Jochem Kuhnen) and Obama cf. nungara found in a garden center in Gilzen (I, Photo by Pierre Gros).
Figure 3 in Risk screening and management of alien terrestrial planarians in The Netherlands
Figure 3. Distribution of alien terrestrial planarian species in The Netherlands (A sp = Anisorynchodemus sp; BK = Bipalium kewense; CC = Caenoplana coerulea; CM = Caenoplana cf. micholitzi; CV = Caenoplana variegata; D sp = Dolichoplana sp. MA = Marionfyfea adventor; ON = Obama cf. nungara; PV = Parakontikia ventrolineata). For details see Supplementary material Table S24.
Figure 2 in Risk screening and management of alien terrestrial planarians in The Netherlands
Figure 2. Cumulative number of introduced species based on the years of first records of alien terrestrial planarians in the United Kingdom, France, and The Netherlands.
Video-Audio Neural Network Ensemble For Comprehensive Screening Of Autism Spectrum Disorder in Young Children (Openpose ADOS Dataset)
<p>Here, we share a de-identify subsample of the data used in the <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0308388">research article</a>, that will allow interested scientists to test the <a href="https://github.com/AutismBrainBehavior/Video-Neural-Network-ASD-screening">shared code</a>, as well as, develop alternatives for achieving better prediction accuracy. We have prepared a subsample of pose estimation videos for the first 10 minutes of ADOS examination videos for each of the 160 children including in the current study (80 ASD and 80 TD, 80 Training set and 80 Testing set).</p> <p>With this subset of the full dataset, our trained model achieved an accuracy of 68.75% over 80 videos (40 ASD & 40 TD) by training the Visual Geometry Group 16 Long short term memory recurrent neural network (VGG16 LSTM RNN) over 80 training videos (40 ASD & 40 TD) at 64 batch size and 120 epochs.</p>
Dataset for the Synaptotagmin-1 antibody screening study
<p><strong>This antibody characterization dataset is related to the F1000 research article openly available at F1000Research.</strong></p> <p><em>The dataset presented contains the following underlying raw data for study which evaluated twelve commercial antibodies against Synaptotagmin-1 in western blot, immunoprecipitation, immunofluorescence and flow cytometry. The original study is also accessible on the YCharOS Zenodo community (<a href="https://doi.org/10.5281/zenodo.12666747">https://doi.org/10.5281/zenodo.12666747</a>).</em></p> <p><em>The Dataset is in the format of a zip file. Once downloaded, please expand the zip file to access the folders containing the underlying data for Western blot (Wb), immunoprecipitation (IP) and immunofluorescence (IF).</em></p>
Figure 1 in Phytochemical screening and evaluation of antioxidant, total phenolic and flavonoid contents in various weed plants associated with wheat crops
Figure 1. DPPH Assay for Convolvulus arvensis, Chenopodium murale, Avena fatua, Phalaris minor extracts in different solvents.
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.