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.
6,467
datasets available to search
ShareScore release 0.9.0
Dataset results
6,467 results for “process”
The spin-coating process of the control (top) and ZW-treated (bottom) 2D perovskite films.
<p>The spin-coating process of the control (top) and ZW-treated (bottom) 2D perovskite films.</p>
Detection, quantification and classification of ripened tomatoes: a comparative analysis of image processing and machine learning
<p>This is an open dataset.</p>
Detection, quantification and classification of ripened tomatoes: a comparative analysis of image processing and machine learning
<p>In this study, specifically for the detection of ripe/unripe tomatoes with/without defects in the crop field, two distinct methods are described and compared from captured images by a camera mounted on a mobile robot. One is a machine learning approach, known as 'Cascaded Object Detector' (COD) and the other is a composition of traditional customised methods, individually known as 'Colour Transformation': 'Colour Segmentation' and 'Circular Hough Transformation'. The (Viola-Jones) COD generates 'histogram of oriented gradient' (HOG) features to detect tomatoes. For ripeness checking, the RGB mean is calculated with a set of rules. However, for traditional methods, colour thresholding is applied to detect tomatoes either from natural or solid background and RGB colour is adjusted to identify ripened tomatoes. This algorithm is shown to be optimally feasible for any micro-controller based miniature electronic devices in terms of its run time complexity of <i>O</i>(<i>n</i><sup>3</sup>) for a traditional method in best and average cases. Comparisons show that the accuracy of the machine learning method is 95%, better than that of the Colour Segmentation Method using MATLAB.</p>
Dataset Used in "Phase-I analysis for monitoring nonlinear profile signals in manufacturing processes"
<p>This is the dataset used in Ding, Zeng, and Zhou, 2006, “Phase-I analysis for monitoring nonlinear profile signals in manufacturing processes.” <em>Journal of Quality Technology</em>, Vol. 38(3), pp. 199-216. This data file has 528 data records, two fewer than the 530 number mentioned in the paper.</p>
Japhug for Natural Language Processing: a single-speaker audio corpus with transcriptions
<p><em>(français ci-dessous)</em></p> <p>This archive contains a dataset (audio files and transcriptions) of a minority language, Japhug (Glottocode: japh1234; closest iso 639-3 code: jya). The archive contains a subset of the Japhug corpus of the Pangloss Collection: it is a single-speaker corpus, consisting of all the audio resources transcribed, for the main speaker of this corpus (Ms. Tshendzin).<br> The corpus is versioned, so that the experiments carried out on these resources (for linguistic research or for Natural Language Processing) are fully reproducible. All relevant information is contained in YAML files (.yml extension; one in French, one in English).<br> The data sub-folder contains the converted and demultiplexed audio files, as well as the annotations associated with each channel of the audio files.<br> The summary files contain, among other things, the list of graphemes used in the language (complex graphemes are particularly important), as well as information on the various resources (audio and annotations), such as their identifiers (DOIs) and links to the original files.<br> From a computational point of view, the list of DOIs of the audios and annotations described in this YAML file is sufficient to generate this corpus at a given time. A corpus like the present one can be viewed as the version, at a given time, of a set of documents in the Pangloss collection: a corpus as it stands at a precise version.</p> <p>Further information is available from <a href="https://gitlab.com/lacito/outilspangloss">https://gitlab.com/lacito/outilspangloss</a></p> <p>---------------</p> <p>Cette archive contient un jeu de données (audios et transcriptions) d’une langue à tradition orale, le japhug (Glottocode: japh1234; code iso 639-3 le plus proche : jya). L’archive contient un sous-ensemble du corpus japhug de la collection Pangloss : c’est un corpus monolocuteur, constitué de l’intégralité des ressources audio transcrites pour la locutrice principale de ce corpus (Mme Tshendzin).<br> Le corpus est versionné, de sorte que les expériences menées sur ces ressources (pour la linguistique ou pour le Traitement automatique des langues) soient reproductibles de façon exacte (en pensant bien à joindre l’algorithme : paramètres, répartitions des fichiers dans les différents ensembles, etc.). Toutes les informations pertinentes se trouvent dans les fichiers YAML (extension .yml ; un en français, un autre en anglais).<br> Le sous-dossier des données contient d’une part les audios convertis et démultiplexés et d’autre part les annotations associées à chaque canal desdits audios.<br> Les fichiers récapitulatifs contiennent notamment la liste des graphèmes utilisés dans cette langue (les graphèmes complexes sont particulièrement importants), ainsi que des informations sur les différentes ressources (audios et annotations), comme les identifiants (DOI), les liens vers les fichiers originaux, etc.<br> Au plan informatique, la liste des identifiants DOI des audios et annotations décrits dans ce fichier YAML suffit pour générer ce corpus à un instant t. Un corpus comme celui-ci peut être vu comme la version à l’instant t d’un ensemble de documents de la collection Pangloss : un corpus arrêté à une version précise.<br> Pour plus de précisions : <a href="https://gitlab.com/lacito/outilspangloss">https://gitlab.com/lacito/outilspangloss</a></p>
Figures 9-15 from: Gates M, Buffington M (2011) Description of two techniques to increase efficiency in processing and curating minute arthropods, with special reference to parasitic Hymenoptera. Journal of Hymenoptera Research 22: 133-140. https://doi.org/10.3897/jhr.22.2099
Figures 9-15 - 9 Collection labels are removed from the sample. 10 Specimens in ethanol are poured into the filter. 11 Complete vacuum pump system. 12 Filter under vacuum. 13 Specimens dried after running the pump. 14 Specimens removed from the filter onto a mounting board. 15 Glue boards are arranged.
Figures 1-8 from: Gates M, Buffington M (2011) Description of two techniques to increase efficiency in processing and curating minute arthropods, with special reference to parasitic Hymenoptera. Journal of Hymenoptera Research 22: 133-140. https://doi.org/10.3897/jhr.22.2099
Figures 1-8 - 1 Miscellaneous vials for specimen storage 2 Ilford antistatic cloth. 3 Kinetronics antistatic brush. 4 Zerostat 3 product box. 5 Zerostat 3 gun with cap. 6 USNM ½ unit tray with plastic specimen boxes. 7 Specimens attracted to box lid by static. 8 Specimens at bottom of box lid after static dissipated.
Figure 3 from: Remsen D, Knapp S, Georgiev T, Stoev P, Penev L (2012) From text to structured data: Converting a word-processed floristic checklist into Darwin Core Archive format. PhytoKeys 9: 1-13. https://doi.org/10.3897/phytokeys.9.2770
Figure 3 - An updated database with final column titles and unique identifier added for each record.
Figure 2 from: Silva M, Rezende R, Lopes Ferreira R (2013) Detritus processing in lentic cave habitats in the neotropics. Subterranean Biology 11: 3-14. https://doi.org/10.3897/subtbiol.11.5107
Figure 2 - Remaining weight of plant disks exposed to processing in the 9 mm2 litterbag mesh size in the lentic habitats of Brega and Santuário caves. Mean, Box: Mean±SE, Whisker: Mean±SD.
Figure 1 from: Silva M, Rezende R, Lopes Ferreira R (2013) Detritus processing in lentic cave habitats in the neotropics. Subterranean Biology 11: 3-14. https://doi.org/10.3897/subtbiol.11.5107
Figure 1 - Remaining weight of plant disks exposed to processing in the 0.04 mm2 litterbag mesh size in the lentic habitats of Brega and Santuário caves. Mean, Box: Mean±SE, Whisker: Mean±SD
Figure 3 from: Silva M, Rezende R, Lopes Ferreira R (2013) Detritus processing in lentic cave habitats in the neotropics. Subterranean Biology 11: 3-14. https://doi.org/10.3897/subtbiol.11.5107
Figure 3 - Temporal values of nitrogen and phosphorous (μg/kg) measured in plant discs exposed to decomposition in lentic habitats of the Santuário and Brega caves.
Figure 2 from: Mankga L, Kowiyou Y, Moteetee A, Daru B, van der Bank M (2013) Efficacy of the core DNA barcodes in identifying processed and poorly conserved plant materials commonly used in South African traditional medicine. ZooKeys 365: 215-233. https://doi.org/10.3897/zookeys.365.5730
Figure 2 - Evaluation of barcode gaps in matK, rbcLa and rbcLa + matK for commonly used medicinal plants of South Africa. A Boxplots indicate the genetic variation between interspecific distance and intraspecific distance; the boxplots clearly shows significant differences between inter- and intraspecific distances for all gene regions tested (P < 0.001; see text) B Lineplot of the barcode gap for the commonly used plants in South African medicine. For each gene region, the grey lines correspond to the furthest intraspecific distance (bottom of line value), and the closest interspecific distance (top of line value). The red lines show where this relationship is reversed, i.e. cases where there is no barcode gap.
Figure 1 from: Mankga L, Kowiyou Y, Moteetee A, Daru B, van der Bank M (2013) Efficacy of the core DNA barcodes in identifying processed and poorly conserved plant materials commonly used in South African traditional medicine. ZooKeys 365: 215-233. https://doi.org/10.3897/zookeys.365.5730
Figure 1 - Examples ofmedicinal herbs bought at Faraday muthi market in Johannesburg A different medicinal herbs in bags B Seeds of Entada rheedii (tindili) C mixed herbs (fembo) D A twig of Adenia gummifera (mphinde umshaye) E Barks of Vachellia sp. (umkhanya-kute) F Bulb of Boophane disticha (umqotho) G mixed herbs H Myrothamnus flabellifolius (vuka) I Barks of Vachellia sp. (umkhanya-kute) J Sarcostemma viminale (ube nam) K Plant of Clivia sp. (mayime) L Stangeria eriopus (imfingo) M mixed herbs (isihlalakahle) N Tuber (umbonsi) O Helichrysum sp. (impepo) and P Twigs of Synadenium cupulare (umdletshane). Names in brackets are vernacular names in isiZulu.
Figure 2 from: Caubet Y, Richard F-J (2015) NEIGHBOUR-IN: Image processing software for spatial analysis of animal grouping. In: Taiti S, Hornung E, Štrus J, Bouchon D (Eds) Trends in Terrestrial Isopod Biology. ZooKeys 515: 173–189. https://doi.org/10.3897/zookeys.515.9390
Figure 2 - Virtual configurations used for software validation. Virtual configurations used to compile the data presented in the Table 1. Part 2.8 is one of the 10 replicates obtained with a random distribution. All other configurations have been designed in order to reach the desired level of aggregation and affinity between groups. The filled and empty shapes represented two virtual groups in the population.
Figure 1 from: Caubet Y, Richard F-J (2015) NEIGHBOUR-IN: Image processing software for spatial analysis of animal grouping. In: Taiti S, Hornung E, Štrus J, Bouchon D (Eds) Trends in Terrestrial Isopod Biology. ZooKeys 515: 173–189. https://doi.org/10.3897/zookeys.515.9390
Figure 1 - Flow chart of the creation of a new NEIGHBOUR-IN file. This figure presents the different steps in the creation of a new file, from the importation of the snapshot to the calculation of the statistics of dispersion.
Figure 4 from: Caubet Y, Richard F-J (2015) NEIGHBOUR-IN: Image processing software for spatial analysis of animal grouping. In: Taiti S, Hornung E, Štrus J, Bouchon D (Eds) Trends in Terrestrial Isopod Biology. ZooKeys 515: 173–189. https://doi.org/10.3897/zookeys.515.9390
Figure 4 - Spatial distribution in woodlice. Graphic outputs of spatial distribution patterns obtained in three configurations with monospecific or bispecific populations including two groups of eight individuals: a PD-PD: The two groups are Porcellio dilatatus (red and green) b PD-PS: Porcellio dilatatus (red) and Porcellio scaber (green) c PD-AV: Porcellio dilatatus (red) and Armadillidium vulgare (green). The outputs show 64 cells. Each cell is represented with a colour corresponding to the individual(s) in that cell. The colour is mixed using green and red proportional to the number of green and red individuals. If the cell is empty, the colour is black. The intensity of the colour reflects the number of individuals. The position of the individual is determined by its point G (centre-point).
Figure 3 from: Caubet Y, Richard F-J (2015) NEIGHBOUR-IN: Image processing software for spatial analysis of animal grouping. In: Taiti S, Hornung E, Štrus J, Bouchon D (Eds) Trends in Terrestrial Isopod Biology. ZooKeys 515: 173–189. https://doi.org/10.3897/zookeys.515.9390
Figure 3 - Aggregation heterogeneity in woodlice. Aggregation patterns of two groups of woodlice illustrating the Aggregation Heterogenity Index (AHI) and the Spatial Mixed Index (SMI). PD: Porcellio dilatatus, PS: Porcellio scaber, CC: Cylisticus convexus. Values of indexes: PD-PD: AHI=0.93 & SMI=0.80; PD-PS: AHI=0.67 & SMI=0.60; PD-CC: AHI=0.63 & SMI=0.33.
Figure 1 from: Lyal CHC (2016) Digitising legacy zoological taxonomic literature: Processes, products and using the output. In: Michel E (Ed.) Anchoring Biodiversity Information: From Sherborn to the 21st century and beyond. ZooKeys 550: 189–206. https://doi.org/10.3897/zookeys.550.9702
Figure 1 - Outline workflow to acquire, put into a suitable format, retrieve and utilize legacy literature.
3D wind velocity over a nebkha(processed)
<p>The processed data of the turbulent flow dynamics over a nebkha.</p>
Controlling grain structure in metallic additive manufacturing using a simple, inexpensive process control system
<p>Raw data associated with a paper submission.<br> "Hardness Variation in Inconel 718 Produced by Laser Directed Energy Deposition" submitted to Nature Communications.</p> <p>Contained are all the raw data used in the analysis, as well as csv's of any data plotted in graphs.</p> <p>Raw coaxial images captured during printing of various processing parameters<br> EBSD scans (.crc and .oip) of all disucssed samples, as well as matlab (MTEX) files of the EBSD map subsets<br> Raw Hardness Data<br> Position/Power logs from the build</p> <p>Samples built were:<br> Control samples - 3, 6, 10 hatches wide, with No control, Power control and Velocity control<br> Varying Thickness samples - 1, 2, 3, 4, 6, 8 hatches wide, all with constant parameters</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.