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FIGURE 5. Termite nests from site C in Termite nests in eolian backshore settings: An unusual record throughout the Quaternary in the Neotropical realm
FIGURE 5. Termite nests from site C. General view of nest C.1, next to a stream (a); it is seem a diverse vegetation close to the permanent stream, but the termite nests are restrict to grass. Detail of nest C.1 surrounded by grass (b). Detail of the inner passages of nest C.2 surrounded by grass roots (c). Schematic disposition of the termite nests in cross-section (d) and plan view (e); closest nest is approximately 170 m from the shoreline, where the nests are concentrated in the wet interdune zone.
FIGURE 1 in Termite nests in eolian backshore settings: An unusual record throughout the Quaternary in the Neotropical realm
FIGURE 1. Schematic map of studied areas (red circles). In this study only coastal occurrences of termite nests were considered. Site A: 29°23'3.26"S, 49°45'29.94"W; Site B: 30° 5'21.61"S, 50°10'9.26"W; Site C: 31° 9'34.00"S, 50°49'0.12"W; Site D: 33°43'40.44"S, 53°21'3.80"W. Modified from Google Earth.
FIGURE 1. General geological settings. 1 in Bone histology reveals the first record of titanosaur (Dinosauria: Sauropoda) from the Late Cretaceous of Bulgaria
FIGURE 1. General geological settings. 1, Tectonic map of Bulgaria (after Ivanov, 2017); 2, Geological scheme for part of the Western Srednogorie zone and position of the dinosaur bone locality (after Sinnyovsky et al., 2012; simplified).
Data Sets ''Proton Pump Inhibitor Omeprazole Alters the Spiking Characteristics of Proteinoids''
<p>Data Sets for the paper ''Proton Pump Inhibitor Omeprazole Alters the Spiking Characteristics of Proteinoids''</p>
An unambiguous POS tagging set
<p>This data set contains 1,123 short, POS-tagged sentences (extracted from an earlier data set; see https://zenodo.org/records/7694423), using the Universal tag set. The sentences can easily be POS tagged by a human tagger. However, standard POS taggers struggle with these sentences. The data file contains a header row that describes each column. The first column indicates the type of sentence (either a transcript of spoken text (0) or a sentence originating from written text (1)), the second column contains the actual sentence, with ground truth POS tags. The third column indicates the index of the mistagged token, and the remaining five columns show the tags assigned (of which at least one is a mistagging) of five different taggers.</p>
Differential abundance and gene set enrichment in plasma of cancer patients versus controls
<p>Version update: Human Protein Atlas v23 used for gene set enrichment analysis (instead of Human Protein Atlas v18)</p> <p><br>DESeq2 differential abundance output for genes with q < 0.05 and |log<sub>2</sub> fold change| > 1 in cancer vs control plasma samples:</p> <ul> <li><em>differentialabundance_pancancer.txt</em>: tables with differentially abundant genes (|log2(fold change)|>1 and adjusted p>0.05) per cancer-control comparison (cancertype) in a pan-cancer plasma sample cohort (25 locally advanced to metastatic cancer types - 7 or 8 patients per type - vs 8 cancer-free control donors)</li> <li><em>differentialabundance_threecancer.txt</em>: tables with differentially abundant genes (|log2(fold change)|>1 and adjusted p>0.05) per cancer-control comparison (cancertype) in the three-cancer plasma cohort (ovarian, prostate and uterine cancer - 11 or 12 patients per type - vs 20 cancer-free controls) <ul> <li>Gene_id: Ensembl gene id (GChr38 v91); baseMean: mean of normalized counts for all samples; log2FoldChange: log2 fold change for cancer vs control; lfcSE: standard error for cancer vs control; stat: Wald statistic for cancer vs control; pvalue: Wald test p-value for cancer vs control; padj: Benjamini-Hochberg corrected p-value; cancertype: respective cancer type abbreviation of cancer patient plasma samples that were compared to plasma samples of controls.</li> </ul> </li> </ul> <p>Gene set enrichment analyses based on fold change ranked gene lists (cancer versus control) - results obtained with fgea (v1.22.0):</p> <ul> <li><em>customgenesets.txt</em>: custom gene set lists based on RNA Atlas (&Human Protein Atlas), Tabula Sapiens, GTEX, TCGA data. <ul> <li>Reference: reference to create gene sets (including RNA Atlas, Human Protein Atlas, Tabula Sapiens, GTEX, and TCGA); set: set name; genes: gene list for set</li> </ul> </li> <li><em>GSEA_pancancer.txt</em> & <em>GSEA_threecancer.txt</em>: gene set enrichment results based on fold change ranked gene list (specific cancer type versus controls) in pan-cancer cohort and three-cancer cohort, respectively <ul> <li>Sets: gene set category (HALLMARK and KEGG: Hallmark and Canonical Pathways gene sets obtained from MSigDB (v2022.1); CUSTOM: custom tissue and cell type specific gene sets as defined in <em>customgenesets.txt)</em>; pathway: pathway/set name; pval: enrichment p-value; padj: Benjamini-Hochberg adjusted p-value; log2err: expected error for the standard deviation of the P-value logarithm; ES: enrichment score, same as in Broad GSEA implementation; NES: enrichment score normalized to mean enrichment of random samples of the same size; size: size of the pathway after removing genes without statistic values; leadingEdge: leading edge genes that drive the enrichment; Disease: respective cancer type abbreviation of cancer patient plasma samples that were compared to plasma samples of controls</li> </ul> </li> </ul>
Data set (traces) for Software implementations of Multiplication Gadgets: SEAL
<p>This file holds the power-consumption traces in our experiments (using SCALE Board) regarding:<br>"Towards secure software implementations of masking schemes (Multiplication gadgets) in Assembly for ARM Cortex-M3".<br><br>- Format of traces: TRS<br>- Traces for: SNR (random inputs), t-test<br><br></p> <p> </p> <p> </p>
Data set, Model, and Catalog for: Data-driven stellar intrinsic colors and dust reddenings for spectro-photometric data
<p>Intrinsic colors of stars are essential for the studies on both stellar physics and dust reddening. In this work, we developed an XGBoost model to predict the stellar intrinsic colors with the atmospheric parameters, Teff , log g, and [M/H], which is an improvement of the widely used blue-edge method. The dust reddening toward each line-of-sight can then be calculated by the observed colors minus the derived intrinsic colors.</p> <p>Here we provide the related data sets:</p> <ul> <li>xgb_model.pkl: the trained XGBoost model.</li> <li>use_xgb.py: a simple script showing how to use the XGBoost model to predict intrinsic colors.</li> <li>data_set.fits: this fits file contains the training and test sets, separated into four data arrays: <ul> <li>X_train: X-data (teff,logg,mh) of the training set.</li> <li>X_test: X-data (teff,logg,mh) of the test set.</li> <li>y_train: y-data (BP-RP, BP-Ks, J-Ks) of the training set.</li> <li>y_test: y-data (BP-RP, BP-Ks, J-Ks) of the test set.</li> </ul> </li> <li>IC_catalog.csv: a catalog containing a representative set of intrinsic colors at three bands ('BPRP0', 'BPK0', and 'JK0' in columns) as a function of typical Teff, logg, and [M/H] ('teff', 'logg', and 'mh' in columns).</li> </ul> <p>With the above data and model, users can apply the trained XGBoost to new sources to predict their intrinsic colors and calculate their dust reddenings. One can further control the quality of the prediction by selecting training-like sources with the training set and estimating the <a>generalization error by the test set.</a></p>
X-ray diffraction data set for PDB 9G3L: LecB from PA01 in complex with beta-fucosylamide-indole derivative
<p>X-ray diffraction images collected on proxima 1 Soleil the 7th of march 2024 at SOLEIL synchrotron, Saint Aubin, France for PDB ID 9G3L using a DECTRIS EIGER X 16M detector. X-ray dataset and xdsme processing for the structure of LecB from <em>Pseudomonas aeruginosa</em> PA01 strain in complex with synthetic beta-fucosylamide-indole derivative. Images 1-900 were removed during processing and resolution was cut to 1.74 angstrom.</p>
Data Sets "Modulation of electrical activity of proteinoid microspheres with chondroitin sulfate clusters"
<p>Data Sets "Modulation of electrical activity of proteinoid microspheres with chondroitin sulfate clusters"</p>
STILT footprints data set 1
<p>This repository contains the first batch of training data sets of measurement footprints.</p> <p>The footprints are used to train the deep learning model presented in our paper titled "FootNet v1.0: Development of a machine learning emulator of atmospheric transport".</p> <p>Preprint of the manuscript could be accessed at https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1526/.</p> <p>The footprints are provided in Numpy compressed array format, which could be decompressed with Python 3.10.6 and NumPy 1.23.4.</p>
Data set for "Bidirectional microwave-optical transduction based on integration of high-overtone bulk acoustic resonators and photonic circuits"
<p>The repository contains raw data, processing scripts, simulation and GDS files for the manuscript "Bidirectional microwave-optical transduction based on integration of high-overtone bulk acoustic resonators and photonic circuits". For detailed usage instructions, please take a look at the README.txt file. </p>
colorSV translocation call sets on COLO829 and HCC1395
<p>Translocation call sets generated by colorSV (v0.1.0) on the COLO829 and HCC1395 cell lines as described in the <a href="https://doi.org/10.1101/2024.07.29.605160">colorSV paper</a>.</p>
Data sets of multi-body models for conventional, articulated, and equidistant-axles trains and single span bridges
<p>Information about characteristics of multi-body models of conventional, articulated, and equidistant-axles trains.</p> <p>Information about bridge data set, used for vehicle-bridge interaction investigations.</p>
Helsinki-NLP/WMT16-test-enfi: WMT16 test set for Finnish-English
<p>This package provides the test set for the news translation task at WMT 2016 for Finnish-English and the machine-translated submissions by the University of Helsinki.</p>
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 8. Performance in 1st Approach for three data set
<p>This work also deals with classification of multi class images under different constraints of<br> data set. The first experiment is carried out on images without noise, second with Gaussian noise<br> and filtered data set in third experiment. Performance of the classifier using statistical texture<br> features for two approaches are presented in the table 3. It is observed that performance n the first<br> experiment is best in the first data set i.e. data set without noise in both the approach, while the<br> performance is decreased if the same images are affected by Gaussian noise.</p>
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 9. Performance in 2nd Approach for three data set
<p>This work also deals with classification of multi class images under different constraints of<br> data set. The first experiment is carried out on images without noise, second with Gaussian noise<br> and filtered data set in third experiment. Performance of the classifier using statistical texture<br> features for two approaches are presented in the table 3. It is observed that performance n the first<br> experiment is best in the first data set i.e. data set without noise in both the approach, while the<br> performance is decreased if the same images are affected by Gaussian noise. This is because the<br> texture feature of the original images consists Gaussian pattern also. Filtering of the noise from the<br> second data set improves the result. The table also shows that feature extraction using blocking of<br> the image enhance the average classification rate in all the case.</p>
Figure 1. (a) Classical set and (b) Fuzzy set 2.3.-An Efficient Expert System Generator for Qualitative Feed-Back Loop Analysis
<p>A membership function is a curve that represents the degree of points which belong to the<br> specific fuzzy variable. Selecting the appropriate membership function plays an essential rule in<br> design of a fuzzy logic controller. The shape of membership function could be defined based on the<br> simplicity, convenience, speed and efficiency. Many different membership functions are introduced<br> in the literatures such as triangular, trapezoidal and Gaussian. The membership function which<br> represented in figure 1(b) is a trapezoidal type.</p>
Data set for Plos One Article "Force sharing and other collaborative strategies in a dyadic force perception task"
<p>Data set for Plos One Article :</p> <p>Tatti, F., Baud-Bovy G. (2018) "Force sharing and other collaborative strategies in a dyadic force perception task". doi: 10.1371/journal.pone.0192754</p> <p>This study investigates how people might interact to extract information from the forces experienced while holding an object together. More specifically, the dyads (i.e. pairs formed two persons) participating to the study had to identify the direction of a small force applied to a jointly held object by a haptic device. This study included a condition where each participant responded independently and another one where the two participants had to agree upon a single negotiated response.</p> <p>The dataset (data.csv) contains the force produced by the haptic device and the average and standard deviation of the interaction force for all trials together with the responses of the participants. We also included the initial and final position of the haptic device and total distance traveled for each trial.</p> <p>The data are in comma separated text format and its description in a PDF document (readme.pdf).</p>
Figure 2. Correct Classified Instances for different data sets-Intelligent System for Diagnosis of a Three-Phase Separator
<p>The data mining models may be considered a superior</p> <p>technique that may be successful</p> <p>applied in diagnosis and may be develop in the futu</p> <p>re on the base of more training data to increase</p> <p>the accuracy of results.</p> <p>Industrial processes are dynamic processes with ran</p> <p>dom behavior and whose evolution over</p> <p>time cannot be predicted unless it is well known th</p> <p>e process model and use advanced predictive</p> <p>techniques. Consequently, design and implement an a</p> <p>utomated online monitoring and diagnosis</p> <p>three-phase separator remains a future direction of</p> <p>research conducted so far.</p> <p>Conceptually, this system should have permanent acc</p> <p>ess to data collected from field</p> <p>transducers, to be able to identify the type of fau</p> <p>lt occurred, to locate the fault and provide</p> <p>recommendations to remedy abnormal operating condit</p> <p>ion. Also, updating the database defects with</p> <p>new types of defects occurred and the adequate solu</p> <p>tions adopted for eliminating errors in the</p> <p>operating mode is an important feature to be consid</p> <p>ered during the design of the online diagnosis</p> <p>system. This is possible if the system would have s</p> <p>elf-learning capabilities. To acquire this "skill",</p> <p>the automatic online diagnosis system may contain a</p> <p>diagnosis module based on artificial neural</p> <p>networks.</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.