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13,587 results for “specification”
FIGURE 4 in Reinstatement of the independent specific status of Thalictrum hamatum (Ranunculaceae), with T. macrorhynchum reduced to its synonymy
FIGURE 4. Specimens of Thalictrum uncatum. A. CHINA. Gansu: between Mör-ping and Wu-ping, G.N. Potanin s.n. (LE, lectotype). B. Same locality, G.N. Potanin s.n. (PE, syntype). C. CHINA. Xizang: Qamdo, B.Q. Xu et al. 177 (IBSC). D. CHINA. Sichuan: Xiangcheng, D.E. Boufford et al. 28389 (PE).
FIGURE 3 in Reinstatement of the independent specific status of Thalictrum hamatum (Ranunculaceae), with T. macrorhynchum reduced to its synonymy
FIGURE 3. Specimens of Thalictrum macrorhynchum (= T. hamatum). A. CHINA. Beijing: E. Bodinier 60 (P, lectotype). B. Same locality, E. Bodinier 60 (P, isolectotype). C. Same locality, E. Bodinier 60 (E, isolectotype). D. CHINA. Henan: Neixiang, D.E. Boufford et al. 26328 (E).
FIGURE 2. A in Reinstatement of the independent specific status of Thalictrum hamatum (Ranunculaceae), with T. macrorhynchum reduced to its synonymy
FIGURE 2. A specimen of Thalictrum uncatum, G.N. Potanin s.n. (PE), which was misidentified as T. hamatum by Maximowicz on the determination slip, and mistaken as a syntype of this name by Wang (1980), and wrongly designated as its lectotype by Ban et al. (2017).
FIGURE 1 in Reinstatement of the independent specific status of Thalictrum hamatum (Ranunculaceae), with T. macrorhynchum reduced to its synonymy
FIGURE 1. Lectotype (G.N. Potanin s.n., LE01014044) and syntype (G.N. Potanin s.n., LE01014043) of Thalictrum hamatum.
FIGURE 5 in Reinstatement of the independent specific status of Thalictrum hamatum (Ranunculaceae), with T. macrorhynchum reduced to its synonymy
FIGURE 5. Achenes in Thalictrum hamatum (A, B) and T. uncatum (C). A. CHINA. Sichuan: valley of the Honton River, G.N. Potanin s.n. (LE, syntype). B. CHINA. Shaanxi: Ningshan, X.Q. Guo 190 (IBSC). C. CHINA. Xizang: Qamdo, B.Q. Xu et al. 177 (IBSC).
FIGURE 1 in Acantholimon davisii (Plumbaginaceae), a new specific name for the Turkish endemic A. caryophyllaceum subsp. parviflorum
FIGURE 1. Acantholimon davisii Akaydın & M.B.Crespo in habitat, near the type locality (A5 Sinop, between Hanönü and Boyabat, around Salar village, 330 m, 15 July 2001, G. Akaydın 7076).
Datasets for Target-Specific De Novo Peptide Binder Design with DiffPepBuilder
<p>PepPC-F and PepPC Datasets for Target-Specific De Novo Peptide Binder Design with DiffPepBuilder</p>
Supplementary tables for: CHAS, a deconvolution tool, infers cell type-specific signatures in bulk brain histone acetylation studies of brain disorders
<p>Supplementary tables for: CHAS, a deconvolution tool, infers cell type-specific signatures in bulk brain histone acetylation studies of brain disorders. </p> <p>Supplementary Table 1: S1_AD_H3K27ac_Marzi2018_CHAS_analysis.xls, includes the sample metadata, differentially acetylated regions when controlling for CHAS scores and CHAS-MF cell type proportions, GO enrichment analysis results using the two sets of differentially acetylated regions. </p> <p>Supplementary Table 2: S2_PD_H3K27ac_Toker2022_CHAS_analysis.xls, includes the sample metadata, differentially acetylated regions when controlling for CHAS scores and CHAS-MF cell type proportions.</p> <p>Supplementary Table 3: S3_ASD_H3K27ac_Sun2016_CHAS_analysis.xls, includes the sample metadata, differentially acetylated regions when controlling for CHAS scores and CHAS-MF cell type proportions for the prefrontal cortex and the cerebellum, GO enrichment analysis results using the two sets of differentially acetylated regions in the prefrontal cortex. </p> <p>Supplementary Table 4: S4_SCZ_BPD_H3K27ac_Girdhar2022_CHAS_analysis.xls, includes the sample metadata, differentially acetylated regions when controlling for CHAS scores and CHAS-MF cell type proportions, GO enrichment analysis results using the two sets of differentially acetylated regions. </p> <p> </p> <p> </p> <p> </p> <p> </p>
Quantitative real-time PCR assays Q2 for species-specific detection and quantification of Baltic Sea spring bloom dinoflagellates
<p>These are the data behind figures 2 to 7 in the paper: Brink AM, Kremp A and Gorokhova E (2024) Quantitative real-time PCR assays for species-specific detection and quantification of Baltic Sea spring bloom dinoflagellates. Front. Microbiol. 15:1421101. doi: 10.3389/fmicb.2024.1421101</p>
Multi-omics with dynamic network biomarker algorithm prefigures organ-specific metastasis of lung adenocarcinoma
<p><span>Efficacious strategies for early detection of lung cancer metastasis are of significance for improving the survival of lung cancer patients. Utilizing two clinical cohorts of four major types of lung cancer distant metastases, with single-cell RNA sequencing (scRNA-seq) of primary lesions and liquid chromatography mass spectrometry data of sera, we identified the marker genes and serum secretome foreshadowing the lung cancer site-specific metastasis through dynamic network biomarker (DNB)</span> <span>algorithm. Also, we located the intermediate status of cancer cells, along with its gene signatures, in each metastatic state trajectory that cancer cells at this stage still had no specific organotropism. Furthermore, an integrated neural network model based on the filtered scRNA-seq data was successfully constructed and validated to predict the metastatic state trajectory of cancer cells. Overall, our study provided a new insight to locate the pre-metastasis status of lung cancer and primarily examined its clinical application value, contributing to the early detection of lung cancer metastasis in a more feasible and efficacious way.</span></p>
Spring vegetation dynamics drive sex-specific migration propensity and fine-scale green-up tracking in Alpine ibex
<p># Data and srcipts used to perform analyses presented in the manuscript: "Spring vegetation dynamics drive sex-specific migration propensity and fine-scale green-up tracking in Alpine ibex"</p>
Label-Free Quantification (LFQ) LC-MS Data for the Profiling of Domain-specific VCP/p97 Interactions in Living Cells
<p>This deposit includes LFQ LC-MS data for tandem-IP samples from VCP-L278AbK and VCP-D592AbK crosslinking experiments, their VCP-L278BocK and VCP-D592BocK controls, and SpectroMine output files.</p>
Class-specific data augmentation for plant stress classification
<p>This is a companion dataset for the paper titled "<strong>Class-specific data augmentation for plant stress classification</strong>" by Nasla Saleem, Aditya Balu, Talukder Zaki Jubery, Arti Singh, Asheesh K. Singh, Soumik Sarkar, and Baskar Ganapathysubramanian published in <em>The Plant Phenome Journal</em>, https://doi.org/10.1002/ppj2.20112<br><br><br><strong>Abstract:</strong><br><br>Data augmentation is a powerful tool for improving deep learning-based image classifiers for plant stress identification and classification. However, selecting an effective set of augmentations from a large pool of candidates remains a key challenge, particularly in imbalanced and confounding datasets. We propose an approach for automated class-specific data augmentation using a genetic algorithm. We demonstrate the utility of our approach on soybean [<em>Glycine max (L.) Merr</em>] stress classification where symptoms are observed on leaves; a particularly challenging problem due to confounding classes in the dataset. Our approach yields substantial performance, achieving a mean-per-class accuracy of 97.61% and an overall accuracy of 98% on the soybean leaf stress dataset. Our method significantly improves the accuracy of the most challenging classes, with notable enhancements from 83.01% to 88.89% and from 85.71% to 94.05%, respectively. A key observation we make in this study is that high-performing augmentation strategies can be identified in a computationally efficient manner. We fine-tune only the linear layer of the baseline model with different augmentations, thereby reducing the computational burden associated with training classifiers from scratch for each augmentation policy while achieving exceptional performance. This research represents an advancement in automated data augmentation strategies for plant stress classification, particularly in the context of confounding datasets. Our findings contribute to the growing body of research in tailored augmentation techniques and their potential impact on disease management strategies, crop yields, and global food security. The proposed approach holds the potential to enhance the accuracy and efficiency of deep learning-based tools for managing plant stresses in agriculture.</p>
Novel sex-specific genes and diverse interspecific expression in the antennal transcriptomes of ithomiine butterflies"
<p>The following repository contains both the genomic and functional annotations for 4 Ithomiini species. Data that was used in GBE paper "Novel sex-specific genes and diverse interspecific expression in the antennal transcriptomes of ithomiine butterflies".</p>
scCTS: identifying the cell type-specific marker genes from population-level single-cell RNA-seq
<p>Single cell RNA-sequencing (scRNA-seq) provides gene expression profiles of individual cells from complex samples, facilitating the detection of cell type-specific marker genes. In scRNA-seq experiments with multiple donors, the population level variation brings an extra layer of complexity in cell type-specific gene detection, for example, they may not appear in all donors. Motivated by this observation, we develop a statistical model named scCTS to identify cell type-specific genes from population-level scRNA-seq data. Extensive data analyses demonstrate that the proposed method identifies more biologically meaningful cell type-specific genes compared to traditional methods.</p>
Do AI assistants help students write formal specifications? A study with ChatGPT and the B-Method
<p>Replication package of the paper: "Do AI assistants help students write formal specifications? A study with ChatGPT and the B-Method." <br>Submitted to the 37th edition of the IEEE Conference on Software Engineering Education and Training (CSEE&T), co-located with ICSE 2025. </p>
Using Multi-Modal Path-Specific Transit Trips in Transportation Social Sustainability Analysis: Case Study in Atlanta, GA
Open the record for dataset details and reuse information.
Sequence-specific, mechanophore-free mechanochemistry of DNA
<p>Original Data underpinning Figures, Schemes, and Tables of the Manuscript and Supplementary Information.</p>
Data of Le Crom and Boily 2024: Adsorption of ciprofloxacin on goethite: I. Approach and non-specific binding
<p><span><span><span>Simulation input files, data and scripts that were used for the article Le Crom and Boily 2024: Molecular-level insight into the adsorption of ciprofloxacin on goethite: I. Approach and non-specific binding</span></span></span></p>
Treg Specificity
<p>Revision imaging data with associated analysis code (https://github.com/npagane/treg_specificity). </p>
ScienceDex guides
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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.