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9,882 results for “Scale”
Microbial autotrophy explains large-scale soil CO2 fixation
<p><span>Microbial communities play critical roles in fixing carbon from the atmosphere and fixing it in the soils. However, the large-scale variations and drivers of these microbial communities remain poorly understood. Here, we conducted a </span><span>large-scale</span><span> survey across China and found that soil autotrophic organisms are critical for explaining CO2 fluxes from the atmosphere to soils. In particular, we showed that large-scale variations in CO2 fixation rates are highly correlated to those in autotrophic bacteria and phototrophic protists. Paddy soils, supporting a larger proportion of obligate bacterial and protist autotrophs, display four-fold of CO2 fixation rates over upland and forest soils. Precipitation and pH, together with key ecological clusters of autotrophic microbes, also played important roles in controlling CO2 fixation. Our work provides a novel quantification on the contribution of terrestrial autotrophic microbes to soil CO2 fixation processes at a </span><span>large </span><span>scale, with implications for global carbon regulation under climate change.</span></p>
Scalable mixed model approaches for set-based association studies on large-scale categorical data analysis and its application to 450k exome sequencing data in UK Biobank
<p>The ongoing release of large-scale sequencing data in the UK Biobank allows for identifying associations between rare variants and complex traits. SAIGE-GENE+ is a valid approach to conducting set-based association tests for quantitative and binary traits. However, for ordinal categorical phenotypes, applying SAIGE-GENE+ with treating the trait as quantitative or binarizing the trait can cause inflated type I error rates or power loss. In this study, we propose a novel method for rare-variant association tests, POLMM-GENE, in which a proportional odds logistic mixed model was used to characterize ordinal categorical phenotypes while adjusting for sample relatedness. POLMM-GENE fully utilizes the categorical nature of phenotypes and thus can well control type I error rates while remaining powerful. In the analyses of UK Biobank 450k whole exome-sequencing data for 5 ordinal categorical traits, POLMM-GENE identified 54 gene-phenotype associations.</p>
Dataset used for paper "Evaluating simplifications of subsurface process representations for field-scale permafrost hydrology models"
<p>No description provided.</p>
OQM9HK: A Large-scale Graph Dataset for Machine Learning in Materials Science
<p>This is a large-scale graph dataset of materials science based on the Open Quantum Materials Database (OQMD) v1.5 .</p> <p><a href="https://storage.googleapis.com/rimcs_cgnn/oqm9hk_dataset_Sep_30_2022.pdf" target="_blank" rel="noopener">Technical Report</a></p> <p><a href="https://www.rimcs.co.jp" target="_blank" rel="noopener">RIMCS Website</a></p> <p><strong>Data Loading</strong></p> <p>A Python code example:</p> <pre><code>import sys sys.path.append('/your/path/to/data/OQM9HK_BEL') import OQM9HK bel_path='/your/path/to/data/OQM9HK_BEL' config = OQM9HK.load_config(path=bel_path) print(config['atomic_numbers']) split = OQM9HK.load_split(path=bel_path) print(len(split['train']), len(split['val']), len(split['test'])) graph_data = OQM9HK.load_graph_data(path=bel_path) name = next(iter(graph_data)) # Frist entry's name graph = graph_data[name] # Graph object print(graph.nodes) print(graph.edge_sources) print(graph.edge_targets) dataset = OQM9HK.load_targets(path=bel_path) # Pandas dataframe print(dataset) train_set = dataset.iloc[split['train']] val_set = dataset.iloc[split['val']] test_set = dataset.iloc[split['test']]</code></pre> <p> </p>
Machine learning on large-scale proteomics data identifies tissue- and cell type-specific proteins
<p>Using data from 183 public human data sets from PRIDE, a machine learning model was trained to identify tissue and cell-type specific protein patterns. PRIDE projects were searched with ionbot and tissue/cell type annotation was manually added. Data from physiological samples were used to train a Random Forest model on protein abundances to classify samples into tissues and cell types. Subsequently, a one-vs-all classification and feature importance were used to analyse the most discriminating protein abundances per class. Based on protein abundance alone, the model was able to predict tissues with 98% accuracy, and cell types with 99% accuracy. The F-scores describe a clear view on tissue-specific proteins and tissue-specific protein expression patterns. In-depth feature analysis shows slight confusion between physiologically similar tissues, demonstrating the capacity of the algorithm to detect biologically relevant patterns. These results can in turn inform downstream uses, from identification of the tissue of origin of proteins in complex samples such as liquid biopsies, to studying the proteome of tissue-like samples such as organoids and cell lines</p>
Unravelling mechanisms of protein and lipid oxidation in mayonnaise at multiple length scales
<p>raw data</p>
FIGURE 7 in Recent adventive soft scale insects (Hemiptera: Coccomorpha: Coccidae) and mealybugs (Hemiptera: Coccomorpha: Pseudococcidae) in India
FIGURE 7. Antonina thaiensis Takahashi, diagnostic characters of slide-mounted adult female. a. Antenna; b. Anal ring; c. Vulva with heavily sclerotized apophyses; d. Dorsal setae; e. Marginal conical setae; f. Multilocular disc pores; g. Trilocular pores; h. Discoidal pores; i. Large-type tubular ducts; j. Small-type tubular ducts; k. Ventral setae; l. Longer ventral setae on posterior abdominal segments; m. Disc-like pores; n. Multilocular pores posterior to vulva; o. Multilocular pores on margin and submargin, and ventral tubular ducts; p. Ventral discoidal pores; q. Female habitus.
FIGURE 3 in Recent adventive soft scale insects (Hemiptera: Coccomorpha: Coccidae) and mealybugs (Hemiptera: Coccomorpha: Pseudococcidae) in India
FIGURE 3. Kilifia deltoides De Lotto, diagnostic characters of slide mounted adult female. a. Marginal setae; b. Spiracular setae; c. Dorsal setae and dark-rimmed pores; d. Close up of dark-rimmed pore showing slit-like opening; e. Preopercular pores; f. Submarginal tubercle; g. Anal plate with longer anterolateral margin; h. Setae on dorsal surface of anal plate; i. Setae on anterior margin of anogenital fold; j. Setae on lateral margin of anogenital fold; k. Pregenital disc pores; l. Spiracular pores; m. Leg showing coxal cavity; n. Claw digitules and tarsal digitules; o. Antenna; p. Ventral microducts around labium; q. Female habitus.
FIGURE 6 in Recent adventive soft scale insects (Hemiptera: Coccomorpha: Coccidae) and mealybugs (Hemiptera: Coccomorpha: Pseudococcidae) in India
FIGURE 6. Fistulococcus pokfulamensis Hodgson & Martin; host plants: a. Heptapleurum actinophyllum; b. Syzygium cumini; c. Mangifera indica; d. Vaccinium corymbosum. e. An ant, Oecophylla smaragdina (Fabricius), attending F. pokfulamensis. Natural enemies of F. pokfulamensis: f. Chilocorus nigrita (Fabricius); g. Cryptolaemus montrouzieri Mulsant grub; h. C. montrouzieri adult; i. Spalgis epius (Westwood) larva. Life stages of the predator Dipha aphidivora Meyrick: j. Larva feeding from within the gallery; k. Larva removed from the gallery; l. Pupa removed from the cocoon; m. Adult. Other natural enemies recorded: n. Microterys imphalensis Singh & Hayat, a parasitoid of Antonina thaiensis Takahashi; o. Crematogaster rogenhoferi Mayr, an ant attending Formicococcus simplicior (Green).
FIGURE 5 in Recent adventive soft scale insects (Hemiptera: Coccomorpha: Coccidae) and mealybugs (Hemiptera: Coccomorpha: Pseudococcidae) in India
FIGURE 5. Fistulococcus pokfulamensis Hodgson & Martin, diagnostic characters of slide-mounted adult female. a. Marginal seta; b. Highly sclerotized spiracular cleft without spiracular setae; c. Dermal areolation; d. Dorsal setae; e. Small microductule; f. Concave pores; g. Preopercular pores; h. Submarginal chambered ducts; i. Anal plates; j. Setae on dorsal surface of anal plate; k. Setae on anterior margins of anogenital fold; l. Setae on lateral margin of anogenital fold; m. Preanal disc pores; n. Antenna six segmented; o. Spiracle; p. Segmentation between tibia and tarsus unclear; q. Female habitus.
FIGURE 2 in Recent adventive soft scale insects (Hemiptera: Coccomorpha: Coccidae) and mealybugs (Hemiptera: Coccomorpha: Pseudococcidae) in India
FIGURE 2. Maacoccus piperis (Green), diagnostic characters of slide-mounted adult female. a. Body derm; b. Marginal setae; c. Stigmatic setae; d. Anal plate; e. Preopercular pores; f. Dorsal seta; g. Preopercular pores; h. Anogenital fold; i. Anal ring; j. Ventral setae on pregenital segments; k. Submarginal setae; l. Spiracle; m. Spiracular pores; n. Hind leg; o. Antenna; p. Ventral microduct; q. Ventral tubular ducts.
FIGURE 4 in Recent adventive soft scale insects (Hemiptera: Coccomorpha: Coccidae) and mealybugs (Hemiptera: Coccomorpha: Pseudococcidae) in India
FIGURE 4. Paralecanium machili Takahashi, diagnostic characters of slide-mounted adult female. a. Marginal setae; b. Spiracular setae; c. Eyes; d. Dermal areolation; e. Clear areas on derm; f. Dorsal setae; g. Dorsal large pores with sclerotized margin; h. Dorsal small pores; i. Anal plates; j. Setae on anterior margin of anogenital fold; k. Setae on lateral margins of anogenital fold; l. Pregenital disc pores; m. Spiracular pores; n. Leg without tibiotarsal sclerosis; o Antenna; p. Anal ring; q. Female habitus.
FIGURE 8 in Recent adventive soft scale insects (Hemiptera: Coccomorpha: Coccidae) and mealybugs (Hemiptera: Coccomorpha: Pseudococcidae) in India
FIGURE 8. Formicococcus simplicior (Green), diagnostic characters of slide-mounted adult female. a. Anal lobes; b. Anal lobe bar and apical seta; c. Antenna; d. Well-developed leg; e. Claw stout and curved; f. Translucent pores on hind coxa; g. Circulus; h. Ostiole; i. Anal lobe cerarius; j. Penultimate cerarius; k. Preocular cerarius; l. Frontal cerarius; m. Dorsal setae; n. Ventral setae; o. Multilocular pores; p. Oral collar tubular ducts; q. Female habitus.
The Early Functional Abilities-revised may bridge the gap between the disorder of consciousness and the functional independence scales: evidence from Rasch analysis
<p>Raw data associated with the scientific publication “The Early Functional Abilities-revised may bridge the gap between the disorder of consciousness and the functional independence scales: evidence from Rasch analysis”</p>
J1/LMCU Scaling MD Traj b
<p>J1/LMCU Scaling MD Traj b</p> <p>With AMBER topology file and NetCDF trajectories. Scaling factor is labelled in the name.</p>
J1/LM Scaling MD Traj b
<p>J1/LM Scaling MD Traj b</p> <p>With AMBER topology file and NetCDF trajectories. Scaling factor is labelled in the name.</p>
J1/LMCU Scaling MD Traj c
<p>J1/LMCU Scaling MD Traj c</p> <p>With AMBER topology file and NetCDF trajectories. Scaling factor is labelled in the name.</p>
J1/LMCU Scaling MD a
<p>J1/LMCU Scaling MD a</p> <p>With AMBER topology file and NetCDF trajectories. Scaling factor labelled in the name.</p>
"Software is the easy part of Software Engineering" - Lessons and Experiences from A Large-Scale, Multi-Team Capstone Course
<p>Our data contains the peer review form that students used to assess each member in a sub-team. Additionally, we included the project description for the capstone project. Please note that our domain experts provided additional in person requirements that students elicited for the project. However, this project description can serve as a good starting point for determining the primary project criteria. Finally, we include a sample software requirements specification that we provided for our students to use for their initial requirements specification. </p>
How to enhance the inverse distance weighting method to detect the precipitation pattern in a large-scale watershed
<p>These datasets provide precipitation data over Central Plateau watershed of Iran. Four different variants of the Inverse Distance Weighting (IDW) method are utilized to create these datasets. Two out of four IDW variants are proposed and developed by the authors to enhance the performance of the available standard models.</p> <p> </p> <p>Data Format: xlsx</p> <p>Spatial Resolution: ~0.08˚ & 0.25˚</p> <p>Spatial Coverage: Central Plateau watershed, Iran (48˚07'E to 61˚25'E - 26˚33'N to 37˚27'N)</p> <p>Temporal Resolution: Monthly</p> <p>Temporal Coverage: 2005 - 2015 (Inclusively)</p> <p> </p> <p><strong>How to cite:</strong></p> <p><strong>Arash Ghomlaghi, Mohsen Nasseri & Bardia Bayat (2022): How to enhance the inverse distance weighting method to detect the precipitation pattern in a large-scale watershed, Hydrological Sciences Journal, DOI: 10.1080/02626667.2022.2124874</strong></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.