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318 results for “Data mining”
Multiway Registration Capability Study in Increasing the Accuracy of Registration Results for Infrastructure and Mining Pits Terrestrial Laser Scanner (TLS) Data Point Cloud
<p>This material has presented on 2nd International Conference on Advanced Research in Engineering and Technology in October 25, 2023.</p>
Characterization of Road Condition with Data Mining Based on Measured Kinematic Vehicle Parameters, Data
<p>Data for Paper "Characterization of Road Condition with Data Mining Based on Measured Kinematic Vehicle Parameters"</p>
Data from: Effects of apical meristem mining on plant fitness, architecture and flowering phenology in Cirsium altissimum (Asteracaeae)
Premise of the study: Interactions that limit lifetime seed production have the potential to limit plant population sizes and drive adaptation through natural selection. Effects of insect herbivory to apical meristems (apical meristem mining) on lifetime seed production rarely have been quantified experimentally. We studied Cirsium altissimum (tall thistle), whose meristems are mined by Platyptilia carduidactyla (artichoke plume moth), to determine how apical damage affects plant maternal fitness and evaluate both direct and indirect mechanisms underlying these effects. Methods: In restored prairie, apical mining was manipulated on tall thistles by applying insecticide, water, or no spray to apical meristems. We quantified effects on lifetime seed production, plant architecture, and flowering phenology. Seed germinability and seedling mass were evaluated in a greenhouse. Key results: Apical meristem miners decreased lifetime seed production of C. altissimum, but not seed quality. Higher mortality rates of damaged plants contributed to reduced seed production. Apical damage reduced plant height and increased the proportion of blooming flower heads in axial positions on branches. Apical damage delayed flowering and shortened flowering duration. Conclusions: Apical meristem mining reduced plant maternal fitness. The shift in the identity of blooming flower heads from terminal to axial positions contributed to this reduction because axial heads are less fecund. Shorter, meristem-mined plants may have been more susceptible to competition, and this susceptibility may explain their higher mortality rates. The kinds of changes in architecture and phenology that resulted from apical damage to C. altissimum have been shown to affect floral visitation in other plant species.
Data from: Short-term microbial effects of a large-scale mine-tailing storage facility collapse on the local natural environment
We investigated the impacts of the Mount Polley tailings impoundment failure on chemical, physical, and microbial properties of substrates within the affected watershed, comprised of 70 hectares of riparian wetlands and 40 km of stream and lake shore. We established a biomonitoring network in October of 2014, two months following the disturbance, and evaluated riparian and wetland substrates for microbial community composition and function via 16S and full metagenome sequencing. A total of 234 samples were collected from substrates at 3 depths and 1,650,752 sequences were recorded in a geodatabase framework. These data revealed a wealth of information regarding watershed-scale distribution of microbial community members, as well as community composition, structure, and response to disturbance. Substrates associated with the impact zone were distinct chemically as indicated by elevated pH, nitrate, and sulphate. The microbial community exhibited elevated metabolic capacity for selenate and sulfate reduction and an abundance of chemolithoautotrophs in the Thiobacillus thiophilus/T. denitrificans/T. thioparus clade that may contribute to nitrate attenuation within the affected watershed. The most impacted area (a 6km stream connecting two lakes) exhibited 30% lower microbial diversity relative to the remaining sites. The tailings impoundment failure at Mount Polley Mine has provided a unique opportunity to evaluate functional and compositional diversity soon after a major catastrophic disturbance to assess metabolic potential for ecosystem recovery.
Data from: Molecular phylogeny, revised higher classification, and implications for conservation of endangered Hawaiian leaf-mining moths (Lepidoptera: Gracillariidae: Philodoria)
The leaf-mining moth genus Philodoria Walsingham (Lepidoptera: Gracillariidae) is composed of 30 described species, all of which are endemic to the Hawaiian Islands. Philodoria is known to feed on 10 families of endemic Hawaiian host plants, with several species recorded only from threatened or endangered hosts. Beyond their dependence on these plants, little is known of their evolutionary history and conservation status. We constructed a molecular phylogeny of Philodoria to assess validity of its current subgeneric classification and to help guide future work on this threatened Hawaiian lineage. Mitochondrial and nuclear DNA sequences from three genes (CO1, CAD, EF-1α) combining for a total of 2,041 base pairs, were collected from 11 Philodoria species, incorporating taxa from both currently recognized subgenera. These data were analyzed using both parsimony and model-based phylogenetic approaches. Contrary to the most recent systematic treatment of Philodoria, our results indicate strongly that the two currently recognized Philodoria subgenera are not monophyletic and that morphological characters used to classify them are homoplasious. Based on our robust results, we revised the higher classification of Philodoria: the subgenus Eophilodoria Zimmerman, 1978 is established as subjective junior synonym of Philodoria Walsingham, 1907. We also present new host plant and distribution data and discuss host range of Philodoria as it pertains to endangered Hawaiian plants.
Mining the first 100 days: Human and data ethics in Twitter research
<p>This dataset consists of tweet identifiers for tweets harvested between November 28, 2016, following the election of Donald Trump through the end of the first 100 days of his administration. Data collection ended May 1, 2017.</p> <p>Tweets were harvested using multiple methods described below. The total dataset consists of 218,273,152 tweets. Because of the different methods used to harvest tweets, there may be some duplication.</p>
Legacies of historic charcoal production affect the forest flora in a Swedish mining district: survey data
<p>Iron production was historically associated with one of the major impacts on forests worldwide, as vast amounts of wood were harvested to produce the charcoal needed for reducing iron oxides in the ore to iron. This impact has left abundant legacies which potentially may remain in the present-day vegetation. We investigated how remains of historic charcoal production, mainly from the 18<sup>th</sup> to the early 20<sup>th</sup> century, at still remaining charcoal kiln platforms (CKPs), affect the current species richness, species occurrences, and cover of field layer vascular plants in a Swedish mining district located in the boreo-nemoral forest zone. CKPs have a significantly higher species richness than the surrounding forest, and they also affect cover (negatively) for ericaceous species typically dominating the forest field-layer. Several forest species are more frequent at CKPs, and these also harbor significantly more uncommon species, of which many are typical for traditionally managed grasslands; these latter species are likely to represent remnants in present-day forests reflecting former land-use such as livestock grazing. The soil chemistry at CKPs is strongly deviating from the surrounding forest, and this, together with a lower cover of ericaceous shrubs, are the most likely mechanisms behind the higher species richness. CKPs represent conspicuous and abundant historic anthropogenic habitats in the forest vegetation. As far as we are aware, the flora at CKPs in boreal and boreo-nemoral forests has not previously been investigated in detail, and they deserve more attention, both from a biological and a cultural-historical perspective.</p>
Mapping forests with different levels of naturalness using machine learning and landscape data mining - GRASS GIS DB
<p>The GRASS GIS database containing the input raster layers needed to reproduce the results from the manuscript entitled:</p> <p><strong>"Mapping forests with different levels of naturalness using machine learning and landscape data mining"</strong> (under review)</p> <p>Abstract:</p> <p><em>To conserve biodiversity, it is imperative to maintain and restore sufficient amounts of functional habitat networks. Hence, locating remaining forests with natural structures and processes over landscapes and large regions is a key task. We integrated machine learning (Random Forest) and wall-to-wall open landscape data to scan all forest landscapes in Sweden with a 1 ha spatial resolution with respect to the relative likelihood of hosting High Conservation Value Forests (HCVF). Using independent spatial stand- and plot-level validation data we confirmed that our predictions (ROC AUC in the range of 0.89 - 0.90) correctly represent forests with different levels of naturalness, from deteriorated to those with high and associated biodiversity conservation values. Given ambitious national and international conservation objectives, and increasingly intensive forestry, our model and the resulting wall-to-wall mapping fills an urgent gap for assessing fulfilment of evidence-based conservation targets, spatial planning, and designing forest landscape restoration.</em></p> <p>This database was compiled from the following sources:</p> <p>1. <strong>HCVF</strong>. A database of High Conservation Value Forests in Sweden. Swedish Environmental Protection Agency.</p> <p>source: <a href="https://geodata.naturvardsverket.se/nedladdning/skogliga_vardekarnor_2016.zip">https://geodata.naturvardsverket.se/nedladdning/skogliga_vardekarnor_2016.zip</a></p> <p>2. <strong>NMD</strong>. National Land Cover Data. Swedish Environmental Protection Agency.</p> <p>source: <a href="https://www.naturvardsverket.se/en/services-and-permits/maps-and-map-services/national-land-cover-database/">https://www.naturvardsverket.se/en/services-and-permits/maps-and-map-services/national-land-cover-database/</a></p> <p>3. <strong>DEM</strong>. Terrain Model Download, grid 50+. Lantmateriet, Swedish Ministry of Finance.</p> <p>source: <a href="https://www.lantmateriet.se/en/geodata/geodata-products/product-list/terrain-model-download-grid-50/">https://www.lantmateriet.se/en/geodata/geodata-products/product-list/terrain-model-download-grid-50/</a></p> <p>4. <strong>GFC</strong>. Global Forest Change. Global Land Analysis and Discovery, University of Maryland.</p> <p>source: <a href="https://glad.earthengine.app">https://glad.earthengine.app</a></p> <p>5. <strong>LIGHTS</strong>. A harmonized global nighttime light dataset 1992–2018. Land pollution with night-time lights expressed as calibrated digital numbers (DN).</p> <p>source: <a href="https://doi.org/10.6084/m9.figshare.9828827.v2">https://doi.org/10.6084/m9.figshare.9828827.v2</a></p> <p>6. <strong>POPULATION</strong>. Total Population in Sweden. Statistics Sweden.</p> <p>source: <a href="https://www.scb.se/en/services/open-data-api/open-geodata/grid-statistics/">https://www.scb.se/en/services/open-data-api/open-geodata/grid-statistics/</a></p> <p> </p> <p>To learn more about the GRASS GIS database structure, see:</p> <p><a href="https://grass.osgeo.org/grass82/manuals/grass_database.html">https://grass.osgeo.org/grass82/manuals/grass_database.html</a></p>
Label-free data mining of scientific literature by unsupervised syntactic distance analysis
<p>1. OER.zip: literature resources, mined data and manual annotation of OER</p> <p>2. OLED.zip: literature resources and mined data of OLED</p> <p>3. PVK_ligand.zip: literature resources, mined data and manual annotation of perovskite ligand</p> <p>4. PVK_solvent.zip: literature resources, mined data and manual annotation of perovskite solvent</p> <p>5. syn_vec.part01.rar - syn_vec.part01.rar: word vector model trained by synthesis paragraphs in USPTO</p> <p>6. crossref_vec.part01.rar - crossref_vec.part10.rar: word vector trained by abstracts in Crossref</p>
BOLD Insecta and Araneae data files for "Mining biodiversity databases establishes a global baseline of cosmopolitan Insecta mOTUs: a case study on Platygastroidea (Hymenoptera) with consequences for biological control programs"
<p>These are the BOLD Insecta and Araneae DWC files for use with the automated scripting procedure from "Mining biodiversity databases establishes a global baseline of cosmopolitan Insecta mOTUs: a case study on Platygastroidea (Hymenoptera) with consequences for biological control programs".</p>
Fig. 6 in Genomic data mining approaches for the discovery of anticancer peptides from Ganoderma sinense
Fig. 6. Three-dimensional structures of P14 and P15, shown in cartoon representation; red represents α-helix, yellow represents β-sheet, and blue represents turn structure. Structures were extracted from the trajectory of molecular dynamic simulation from 20 ns to 100 ns. The lowest energy conformation is presented. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 4 in Genomic data mining approaches for the discovery of anticancer peptides from Ganoderma sinense
Fig. 4. Length distributions of cleaved peptides. The parent proteins were cleaved by in silico enzyme digestion with trypsin. The length distributions of peptides between 7 and 19 amino acids in length are shown.
Fig. 2 in Genomic data mining approaches for the discovery of anticancer peptides from Ganoderma sinense
Fig. 2. Experimental flowchart. (A) Blast search and alignment of G. sinense proteins with known anticancer peptides. (B) An example of a Hit sequence. (C) Hit sequences were cleaved by in silico enzyme digestion of trypsin. (D) The digested fragments were screened by mACPpred, an SVM-based algorithm, to identify putative ACPs. (E) The resulting peptides were then subjected to sequence comparison to known ACPs.
Bibliometric Analysis of Data Mining Techniques in Human Motion Research: Future Directions
<p>bibliometric dataset</p>
Data Mining: Precision Analytical Retrospective Data Correlation
ClinicalTrials.gov study NCT04305093. IPD Sharing: Not stated. Countries: 1. Publications: 8.
Big Data and Text-mining Technologies Applied for Breast Cancer Medical Data Analysis
ClinicalTrials.gov study NCT02810093. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: Co-introduction of native mycorrhizal fungi and plant seeds accelerates restoration of post-mining landscapes
Open the record for dataset details and reuse information.
Data from: Acoustic monitoring of coastal dolphins and their response to naval mine neutralization exercises
Open the record for dataset details and reuse information.
Data from: Restoration as mitigation: analysis of stream mitigation for coal mining impacts in southern Appalachia
Open the record for dataset details and reuse information.
Data from: Using text-mined trait data to test for cooperate-and-radiate co-evolution between ants and plants
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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.