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753 results for “metrics”
Data from: Energetic fitness: field metabolic rates assessed via 3D accelerometry complement conventional fitness metrics
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Repository Analytics and Metrics Portal (RAMP) 2017 data
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Dental linear metrics from a wild population of baboons (Papio cynocephalus), Kenya
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Postcanine dental metrics for hominin fossils from the Omo, Ethiopia, and the comparative dataset
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nRCFV: A sequence, taxon and character state-normalised metric for the pre-reconstruction evaluation of compositional heterogeneity
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Data from: Standardising fossil disparity metrics using sample coverage
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Common field data limitations can substantially bias sexual selection metrics
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Supplementary material for: Phylogenetic biodiversity metrics should account for both accumulation and attrition of evolutionary heritage
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Data from: The shape of avian eggs: assessment of a novel metric for quantifying eggshell conicality
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Quantifying microhabitat selection of snowshoe hares using forest metrics from UAS-based LiDAR
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Dimensional analysis of spring-wing systems reveals performance metrics for resonant flapping-wing flight
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Hubbard Brook Experimental Forest: 5 meter LiDAR-derived Topographic Metrics, 2018
This data package contains 5 m LiDAR-derived topographic metrics across Hubbard Brook EF following the approach reported by (Gillin et al., 2015). The LiDAR was collected during leaf-off and snow-free conditions by Photo Science, Inc. in April 2012 for the White Mountain National Forest (WMNF). These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station. Gillin, C.P., S.W. Bailey, K.J. McGuire, and J.P. Gannon. 2015. Mapping of Hydropedologic Spatial Patterns in a Steep Headwater Catchment. Soil Sci. Soc. Am. J. 79(2): 440–453. doi: 10.2136/sssaj2014.05.0189.
Phenological metrics for Protected Area "OhridPrespa", MODIS aqua tile h19v04
Phenological metrics derived from satellite data by use of R-package "phenex" (Lange (2017)). NDVI is filtered by modified BISE algorithm (see Viovy (1992)). NDVI curve is modelled with method DLogistic (see Doktor (2017), Lange (2017)). Phenological metrics include: (1) Start of season / green-up (GU); (2) End of season / senescence (SEN); (3) Length of vegetation period (VP); (4) GPP proxy (integral over vegetation period, GSIVI); (5) minimum NDVI (MinNDVI); (6) maximum NDVI (MaxNDVI); (7) day of minimum NDVI as julian date (MinDOY); (8) day of maximum NDVI as julian date (MaxDOY); (9) r-square of modelled NDVI curve; (1)-(4) are derived by using local threshold (LT) and global threshold (GT) method (see Doktor (2017), Lange (2017)). (1)-(6) include mean and standard deviation; References: [Viovy 1992]: Viovy N, Arino O, Belward A (1992) The Best Index Slope Extraction (BISE): A method for reducing noise in NDVI time-series. International Journal of Remote Sensing 13(8):1585–1590; [Doktor 2017]: Doktor D, Lange M (2017) Disparate applicability and broad spatio-temporal satellite resolution affects extracted trends of European spring phenology for 1989-2007. In preparation for Global Ecology and Biogeographie; [Lange 2017]: Lange M, Doktor D (2017) phenex: Auxiliary Functions for Phenological Data Analysis. R package version 1.4-5, https://CRAN.R-project.org/package=phenex, Last accessed on 2017-05-29;
A Metric for Optimism: John Ioannidis on Reproducibility, Preregistration, and Data Sharing
<p><strong>Episode Summary:</strong></p> <p>In this episode we are discussing data sharing and Open Science. Our interview guest will be Stanford University Professor of Medicine: John Ioannidis who has now come to the Berlin Institute of Health as an Einstein BIH Visiting Fellow at the BIH QUEST Center to establish the Meta-Research Innovation Center Berlin (METRIC-Berlin), the European “sister” of the Meta-Research Innovation Center at Stanford (METRICS). We will cover his research and opinions on data sharing, reproducibility, and how to improve research.</p> <p><strong>Links: </strong></p> <p><a href="https://profiles.stanford.edu/john-ioannidis">John Ioannidis</a></p> <p><a href="https://www.bihealth.org/en/research/quest-center/mission-approaches/">BIH Quest Centre</a></p> <p><a href="https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.0020124">Why Most Published Research Findings Are False</a></p> <p><strong>Quotes:</strong></p> <p>'I think that scientists, by themselves, are recognizing that it is important to share [data] and in many fields, like in Genetics, they realize that unless they share they cannot really go very far'</p> <p>'Clearly over the years we have seen more scientific sharing of data'</p>
Data Set from the Systematization of Vulnerability Discovery Metrics
<p>The data set contains vulnerability discovery metric data extracted from 26 primary studies identified as part of the systematic literature review conducted. The review was conducted as part achieving our overall research vision to <em>assist software engineers in building secure software by providing a technique that generates scientific, interpretable, and actionable feedback on security as the software evolves</em>.</p>
Stimuli and Results for "Investigating the Perceptual Validity of Evaluation Metrics for Automatic Piano Music Transcription"
<p>This contains the stimuli and the participants data for the listening tests presented in the paper:</p> <p>Adrien Ycart, Lele Liu, Emmanouil Benetos, Marcus T. Pearce. "Investigating the Perceptual Validity of Evaluation Metrics for Automatic Piano Music Transcription". <em>Transactions of the International Society for Music Information Retrieval</em>, 3(1):68-81, 2020 .</p> <p>More precisely, it contains:</p> <ul> <li>MAPS_midi_cut.zip: The MIDI files used to create the stimuli </li> <li>cut_points_seconds.zip: The points in seconds at which the MAPS music pieces were cut to make the stimuli. These correspond to manually-selected 5 to 10 seconds chunks, roughly corresponding to musical phrases.</li> <li>listening_test_results.zip: The data gathered during the listening test: <ul> <li>user_data.csv contains data about participants</li> <li>answers_data.csv contains the answers given by all participants</li> <li>comments.txt contains the comments left by the participants.</li> </ul> </li> </ul> <p>For any enquiries, please contact Adrien Ycart (a.ycart@qmul.ac.uk) or Emmanouil Benetos (emmanouil.benetos@qmul.ac.uk).</p> <p> </p>
Data from: Higher dominance rank is associated with lower glucocorticoids in wild female baboons: A rank metric comparison
<p>In vertebrates, glucocorticoid secretion occurs in response to energetic and psychosocial stressors that trigger the hypothalamic-pituitary-adrenal (HPA) axis. Measuring glucocorticoid concentrations can therefore shed light on the stressors associated with different social and environmental variables, including dominance rank. Using 14,172 fecal samples from 237 wild female baboons, we test the hypothesis that high-ranking females experience fewer psychosocial and/or energetic stressors than lower-ranking females. We predicted that high-ranking females would have lower fecal glucocorticoid (fGC) concentrations than low-ranking females. Because dominance rank can be measured in multiple ways, we employ an information theoretic approach to compare 5 different measures of rank as predictors of fGC concentrations: ordinal rank; proportional rank; Elo rating; and two approaches to categorical ranking (alpha vs non-alpha and high-middle-low).</p> <p>Our hypothesis was supported, but it was also too simplistic. We found that alpha females exhibited substantially lower fGCs than other females (typical reduction = 8.2%). If we used proportional rank instead of alpha- versus non-alpha status in the model, we observed a weak effect of rank such that fGCs rose 4.2% from the highest- to lowest-ranking female in the hierarchy. Models using ordinal rank, Elo rating, or high-middle-low categories alone failed to explain variation in female fGCs. Our findings shed new light on the association between dominance rank and the stress response, the competitive landscape of female baboons as compared to males, and the assumptions inherent in a researcher's choice of rank metric.</p>
Qualisign: Software Metrics and GoF Design Patterns of the Maven Central Repository
<p>This dataset contains software metric and design pattern data for around 100,000 projects from the Maven Central repository. The data was collected and analyzed as part of my master's thesis "Mining Software Repositories for the Effects of Design Patterns on Software Quality" (https://www.overleaf.com/read/vnfhydqxmpvx, https://zenodo.org/record/4048275).</p> <p>The included qualisign.* files all contain the same data in different formats:<br> - qualisign.sql: standard SQL format (exported using "pg_dump --inserts ..."),<br> - qualisign.psql: PostgreSQL plain format (exported using "pg_dump -Fp ..."),<br> - qualisign.csql: PostgreSQL custom format (exported using "pg_dump -Fc ...").</p> <p>create-tables.sql has to be executed before importing one of the qualisign.* files. Once qualisign.*sql has been imported, create-views.sql can be executed to preprocess the data, thereby creating materialized views that are more appropriate for data analysis purposes.</p> <p>---</p> <p>Software metrics were calculated using CKJM extended:<br> http://gromit.iiar.pwr.wroc.pl/p_inf/ckjm/</p> <p>Included software metrics are (21 total):<br> - AMC: Average Method Complexity<br> - CA: Afferent Coupling<br> - CAM: Cohesion Among Methods<br> - CBM: Coupling Between Methods<br> - CBO: Coupling Between Objects<br> - CC: Cyclomatic Complexity<br> - CE: Efferent Coupling<br> - DAM: Data Access Metric<br> - DIT: Depth of Inheritance Tree<br> - IC: Inheritance Coupling<br> - LCOM: Lack of Cohesion of Methods (Chidamber and Kemerer)<br> - LCOM3: Lack of Cohesion of Methods (Constantine and Graham)<br> - LOC: Lines of Code<br> - MFA: Measure of Functional Abstraction<br> - MOA: Measure of Aggregation<br> - NOC: Number of Children<br> - NOM: Number of Methods<br> - NOP: Number of Polymorphic Methods<br> - NPM: Number of Public Methods<br> - RFC: Response for Class<br> - WMC: Weighted Methods per Class</p> <p>In the qualisign.* data, these metrics are only available on the class level. create-views.sql additionally provides averages of these metrics on the package and project levels.</p> <p>---</p> <p>Design patterns were detected using SSA:<br> https://users.encs.concordia.ca/~nikolaos/pattern_detection.html</p> <p>Included design patterns are (15 total):<br> - Adapter<br> - Bridge<br> - Chain of Responsibility<br> - Command<br> - Composite<br> - Decorator<br> - Factory Method<br> - Observer<br> - Prototype<br> - Proxy<br> - Singleton<br> - State<br> - Strategy<br> - Template Method<br> - Visitor</p> <p>---</p> <p>The code to generate the dataset is available at:<br> https://github.com/jaichberg/qualisign</p> <p>The code to perform quality analysis on the dataset is available at:<br> https://github.com/jaichberg/qualisign-analysis</p>
Metrics Literacies research project mapped to Knowledge two Action (K2A) framework
<p><strong>Schematic representation of the Metrics Literacies research project mapped onto the Knowledge to Action (K2A) framework </strong></p> <p>Based on K2A framework proposed by:</p> <p>Wilson, K. M., Brady, T. J., Lesesne, C., & NCCDPHP Work Group on Translation. (2011). An organizing framework for translation in public health: The Knowledge to Action Framework. <em>Preventing Chronic Disease</em>, <em>8</em>(2), A46. PMID: 21324260</p> <p> </p>
[2019 QSM Reconstruction Challenge] Metrics and Submission Information
<p>This repository contains information about submitted solutions and resulting analysis metrics of the 2019 Quantitative Susceptibility Mapping Reconstruction Challenge. The original susceptibility maps submitted for participation in the challenge are available <a href="http://dx.doi.org/10.5281/zenodo.3687342">here</a> and <a href="http://dx.doi.org/10.5281/zenodo.3688703">here</a>.</p> <p>The package contains seven Comma-Separated Values (CSV) files and two PDF files:</p> <ul> <li><em>master_stage1_anonymized.csv</em>: Results of stage 1 of the challenge at the time of presentation at the workshop (fully-blinded);</li> <li><em>master_stage2_snr1_anonymized.csv</em>: Results of stage 2 of the challenge using the high noise dataset at the time of presentation at the workshop (fully-blinded);</li> <li><em>master_stage2_snr2_anonymized.csv</em>: Results of stage 2 of the challenge using the low noise dataset at the time of presentation at the workshop (fully-blinded);</li> <li><em>submission_form_stage1.pdf</em>: PDF export of the online form used in stage 1;</li> <li><em>submission_form_stage2.pdf</em>: PDF export of the online form used in stage 2.</li> </ul> <p>For the manuscript, we analyzed these CSV files with scripts reported <a href="https://doi.org/10.5281/zenodo.4559540">here</a>.</p> <p>Each csv file contains metrics for all submitted solutions along with detailed information about the algorithm used, provided by the participant at the time of submission. The very first record in each file is a header containing a list of field names:</p> <ul> <li><em>normalized rmse</em>: Whole-brain root-mean-squared error relative to ground truth;</li> <li><em>rmse_detrend_tissue</em>: Root-mean-squared error relative to ground truth (after detrending) in grey and white matter mask;</li> <li><em>rmse_detrend_blood</em>: Root-mean-squared error relative to ground truth (after detrending) using a one-pixel dilated vein mask;</li> <li><em>rmse_detrend_DGM</em>: Root-mean-squared error relative to ground truth (after detrending) in a deep gray matter mask (substantia nigra & subthalamic nucleus, red nucleus, dentate nucleus, putamen, globus pallidus and caudate);</li> <li><em>DeviationFromLinearSlope</em>: Absolute difference between the slope of the average value of the six deep gray matter regions vs. the prescribed mean value and 1.0;</li> <li><em>CalcStreak</em>: Estimation of the impact of the streaking artifact in a region of interest surrounding the calcification through the standard deviation of the difference map between reconstruction and the ground truth;</li> <li><em>DeviationFromCalcMoment</em>: Absolute deviation from the volumetric susceptibility moment of the reconstructed calcification, compared to the ground truth (computed at in the high-resolution model);</li> <li><em>Submission Identifier</em>: Self-chosen unique identifier of the submission;</li> <li><em>Submission Identifier of the corresponding Stage 1 submission</em>: This is the Submission Identifier of the solution submitted to Stage 2 that was calculated with a similar algorithm in Stage 1;</li> <li><em>Changes with respect to Stage 1 submission</em>: Self-reported information about modifications made to the algorithm for Stage 2;</li> <li><em>Number of submissions in Stage 2</em>: The number of solutions that were submitted to Stage 2 with a similar algorithm;</li> <li><em>Sim1/Sim2</em>: Filename of the submitted solutions for Stage 1;</li> <li><em>File name of the zip-file you are going to upload</em>: Filename of the file uploaded to Stage 2;</li> <li><em>Full name of the algorithm</em>: Self-reported full name of the algorithm used;</li> <li><em>Preferred Acronym</em>: Self-reported acronym of the algorithm used;</li> <li><em>Algorithm-type</em>: Self-reported type of algorithm used;</li> <li><em>Does your algorithm incorporate information derived from magnitude images?</em>: Self-reported Yes/No;</li> <li><em>Regularization terms</em>: Self-reported types of regularization terms involved;</li> <li><em>Did your algorithm use the provided frequency map or the four individual echo phase images?</em>: Self-reported information about involved magnitude information;</li> <li><em>Publication-ready description of the reconstruction technique</em>: Self-reported description of the algorithm;</li> <li><em>Publications that describe the algorithm</em>: Self-reported literature reference;</li> <li><em>Algorithm publicly available?</em>: Self-reported public availability of the algorithm;</li> <li><em>If your algorithm is not yet publicly available, would you be willing to make it available at the end of the challenge?</em>: Self-reported willingness to share the algorithm code with the public;</li> <li><em>Specific information about this solution</em>: Self-reported detailed information about the solution;</li> <li><em>Herewith, I permit the QSM Challenge committee to publish my uploaded files (calculated maps) after the completion of the challenge</em>: Self reported agreement with publication of submitted solution;</li> <li><em>Ground truth was not explicitly or implicitly incorporated into your algorithm or solution</em>: Self-reported confirmation that the ground truth was not incorporated in the solution.</li> </ul>
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.