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753 results for “metrics”
Metrics As Scores Dataset: The Iris Flower Data Set
<p>The Iris flower data set or Fisher’s Iris data set is a multivariate data set used and made famous by the British statistician and biologist Ronald Fisher. The dataset was introduced in his 1936 paper "The Use of Multiple Measurements in Taxonomic Problems" (Fisher 1936) as an example of linear discriminant analysis.</p> <p>This dataset has the following Features:</p> <ul> <li><em>Petal.Length</em>: Length of the petal</li> <li><em>Petal.Width</em>: Width of the petal</li> <li><em>Sepal.Length</em>: Length of the sepal</li> <li><em>Sepal.Width</em>: Width of the sepal</li> </ul> <p>It has a total of 3 <strong>Groups</strong>: <em>setosa</em>, <em>versicolor</em>, and <em>virginica</em>.</p>
Metrics As Scores Dataset: Metrics and Domains From the Qualitas.class Corpus
<p>This dataset was created by extracting software metrics data from the Qualitas.class corpus (Terra et al. 2013; Tempero et al. 2010). Therefore, the principal quantity type is Metric, and the context is given by a system’s Domain (e.g., "Game", "Middleware", etc.). Some metrics were obtained on program-level, while others are package- or method-level metrics. Most of the metrics in the corpus are of discrete/integral nature. The corpus holds 23 types of pre-computed software metrics for a total of 111 systems which are spread across eleven different domains.</p> <p>This dataset has the following discrete <strong>Features</strong> (Metrics):</p> <ul> <li>CA: Afferent Coupling</li> <li>CE: Efferent Coupling</li> <li>DIT: Depth of Inheritance Tree</li> <li>MLOC: Method Lines of Code</li> <li>NBD: Nested Block Depth</li> <li>NOC: Number of Classes</li> <li>NOF: Number of Attributes</li> <li>NOI: Number of Interfaces</li> <li>NOM: Number of Methods</li> <li>NOP: Number of Packages</li> <li>NORM: Number of Overridden Methods</li> <li>NSC: Number of Children</li> <li>NSF: Number of Static Attributes</li> <li>NSM: Number of Static Methods</li> <li>PAR: Number of Parameters</li> <li>TLOC: Total Lines of Code</li> <li>VG: McCabe Cyclomatic Complexity</li> <li>WMC: Weighted Methods per Class</li> </ul> <p>The following features are continuous:</p> <ul> <li>LCOM: Lack of Cohesion in Methods</li> <li>RMA: Abstractness</li> <li>RMD: Normalized Distance</li> <li>RMI: Instability</li> <li>SIX: Specialization Index</li> </ul> <p>It has a total of 11 <strong>Groups</strong> (Domains): 3D; Graphics; Media, Databases, Diagrams; Visualiz., Games, IDE, Middleware, Parsers; Generators, Progr. Language, SDK, Testing, and Tool.</p>
CYJAX: A package for Calabi-Yau metrics with JAX [data & figures]
<p>Data of achieved accuracies of numerically approximated Calabi-Yau metrics as presented in the associated paper "CYJAX: A package for Calabi-Yau metrics with JAX" [<a href="https://arxiv.org/abs/2211.12520">2211.12520</a>].</p>
(Annotation and metric) Thalassiosirales reference transcriptomes
<p>Here are deposited the public data recompiled for the construction of a Thalassiosirales reference database as part of a Ph.D Thesis "TEMPERATURE ACCLIMATION CAPACITY AND COLD-ADAPTATION MECHANIMS IN THALASSIOSIRALES ANTARCTIC MEMBERS". Data correspond to the annotation of 53 transcriptomes from the MMETSP of Thalassiosirales members.</p>
Metrics and peer review agreement at the institutional level - Data
<p>This data is released to accompany the paper:</p> <p>Traag, VA, Malgarini, M and Sarlo, S (2020) Metrics and peer review agreement at the institutional level.</p>
Euclide, the crow, the wolf and the pedestrian: distance metrics for linguistic typology - Dataset
<p>This repository contains the distance matrices and code for the paper "Euclide, the crow, the wolf and the pedestrian: distance metrics for linguistic typology"</p>
METRIC - Multi-Eye To Robot Indoor Calibration Dataset
<p>The METRIC dataset comprises more than 10,000 synthetic and real images of ChAruCo and checkerboard patterns. Each pattern is securely attached to the robot's end-effector, which is systematically moved in front of four cameras surrounding the manipulator. This movement allows for image acquisition from various viewpoints. The real images in the dataset encompass multiple sets of images captured by three distinct types of sensor networks: Microsoft Kinect V2, Intel RealSense Depth D455, and Intel RealSense Lidar L515. The purpose of including these images is to evaluate the advantages and disadvantages of each sensor network for calibration purposes. Additionally, to accurately assess the impact of the distance between the camera and robot on calibration, we obtained a comprehensive synthetic dataset. This dataset contains associated ground truth data and is divided into three different camera network setups, corresponding to three levels of calibration difficulty based on the cell size.</p>
Supplementary material for the article "High-resolution projections of ambient heat for major European cities using different heat metrics"
<p>This dataset contains the data displayed in the figures or the article "High-resolution projections of ambient heat for major European cities using different heat metrics".</p> <p>The different files contain:</p> <ul> <li>Data_Fig1_DeltaTXx_EURO-CORDEX_1981-2010_to_3K-European-warming_RCP85.nc:<br> Change of yearly maximum temperature in Europe between 1981-2010 and 3 °C European warming relative to 1981-2010.</li> <li>Data_Fig2_timeseries-GSAT-ESAT_EURO-CORDEX_CMIP5_CMIP6_1971-2100_RCP85_SSP585.xlsx:<br> Time series of global mean surface air temperature (GSAT) for CMIP5 and CMIP6 models, and for European mean surface air temperature (ESAT) for EURO-CORDEX, CMIP5, and CMIP6 models for the period 1971-2100.</li> <li>Data_Fig3_TX-distribution_distance-from-city-centre_E-OBS_1981-2010.xlsx:<br> Distribution of average daily maximum temperature in summer (June, July, August) in 1981-2010 for E-OBS for all investigated cities. Temperature data are indicated as a function of the distance to the city centre.</li> <li>Data_Fig3_TX-distribution_distance-from-city-centre_ERA5-Land_1981-2010.xlsx:<br> Distribution of average daily maximum temperature in summer (June, July, August) in 1981-2010 for ERA5-Land for all investigated cities. Temperature data are indicated as a function of the distance to the city centre.</li> <li>Data_Fig3_TX-distribution_distance-from-city-centre_EURO-CORDEX_1981-2010.xlsx:<br> Distribution of average daily maximum temperature in summer (June, July, August) in 1981-2010 for the EURO-CORDEX models for all investigated cities. Temperature data are indicated as a function of the distance to the city centre.</li> <li>Data_Fig3_TX-distribution_distance-from-city-centre_weather-stations_1981-2010.xlsx:<br> Distribution of average daily maximum temperature in summer (June, July, August) in 1981-2010 for GSOD and ECA&D stations for all investigated cities. Temperature data are indicated as a function of the distance to the city centre.</li> <li>Data_Fig4_TX-ambient-heat_EURO-CORDEX_3K-European-warming.xlsx:<br> Daytime heat metrics for the investigated cities: HWMId-TX at 3 °C European warming relative to 1981-2010, TX exceedances above 30 °C at 3 °C European warming relative to 1981-2010, and TXx change between 1981-2010 and 3 °C European warming relative to 1981-2010 for EURO-CORDEX models.</li> <li>Data_Fig5_Contribution-of-explanatory-variables-to-total-explained-variance.xlsx:<br> Contribution of different explanatory variables (climate and location factors) to the total explained variance of spatial patterns of heat metrics.</li> <li>Data_Fig6_TN-ambient-heat_EURO-CORDEX_3K-European-warming.xlsx:<br> Nighttime heat metrics for the investigated cities: HWMId-TN at 3 °C European warming relative to 1981-2010, TN exceedances above 20 °C at 3 °C European warming relative to 1981-2010, and TNx change between 1981-2010 and 3 °C European warming relative to 1981-2010 for EURO-CORDEX models.</li> <li>Data_Fig7_TX-ambient-heat_CMIP5_3K-European-warming.xlsx:<br> Daytime heat metrics for the investigated cities: HWMId-TX at 3 °C European warming relative to 1981-2010, TX exceedances above 30 °C at 3 °C European warming relative to 1981-2010, and TXx change between 1981-2010 and 3 °C European warming relative to 1981-2010 for CMIP5 models.</li> <li>Data_Fig7_TX-ambient-heat_CMIP6_3K-European-warming.xlsx:<br> Daytime heat metrics for the investigated cities: HWMId-TX at 3 °C European warming relative to 1981-2010, TX exceedances above 30 °C at 3 °C European warming relative to 1981-2010, and TXx change between 1981-2010 and 3 °C European warming relative to 1981-2010 for CMIP6 models.</li> <li>Data_Fig8_GCM-RCM-matrix_ambient-heat_3K-European-warming.xlsx:<br> GCM-RCM matrices for the three heat metrics.</li> </ul>
Return on Investment Metrics for Data Repositories in Earth and Environmental Sciences
Despite a growing recognition of the importance of data to the economy and to science, investment in repositories to manage and disseminate that data in easily accessible and understandable ways is scarce. Keeping repository services active and up-to-date for a long time period is difficult due to this funding situation. As a result, repositories must continually provide proof of their value, their Return on Investment (ROI) to their sponsors; yet doing so has always been difficult, problematic and not always successful. In this work, an analysis of approaches for assessing the ROI of several scientific data repositories has identified various techniques that repositories use to report on the impact and value of their data products and services. A survey of selected repositories rated the set of metrics identified and rated each by its importance as well as the ease with which the metric could be measured. The discussion is broken down into considerations for calculating costs, perceived value of repositories and suggested metrics that would allow a repository to calculate an ROI. The authors, representatives of environmental data repositories, concluded that easily obtainable data use metrics, such as data downloads, etc., have limited value while more informative analyses would require additional resources.
High-frequency water temperature and dissolved oxygen data and derived stability and metabolism metrics for nine lakes in northeastern North America for months before and after Tropical Cyclone Irene, Fall 2011
This dataset is used in the analysis published in the following manuscript: Klug, J.L., D.C. Richardson, H.A. Ewing, B.R.Hargreaves, N. R. Samal, D. Vachon, D.C. Pierson, A. E. Lindsey, D. O'Donnell, S.W. Effler, and K.C. Weathers. 2012. Ecosystem effects of a tropical cyclone on a network of lakes in northeastern North America. Environmental Science and Technology 46(21): 11693–11701. We include Quality Assurance Quality Controlled (QAQC) high-frequency dissolved oxygen, wind speed, and water temperature data from nine lakes and reservoirs in northeastern North America which were near the track of Tropical Cyclone Irene in August 2011. These data were collected using a set of in situ, automated monitoring systems associated with the Global Lake Ecological Observatory Network (GLEON) that record data at high frequency (10 min to 6 h). These sensor data were the basis for the derived measures of Schmidt stability, net ecosystem production, respiration, and gross primary production included in the dataset. We also include daily rainfall data collected at on-site or nearby weather stations. All data cover the period from 01 August through 15 October 2011.
Forest metrics derived from the 2008 Lidar point clouds, includes canopy closure, percentile height, and stem mapping for the Andrews Experimental Forest.
There are three types of forest metrics within this database. They all are derived from the raw Lidar point clouds using the FUSION software. The three types are canopy closure, height metric, and stem mapping. The canopy closure and height metric grids cover a variety of canopy heights and grid cell sizes. 1. Canopy closure: This metric measures the canopy closure of a given horizontal cell above a given vertical threshold (height break). Canopy closure can inform many landscape models and provide insight on how much light will reach the forest floor. 2. Height Metric: This metric measures the height at which a given percent of the first return points are below. This analysis is done in a given grid cell size. Height metrics give various statistics of the elevation above ground for a given set of Lidar points. In forested landscapes, first return height metrics describe the forest canopy. 3.This stem map locates the approximate center of all trees in the HJ Andrews Research Forest greater than 10 meters. In addition to the stem location, a canopy radius is also provided. FUSION and TreeVaWA software programs were used to develop this data. Watershed Sciences, Inc. (WS) collected Light Detection and Ranging (LiDAR) data from HJ Andrews and the Willamette National Forest (NF) on August 10th and 11th 2008. Total area for this AOI is 17,705 acres. The total area of delivered LiDAR including 100 m buffer is 19,493 acres.
Movement Metrics for Common Snook and Atlantic Tarpon in the Coastal Everglades, Florida, USA, July 2016 to April 2021
This dataset contains acoustic telemetry derived residency and movement metrics for Common Snook (Centropomus undecimalis) and Atlantic Tarpon (Megalops atlanticus) tracked in two neighboring estuarine systems in the coastal Florida Everglades by the Coastal Everglades Lakes Acoustic Array. Data were summarized at the quarter-season level (early dry, late dry, early wet, late wet) across multiple years (2016–2021), resulting in 326 records and 15 variables. Each record includes metadata on species identity, year, system, and season, as well as individual-level residency time statistics (minimum, maximum, mean, variance), number of lakes visited, and proportional system use. The dataset provides a standardized summary of spatial and temporal variation in Snook and Tarpon habitat use, suitable for investigating seasonal residency and patterns of movement behavior. Data collection for this package is complete.
Phenological metrics for Protected Area "GranParadiso", MODIS terra tile h18v04
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;
Phenological metrics for Protected Area "HighTatra", MODIS terra 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;
Fig 4 in Do different sampling designs produce differences in the metrics of curimba, Prochilodus lineatus (Characiformes: Prochilodontidae)?
Fig 4. Frequency distribution by standard length (SL) class of curimba for fixed and variable sampling sites in Volta Grande (VGR) and Jaguara (JR) reservoirs.
Fig. 3 in Do different sampling designs produce differences in the metrics of curimba, Prochilodus lineatus (Characiformes: Prochilodontidae)?
Fig. 3. Temporal variation in catch per unit effort (CPUE) of curimba for fixed and variable sampling sites in Volta Gran- de (VGR) and Jaguara (JR) reservoirs.
Data from : Classifying wetland‐related land cover types and habitats using fine‐scale lidar metrics derived from country‐wide Airborne Laser Scanning
<p>This data repository contains the processed lidar metrics for characterizing the habitat structure for classifying main land cover and habitat types in the Lauwersmeer area in the northern part of the Netherlands in the province of Groningen (5754 ha). The lidar metrics were derived from Airborne Laser Scanning (ALS) data using the Actueel Hoogtebestand Nederland 2 (AHN2) openly available dataset from https://www.pdok.nl/. </p> <p>The derived lidar metrics saved in *.grd file format and contain 32 bands. Each band represents a lidar metric and the water surface was masked out in the dataset. The *l1* in the file name indicates that the file was used for level 1 (wetland) classification and *l23* used for level 2 (land cover types within wetland) and level 3 (reedbed habitats) classification. The lidar metrics were calculated using lidR (<a href="https://github.com/Jean-Romain/lidR">https://github.com/Jean-Romain/lidR</a>) software package. Further details related to the lidar metrics extraction can be found at <a href="https://github.com/eEcoLiDAR/PhDPaper1_Classifying_wetland_habitats">https://github.com/eEcoLiDAR/PhDPaper1_Classifying_wetland_habitats</a> Github repository.</p> <p> </p>
Data from: Effects of taxon sampling and tree reconstruction methods on phylodiversity metrics
1. The amount and patterns of phylodiversity in a community are often used to draw inferences about the local and historical factors affecting community assembly and can be used to prioritize communities and locations for conservation. Because measures of phylodiversity are based on the topology and branch lengths of phylogenetic trees, which are affected by the number and diversity of taxa in the tree, these analyses may be sensitive to changes in taxon sampling and tree reconstruction methods. 2. To investigate the effects of taxon sampling and tree reconstruction methods on measures of phylodiversity, we investigated the community phylogenetics of the Ordway-Swisher Biological Station (Florida), which is home to over 600 species of vascular plants. We studied the effects of 1) the number of taxa included in the regional phylogeny; 2) random vs. targeted sampling of species to assemble the regional species pool; 3) including only species from specific clades rather than broad sampling; 4) using trees reconstructed directly for the taxa under study compared to trees pruned from a larger reconstructed tree; and 5) using phylograms compared to chronograms. 3. We found that including more taxa in a study increases the likelihood of observing significantly non-random phylogenetic patterns. However, there were no consistent trends in the phylodiversity patterns based on random taxon sampling compared to targeted sampling, or within individual clades compared to the complete dataset. Using pruned and reconstructed phylogenies resulted in similar patterns of phylodiversity, while chronograms in some cases led to significantly different results from phylograms. 4. The methods commonly used in community phylogenetic studies can significantly impact the results, potentially influencing both inferences of community assembly and conservation decisions. We highlight the need for both careful selection of methods in community phylogenetic studies and appropriate interpretation of results, depending on the specific questions to be addressed.
3D cloud metrics for OCO-2 observations
<p>The dataset contain contains supplementary data and the 3D cloud metrics described and analyzed in "Analysis of 3D Cloud Effects in OCO-2 XCO2 Retrievals”, Massie, S. T., Cronk, H., Merrelli, A., Schmidt, K. S., Chen, H., and Baker, D., Atmospheric Measurement Techniques, 14, 1475–1499, 2021.</p> <p>https://doi.org/10.5194/amt-14-1475-2021</p> <p>The supplementary data files include the average OCO-2 - TCCON XCO2 differences from several key figures in the manuscript (6, 7, 12). The OCO-2 data was extracted from Version 10 XCO2 retrievals from a subset of the data record used for testing and development by the OCO-2 algorithm team.</p> <p><br> figure6_ocean_distkm.dat<br> figure6_land_distkm.dat<br> These text files contain the average OCO-2 - TCCON XCO2 differences for several variations of the OCO2 XCO2 (raw and bias corrected, quality filter 0 or 1) as a function of the cloud distance metric. The first file includes only OCO-2 ocean glint data, and corresponds to Figure 6 from the manuscript. The second file contains the equivalent data for OCO-2 land (not plotted in the manuscript).</p> <p> </p> <p>figure7_ocean_csnoiseratio.dat<br> figure7_land_csnoiseratio.dat<br> These text files contains the average OCO-2 - TCCON XCO2 differences for several variations of the OCO-2 XCO2 (raw and bias corrected, quality filter 0 or 1) as a function of the color slice noise ratio. The first file includes only OCO-2 ocean glint data, and corresponds to Figure 7 from the manuscript. The second file contains the equivalent data for OCO-2 land (not plotted in the manuscript).</p> <p> </p> <p>figure12_ocean.dat<br> figure12_land.dat<br> These text files contain the average OCO-2 - TCCON XCO2 differences for several variations of the OCO-2 XCO2, same as above, but as a function of both the cloud distance metric and the color slice noise ratio. The x-variable is the cloud distance metric, and the y-variable is the colorslice noise ratio.<br> The first file includes only OCO-2 glint data, and corresponds to Figure 12 from the manuscript. The second file contains the equivalent data for OCO-2 land (not plotted in the manuscript).</p> <p> </p> <p><br> 3D cloud metric data:<br> 3d_cloud_metrics_oco2_v9_2014.zip<br> 3d_cloud_metrics_oco2_v9_2015.zip<br> 3d_cloud_metrics_oco2_v9_2016.zip<br> 3d_cloud_metrics_oco2_v9_2017.zip<br> 3d_cloud_metrics_oco2_v9_2018.zip<br> 3d_cloud_metrics_oco2_v9_2019.zip</p> <p>These files are created with observation arrays that match those contained within the version 9 OCO-2 Lite XCO2 product available at NASA Earthdata:</p> <p>OCO-2 Science Team/Michael Gunson, Annmarie Eldering (2018), OCO-2 Level 2 bias-corrected XCO2 and other select fields from the full-physics retrieval aggregated as daily files, Retrospective processing V9r, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), 10.5067/W8QGIYNKS3JC</p> <p>Each file is a daily aggregrate, with matched observation lists to the parent version 9 Lite XCO2 files. Each file contains a copy of the sounding_id from the original Lite product files. These files are grouped into yearly zip files to facilitate easier file transfer. Each daily file is in netCDF4 format with appropriate metadata to describe the fields and their units. The filenames are derived from the parent version 9 Lite XCO2 file with "3Dmetrics" added as a suffix. For example, the first file from 2014 is named "oco2_LtCO2_140906_B9003r_180927215925s_3Dmetrics.nc4" which is derived from the Version 9 Lite product file "oco2_LtCO2_140906_B9003r_180927215925s.nc4".<br> </p>
Enhanced Bug Prediction in JavaScript Programs with Hybrid Call-Graph Based Invocation Metrics (Training Dataset)
<p>This dataset consists of multiple files which contain bug prediction training data.</p> <p>The entries in the dataset are JavaScript functions either being buggy or non-buggy. Bug related information was obtained from the project EsLint contained in BugsJS (https://github.com/BugsJS/eslint). The buggy instances were collected throughout the lifetime of the project, however we added non-buggy entries from the latest version which is tagged as fix (entries which were previously included as buggy were not included as non-buggy later on).</p> <p>The dataset is based on hybrid call graphs which are constructed by https://github.com/sed-szeged/hcg-js-framework. The result of this tool is a call graph where the edges are associated with a confidence level which shows how likely the given edge is a valid call edge.</p> <p>We used different threshold values from which we considered the edges to be valid. The following threshold values were used:</p> <ul> <li>0.00</li> <li>0.05</li> <li>0.20</li> <li>0.30</li> </ul> <p>The prefix in the dataset file names are coming from the used threshold. The the datasets include coupling metrics NII (Nubmer of Incoming Invocations) and NOI (Number of Outgoing Invocations) which were calculated by a static source code analyzer called SourceMeter. Hybrid counterparts of these metrics (HNII and HNOI) are based on the given threshold values.</p> <p>There are four variants for all of these datasets:</p> <ul> <li>Both static (NII, NOi) and hybrid (HNII, HNOI) coupling metrics are included with additional static source code metrics and information about the entries (file without any postfix). Column contained only in this dataset are: <ul> <li>ID</li> <li>Name</li> <li>Longname</li> <li>Parent ID</li> <li>Component ID</li> <li>Path</li> <li>Line</li> <li>Column</li> <li>EndLine</li> <li>EndColumn</li> </ul> </li> <li>Both static (NII, NOi) and hybrid (HNII, HNOI) coupling metrics are included with additional static source code metrics (file with '_h+s' postfix)</li> <li>Only static (NII, NOI) coupling metrics are included with additional static source code metrics (file with '_s' postfix)</li> <li>Only hybrid (HNII, HNOI) coupling metrics are included with additional static source code metrics (file with '_h' postfix)</li> </ul> <p>Static source code metrics which are contained in all dataset are the following:</p> <ul> <li>McCC - McCabe Cyclomatic Complexity</li> <li>NL - Nesting Level</li> <li>NLE - Nesting Level Else If</li> <li>CD - Comment Density</li> <li>CLOC - Comment Lines of Code</li> <li>DLOC - Documentation Lines of Code</li> <li>TCD - Total Comment Density (Comment Lines in an emedded function will be also considered)</li> <li>TCLOC - Total Comment Lines of Code (Comment Lines in an emedded function will be also considered)</li> <li>LLOC - Logical Lines of Code (Comment and empty lines not counted)</li> <li>LOC - Lines of Code (Comment and empty lines are counted)</li> <li>NOS - Number of Statements</li> <li>NUMPAR - Number of Parameters</li> <li>TLLOC - Logical Lines of Code (Lines in embedded functions are also counted)</li> <li>TLOC - Lines of Code (Lines in embedded functions are also counted)</li> <li>TNOS - Total Number of Statements (Statements in embedded functions are also counted)</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.