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753 results for “metrics”
Histological validation of per-bundle water diffusion metrics within a region of fiber crossing following axonal degeneration
<p>Interactive plots showing the correlation between histological parameters of optic nerves and chiasm, and metrics derived from diffusion MRI in a rat model of unilateral retinal ischemia.</p> <p> </p> <p>There are two .html files, each containing an interactive figure, one for data pertaining to the optic nerve, the other for the chiasm. The left panel shows the correlation matrix. Click on any cell to see the corresponding scatter plot on the right panel. </p>
Exploring Internal Quality Metric Fluctuations in Revision Histories – An Empirical Study
<p>Datasets and analysis script for the article "Exploring Internal Quality Metric Fluctuations in Revision Histories – An Empirical Study" submitted to ESEM'19 under double-blind review and provided as-is.</p>
FIGURE 1 in Body size responses to land use in stream fish: the importance of different metrics and functional groups
FIGURE 1 | Description of the four body size metrics used to investigate body size patterns in overall stream fish communities and in distinct functional groups. A. Skewness describes the tendency of value distribution being biased towards the right (negative-skewed) or left (positive-skewed). B. Kurtosis describes if the distribution of values is more flatted (platykurtic) or biased towards the center (narrow). Mean values can be the same for distinct kurtosis. Coefficient of variation (CV) describes the variation in values standardized to the mean.
FIGURE 3 in Body size responses to land use in stream fish: the importance of different metrics and functional groups
FIGURE 3 | Clusters of fish species based on ecomorphological and trophic traits, resulting in 11 functional groups (FG). Full species names by FG are available in S2.
FIGURE 2 in Body size responses to land use in stream fish: the importance of different metrics and functional groups
FIGURE 2 | Location of the 40 stream sites where fish communities were sampled in South Brazilian grassland biome (Pampa).
Demo Data for The Last Metric
<p>Demonstration dataset for "The Last Metric", information theory based metric for quantifying the performance of Rubin-LSST survey strategies for redshift inference. Corresponding code is found at https://github.com/aimalz/TheLastMetric and a description of The Last Metric approach in https://arxiv.org/abs/2104.08229 </p>
NCSRD-DS-5GDDoS: 5G Radio and Core metrics containing sporadic DDoS attacks
<p><strong>NCSRD-DS-5GDDos v3.0 Dataset</strong><br>===<br>NCSRD-DS-5GDDos is a comprehensive dataset recorded in a real-world 5G testbed that aligns with the 3GPP specifications. The dataset captures Distributed Denial of Service (DDoS) attacks initiated by malicious connected users (UEs). </p> <p>The setup comprises of 3 cells with a total of 9 UEs connected to the same core network. The 5G network is implemented by the Amarisoft Callbox Mini solution (cell 2), and we further employ a second cell using the Amarisoft Classic (cell 1 & 3), that also hosts the 5G core.</p> <p>The setup utilizes a broad set of UE devices comprising a set of smart phones (Huawei P40), microcomputers (Raspberry Pi 4 - Waveshare 5G Hat M2), industrial 5G routers (Industrial Waveshare 5G Router), a WiFi-6 mobile hotspot (DWR-2101 5G Wi-Fi 6 Mobile Hotspot) and a CPE box (Waveshare 5G CPE Box). All UEs are being operated by subsidiary hosts which are responsible for the traffic generation, occurring from scheduled communications times.</p> <p>All identifiers are artificially generated and do not represent or based on personal data. We identify each UE through its ‘imeisv’ ID, that corresponds to the device in use, due to vendor implementation, that uses the same IMSI for all UEs.</p> <p>This dataset captures attack data from a total of 5 malicious User Equipment (UE) devices that initiated various flooding attacks on a 5G network. Each record includes key identifiers such as the IMEISV (International Mobile Equipment Identity Software Version number) and IP address of the attacking UE, along with the device type. The file "summary_report.csv" summarizes this information. The traffic types used in the attacks include syn flooding, UDP flooding, ICMP flooding, DNS flooding, and GTP-U flooding. The benign users stream YouTube and Skype traffic.</p> <p>The dataset is recorded through the use of a data collector that interfaces with the 5G network and gathers data regarding UEs, gNBs and the Core Network. The data are recorded in an InfluxdB and pre-processed into three separate tabular .csv files for more efficient processing: “amari_ue_data.csv”, “enb_counters.csv” and “mme_counters.csv”. In this version, we use an Amarisoft Classic (cells 1 & 3, Core Network) and an Amarisoft Mini (cell 2) (more information on the products can be found in https://www.amarisoft.com/).</p> <p>The ”amari_ue_data.csv” provides information on the UEs regarding identification (“imeisv”, “5g_tmsi”, “rnti”), IP addressing, bearer information, cell information (“tac”, “ran_plmn”), and cell information (“ul_bitrate”, “dl_bitrate”, “cell_id”, retransmissions per user per cell “ul_retx” as well as aggregated bit rates for each cell).</p> <p>The ”enb_counters.csv” focuses on cell-level information, providing downlink and uplink bitrates, usage ratio per user, cpu load of the gNB.</p> <p>We provide separate files of ”amari_ue_data.csv” and ”enb_counters.csv” generated from each gNB (Amarisoft Classic and Mini).</p> <p>The “mme_counters.csv” provides information on the Non-Access Stratum (NAS) of the 5G Network and focuses on session status reports (e.g., number of PDU session establishments, paging, context setup. This part gives an overview of the connection management throughout the recording session, and provides information on features suggested by 3GPP for abnormal user behavior.</p> <p>We also provide a separate pre-processed dataset, that merges the two "amari_ue_data_*.csv" file, including labeling of the malicious/benign samples, and may be more flexible for interested data scientists.</p> <p>Please refer to README.txt for the features included in each file.</p> <p>If you use this dataset, please also cite the following papers:</p> <p>M. Christopoulou, A. Garos, A. Vekraki, D. Santorinaios, I. Koufos, S. Karamitsiani, G. Xilouris, M.-A. Kourtis, G. Gardikis, and P. Trakadas, “User Terminals as Attackers: An Open Dataset Analysis of DDoS Attacks in 5G Networks,” in Proc. 2024 IEEE Conf. Standards Commun. Netw. (CSCN), 2024, pp. 301–307, doi: 10.1109/CSCN63874.2024.10849694.</p> <p>G. Xylouris, A. Vekraki, M. Christopoulou, M. A. Kourtis, E. K. Markakis and P. Trakadas, "Advancing Predictive Security for Consumer Applications in Beyond 5G/6G Networks With Annotated Datasets," in IEEE Transactions on Consumer Electronics, doi: 10.1109/TCE.2025.3567151.</p>
Relxill_NK Johannsen metric transfer function FITS files
<p>Transfer function FITS files to be used with the X-ray reflection model relxill_nk (<a href="https://github.com/ABHModels/relxill_nk">ABHModels/relxill_nk</a>).</p>
Metrics Literacies: On the State of the Art of Multimedia Scholarly Metrics Education (Video presentation)
<p>Scholarly metrics, such as h-index or impact factor, are widely applied in academic tenure and funding decisions but often inappropriately. The quantification of research impact has created a pressure to publish that harms all scholarly disciplines by creating adverse effects, such as plagiarism, gratuitous self-citation, and honorary authorship. The Metrics Literacies project led by Prof. Stefanie Haustein aims to reduce the misuse of metrics and adverse effects by improving the understanding and use of scholarly metrics in academia through the development, testing, and dissemination of multimedia resources. This poster talk shows the current state of the art on the project.</p>
Fig. 6 in Fish passage post-construction issues: analysis of distribution, attraction and passage efficiency metrics at the Baguari Dam fish ladder to approach the problem
Fig. 6. Abundance of fish groups (a) and species (b) along different stretches of the Baguari Dam fish ladder. Each stretch included 27 pools. Lower = first stretch after the fish ladder entrance. Upper = last stretch connected to the reservoir. Number in parenthesis indicates richness for each group.
Fig. 3 in Fish passage post-construction issues: analysis of distribution, attraction and passage efficiency metrics at the Baguari Dam fish ladder to approach the problem
Fig. 3. Temporal variation of fish groups abundance inside the fish ladder (a) and downstream (b; CPUEn) of Baguari Dam, and total dam discharge (c) during the reproductive (RS / gray rectangle) and non-reproductive (NRS) season.
Fig. 5 in Fish passage post-construction issues: analysis of distribution, attraction and passage efficiency metrics at the Baguari Dam fish ladder to approach the problem
Fig. 5. Abundance variation of fish groups (right column) and species (left column) in the tailrace and spill bay area of the Baguari Dam for the reproductive and non-reproductive season compared to the turbine and spillway discharge (bottom). Gray rectangle indicates the reproductive season.
Fig. 4 in Fish passage post-construction issues: analysis of distribution, attraction and passage efficiency metrics at the Baguari Dam fish ladder to approach the problem
Fig. 4. Temporal variation of fish groups (a and d - non-migratory; b and e - migratory; c and f,- migratory allochthonous) richness (S) downstream and inside the fish ladder of Baguari Dam along the reproductive (RS - left column) and nonreproductive (NRS - right column) season.
Repository Analytics and Metrics Portal (RAMP) 2021 data
<p>The Repository Analytics and Metrics Portal (RAMP) is a web service that aggregates use and performance use data of institutional repositories. The data are a subset of data from RAMP, the Repository Analytics and Metrics Portal (<a href="http://ramp.montana.edu/">http://rampanalytics.org</a>), consisting of data from all participating repositories for the calendar year 2021. For a description of the data collection, processing, and output methods, please see the "methods" section below.</p> <p>The record will be revised periodically to make new data available through the remainder of 2021.</p>
Data from: Measuring nestedness: a comparative study of the performance of different metrics
Nullnest <p>The <code>nullnest</code> repository provides a set of programs developed for constructing a <strong>null model</strong> of bipartite networks which constraints the degree sequences -on average-, along with the measurement of the degree of <strong>nestedness</strong> of bipartite networks using a large variety of metrics allowing for the comparison of this value against the null expectation. The whole repository is documented and maintained at: <span><a href="https://github.com/cclaualc/nullnest">https://github.com/cclaualc/nullnest</a>.</span></p> <p>The package is divided into two main blocks. On the one hand, we provide a program to compute the null model, for any bipartite network introduced by the user, which keeps the original degree sequence constant on average while maximizing the entropy of the null ensemble. We also give the ready-to-use results of the null model for an important number of real networks available online. On the other hand, the package contains the programs to measure the degree of nestedness of any network, measuring as well the first two moments of its null distribution, either by using analytical expressions or by numerically sampling the null ensemble.</p> <p>For more information on the functioning, modificable options, implementation and references of this repository visit the <a href="https://github.com/cclaualc/nullnest">github link</a>. When using these programs please acknowledge the authors.</p>
Text-fig. 2. Definition of the metric characters applied in this study, for non-metric characters see Appendix 1. in Genus Apodemus In The Pleistocene Of Central Europe: When Did The Extant Taxa Appear?
Text-fig. 2. Definition of the metric characters applied in this study, for non-metric characters see Appendix 1.
Text-fig. 9. Mean individual differences in non-metric and metric variables of M1 from respective mean values of extant A. flavicollis, A. sylvaticus and A. uralensis in the Recent samples (left) and fossils of particular Pleistocene biozones (right), superimposed to variation ranges and centroids of the former ones. in Genus Apodemus In The Pleistocene Of Central Europe: When Did The Extant Taxa Appear?
Text-fig. 9. Mean individual differences in non-metric and metric variables of M1 from respective mean values of extant A. flavicollis, A. sylvaticus and A. uralensis in the Recent samples (left) and fossils of particular Pleistocene biozones (right), superimposed to variation ranges and centroids of the former ones.
Text-fig. 7. Plot of discriminant scores (R1/R2) of individual M1 of Apodemus spp. from particular Pleistocene biozones superimposed onto a plot of variation ranges for the respective variables for the Recent Apodemus sample (based on the discrimination analysis of total set of characters, both metric and non-metric). in Genus Apodemus In The Pleistocene Of Central Europe: When Did The Extant Taxa Appear?
Text-fig. 7. Plot of discriminant scores (R1/R2) of individual M1 of Apodemus spp. from particular Pleistocene biozones superimposed onto a plot of variation ranges for the respective variables for the Recent Apodemus sample (based on the discrimination analysis of total set of characters, both metric and non-metric).
Text-fig. 4. Frequency diagram of metric variation (M1 length = M1U) in samples of Apodemus spp. representing particular Pleistocene biozones (MN 17 – Q 3), compared to variation span in the Recent samples of A. uralensis, A. sylvaticus and A. flavicollis (a heading strip). in Genus Apodemus In The Pleistocene Of Central Europe: When Did The Extant Taxa Appear?
Text-fig. 4. Frequency diagram of metric variation (M1 length = M1U) in samples of Apodemus spp. representing particular Pleistocene biozones (MN 17 – Q 3), compared to variation span in the Recent samples of A. uralensis, A. sylvaticus and A. flavicollis (a heading strip).
Text-fig. 6. Plot of discriminant scores (R1/R2) of individual m1 and M1 teeth of Apodemus spp. from particular Pleistocene biozones superimposed onto a plot of variation ranges for the respective variables for the Recent Apodemus sample (based on the discrimination analysis of metric variables of M1 and m1). in Genus Apodemus In The Pleistocene Of Central Europe: When Did The Extant Taxa Appear?
Text-fig. 6. Plot of discriminant scores (R1/R2) of individual m1 and M1 teeth of Apodemus spp. from particular Pleistocene biozones superimposed onto a plot of variation ranges for the respective variables for the Recent Apodemus sample (based on the discrimination analysis of metric variables of M1 and m1).
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.