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9,882 results for “Scale”
Extracted Information for the Systematic Review of Survey Scales for Measuring Information Privacy Concerns on Social Network Sites
<p>The data set is part of a systematic literature review of survey scales for measuring information privacy concerns (IPCs) used in research on social network sites (SNSs).</p> <p>The results of this systematic literature review are available in Bartol, J., Vehovar, V., & Petrovčič, A. (2023). Systematic review of survey scales measuring information privacy concerns on social network sites. <em>Telematics and Informatics</em>, 102063. https://doi.org/10.1016/j.tele.2023.102063</p> <p>The article also includes a detailed description of the methods used in generating this data set.</p>
Integrating animal tracking datasets at a continental scale for mapping wildlife habitat
<div><em>Aim:</em></div> <div> </div> <div>The increasing availability of animal tracking datasets collected across many sites provides new opportunities to move beyond local assessments to enable detailed and consistent habitat mapping at biogeographic scales. However, integrating wildlife datasets across large areas and study sites is challenging, as species' varying responses to different environmental contexts must be reconciled. Here, we compare approaches for large-area habitat mapping and assess available habitat for a recolonizing large carnivore, the Eurasian lynx (Lynx lynx).</div> <div> </div> <div> <em>Location: </em>Europe</div> <div> </div> <div><em>Methods:</em></div> <div> </div> <div>We use a continental-scale animal tracking database (450 individuals from 14 study sites) to systematically assess modeling approaches, comparing (1) global strategies that pool all data for training vs. building local, site-specific models and combining them, (2) different approaches for incorporating regional variation in habitat selection, and (3) different modeling algorithms, testing nonlinear mixed effects models as well as machine-learning algorithms.</div> <div> </div> <div><em>Results:</em></div> <div> </div> <div>Both global and local modeling strategies allowed building transferable habitat models with overall similar predictive performance. Model performance was the highest using flexible machine-learning algorithms and when incorporating variation in habitat selection as a function of environmental variation. Our best-performing model used a weighted combination of local, site-specific habitat models. Our habitat maps identified large areas of suitable, but currently unoccupied lynx habitat, with many of the most suitable unoccupied areas located in regions that could foster connectivity between currently isolated populations.</div> <div> </div> <div><em>Main conclusions:</em></div> <div> </div> <div>We demonstrate that global and local modeling strategies can achieve robust habitat models at the continental scale and that considering regional variation in habitat selection improves broad-scale habitat mapping. More generally, we highlight the promise of large wildlife tracking databases for large-area habitat mapping. Our maps provide the first high-resolution, yet continental assessment of lynx habitat across Europe, providing a consistent basis for conservation planning for restoring the species within its former range.</div>
Data from Genome scale metabolic network modelling for metabolic profile predictions
<p>Data used to produce figures 4, 5 and 6 in the paper Genome scale metabolic network modelling for metabolic profile predictions.</p>
FiN-2: Larg-Scale Powerline Communication Dataset (Pt.1)
<p># FiN-2 Large-Scale Real-World PLC-Dataset</p> <p>## About<br> #### FiN-2 dataset in a nutshell:<br> FiN-2 is the first large-scale real-world dataset on data collected in a powerline communication infrastructure. Since the electricity grid is inherently a graph, our dataset could be interpreted as a graph dataset. Therefore, we use the word node to describe points (cable distribution cabinets) of measurement within the low-voltage electricity grid and the word edge to describe connections (cables) in between them. However, since these are PLC connections, an edge does not necessarily have to correspond to a real cable; more on this in our paper.<br> FiN-2 shows measurements that relate to the nodes (voltage, total harmonic distortion) as well as to the edges (signal-to-noise ratio spectrum, tonemap). In total, FiN-2 is distributed across three different sites with a total of 1,930,762,116 node measurements each for the individual features and 638,394,025 edge measurements each for all 917 PLC channels. All data was collected over a 25-month period from mid-2020 to the end of 2022.<br> We propose this dataset to foster research in the domain of grid automation and smart grid. Therefore, we provide different example use cases in asset management, grid state visualization, forecasting, predictive maintenance, and novelty detection. For more decent information on this dataset, please see our [paper](https://arxiv.org/abs/2209.12693).</p> <p>* * *<br> ## Content<br> FiN-2 dataset splits up into two compressed `csv-Files`: *nodes.csv* and *edges.csv*.</p> <p>All files are provided as a compressed ZIP file and are divided into four parts. The first part can be found in this repo, while the remaining parts can be found in the following:<br> - <a href="https://zenodo.org/record/8328105">https://zenodo.org/record/8328105</a><br> - <a href="https://zenodo.org/record/8328108">https://zenodo.org/record/8328108</a><br> - <a href="https://zenodo.org/record/8328111">https://zenodo.org/record/8328111</a><br> <br> ### Node data</p> <p>| id | ts | v1 | v2 | v3 | thd1 | thd2 | thd3 | phase_angle1 | phase_angle2 | phase_angle3 | temp |<br> |----|----|----|----|----|----|----|----|----|----|----|----|----|----|<br> |112|1605530460|236.5|236.4|236.0|2.9|2.5|2.4|120.0|119.8|120.0|35.3|<br> |112|1605530520|236.9|236.6|236.6|3.1|2.7|2.5|120.1|119.8|120.0|35.3|<br> |112|1605530580|236.2|236.4|236.0|3.1|2.7|2.5|120.0|120.0|119.9|35.5|</p> <p>- id / ts: Unique identifier of the node that is measured and timestemp of the measurement<br> - v1/v2/v3: Voltage measurements of all three phases<br> - thd1/thd2/thd3: Total harmonic distortion of all three phases<br> - phase_angle1/2/3: Phase angle of all three phases<br> - temp: Temperature in-circuit of the sensor inside a cable distribution unit (in °C)</p> <p>### Edge data<br> | src | dst | ts | snr0 | snr1 | snr2 | ... | snr916 |<br> |----|----|----|----|----|----|----|----|<br> |62|94|1605528900|70|72|45|...|-53|<br> |62|32|1605529800|16|24|13|...|-51|<br> |17|94|1605530700|37|25|24|...|-55|</p> <p>- src & dst & ts: Unique identifier of the source and target nodes where the spectrum is measured and time of measurement<br> - snr0/snr1/.../snr916: 917 SNR measurements in tenths of a decibel (e.g. 50 --> 5dB).</p> <p>### Metadata<br> Metadata that is provided along with the data covers:</p> <p>- Number of cable joints<br> - Cable properties (length, type, number of sections)<br> - Relative position of the nodes (location, zero-centered gps)<br> - Adjacent PV or wallbox installations<br> - Year of installation w.r.t. the nodes and cables</p> <p>Since the electricity grid is part of the critical infrastructure, it is not possible to provide exact GPS locations.</p> <p>* * *<br> ## Usage<br> Simple data access using pandas:</p> <p>```<br> import pandas as pd</p> <p>nodes_file = "nodes.csv.gz" # /path/to/nodes.csv.gz<br> edges_file = "edges.csv.gz" # /path/to/edges.csv.gz</p> <p># read the first 10 rows<br> data = pd.read_csv(nodes_file, nrows=10, compression='gzip')</p> <p># read the row number 5 to 15<br> data = pd.read_csv(nodes_file, nrows=10, skiprows=[i for i in range(1,6)], compression='gzip')</p> <p># ... same for the edges<br> ```</p> <p>Compressed csv-data format was used to make sharing as easy as possible, however it comes with significant drawbacks for machine learning. Due to the inherent graph structure, a single snapshot of the whole graph consists of a set of node and edge measurements. But due to timeouts, noise and other disturbances, nodes sometimes fail in collecting the data, wherefore the number of measurements for a specific timestamp differs. This, plus the high sparsity of the graph, leads to a high inefficiency when using the csv-format for an ML training.<br> To utilize the data in an ML pipeline, we recommend other data formats like [datadings](https://datadings.readthedocs.io/en/latest/) or specialized database solutions like [VictoriaMetrics](https://victoriametrics.com/).</p> <p><br> ### Example use case (voltage forecasting)</p> <p>Forecasting of the voltage is one potential use cases. The Jupyter notebook provided in the repository gives an overview of how the dataset can be loaded, preprocessed and used for ML training. Thereby, a MinMax scaling was used as simple preprocessing and a PyTorch dataset class was created to handle the data. Furthermore, a vanilla autoencoder is utilized to process and forecast the voltage into the future.</p> <p> </p> <p> </p>
Scaled laboratory experiments of analogue magma intrusion in granular material: X-ray Computed Tomography imagery and displacement data
<p>This data set contains X-ray Computed Tomography (CT) images and surface displacement data of 15 scaled laboratory experiments of analogue magma intrusion in granular material. The experimental methodology and the experimental results were described in detail by Poppe et al. (2019). Displacement data of experiment SPCTIN14 was used by Poppe et al. (2023).<br> When using the experimental imagery or their derivatives please reference at a minimum Poppe et al. (2019) and this data set (Poppe et al., 2023, Zenodo data set).<br> The included explanatory notice reproduces the experimental method and presents the structure and file types contained in this data set.</p>
Evolutionary history and analysis of iridescent ultraviolet butterfly scales
<p>Iridescent ultraviolet (IUV) patterns on pierid butterfly wings are phenotypic adaptations commonly used as sexual signals, generated by scales with ultrastructural modifications. Pierid IUV patterns are sexually dichromatic, with reduced size in females, where conspicuous sexual signaling balances courtship against ecological predation. There have been no phylogenetic reconstructions of IUV within Pieridae and little morphological characterization of phenotypic diversity. Our genus-wide characterization of IUV revealed the uniform similarity of stacked lamellar ridges on the dorsal surface of cover scales. We tested a hypothesis of single versus multiple origins by reconstructing a phylogeny of 534 species (~43.2% described species), with all genera represented, and a trait matrix of 734 species (~59.4%) screened for IUV. A single, early dimorphic origin of IUV followed by several losses and gains received strong support, concluding that IUV patterns and structural coloration are old traits. Collectively, these results support the homology of IUV scales and patterns that diversified within several lineages, suggesting an interplay between female-mediated sexual selection and ecological predatory selection.</p>
Physical Effort Scale (PES)
<p>Dataset and script related to the development and validation of the Physical Effort Scale (PES)</p> <p>This dataset includes:</p> <p><strong>For the sample 1</strong></p> <p>- the raw data for the sample 1 "Physical Effort Scale Data Fall 2022.xlsx"</p> <p>- The R Markdown script for the analyses "pes_effort_quanti_sample1.Rmd" and the resulting html "pes_effort_quanti_sample1.html"</p> <p> </p> <p><strong>For the sample 2</strong></p> <p>- the raw data for the sample 2 "Échelle d'effort physique - Partie 1_Printemps.xlsx"; "Échelle d'effort physique - Partie 2_Printemps.xlsx"; "Échelle d'effort physique - Partie 2.1.xlsx"; and "Échelle d'effort physique - Partie 2.2.xlsx".</p> <p>- The R Markdown script for the analyses "pes_effort_quanti_sample2.Rmd" and the resulting html "pes_effort_quanti_sample2.html"</p>
Population links between an insectivorous bird and moths disentangled through national scale monitoring data
<p>Code in support of "Population links between an insectivorous bird and moths disentangled through national scale monitoring data"</p> <p>Luke Christopher Evans, Malcolm D. Burgess, Simon G. Potts, William E. Kunin, Tom H. Oliver</p>
KaJaPa (Kanonen Jagdpanzer) scale model scan
My 3D reconstruction generated with photogrammetry software 3DF Zephyr v4.513 processing 287 images taken froma scale model in 1/35. Source: Objaverse 1.0 / Sketchfab
The Effect of Bristle Brush, Rubber Cup, and Air Polishing on Tooth Surface Roughness of Scaled Teeth
ClinicalTrials.gov study NCT06857474. IPD Sharing: YES. Countries: 1. Publications: 5.
Comparing Treatments for HIV-Infected Opioid Users in an Integrated Care Effectiveness Study (CHOICES) Scale-Up
ClinicalTrials.gov study NCT03275350. IPD Sharing: NO. Countries: 1. Publications: 3.
Scale-up of Treatment of Hepatitis C Infection Among People Who Inject Drugs
ClinicalTrials.gov study NCT02940691. IPD Sharing: NO. Countries: 1. Publications: 1.
Scaling up Maternal Health Equity Best Practices
ClinicalTrials.gov study NCT06541951. IPD Sharing: NO. Countries: 1. Publications: 1.
Evaluate the Efficacy and Safety of Large-Scale Field-Directed Topical Therapy of Actinic Keratosis of the Chest w/Ingenol Mebutate 0.015%
ClinicalTrials.gov study NCT02446223. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Testing Means to Scale Early Childhood Development Interventions in Rural Kenya
ClinicalTrials.gov study NCT03548558. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Measuring the Priorities of Patients With Type II Diabetes Using Likert Scale and Best-worst Scaling
ClinicalTrials.gov study NCT02637609. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Effect of Liraglutide on Body Weight in Non-diabetic Obese Subjects or Overweight Subjects With Co-morbidities: SCALE™ - Obesity and Pre-diabetes
ClinicalTrials.gov study NCT01272219. IPD Sharing: Not stated. Countries: 29. Publications: 17.
Clinical Use of Baxter Animated Retching Faces (BARF) Scale in Children
ClinicalTrials.gov study NCT02421952. IPD Sharing: NO. Countries: 1. Publications: 1.
Turkish Validation of The Drooling Impact Scale
ClinicalTrials.gov study NCT04132765. IPD Sharing: NO. Countries: 1. Publications: 3.
Health Information Technology for COVID-19 Testing in Schools (SCALE-UP Counts)
ClinicalTrials.gov study NCT05112900. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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.