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47,155 results for “evaluations”
Experimentally evaluating effects of postfire drought on establishment, growth and survival of two widespread western conifer species; lodgepole pine and Douglas-fir
This research seeks to understand how drought following wildfires affects the establishment of two widespread Rocky Mountain tree species, lodgepole pine and Douglas-fir. In Yellowstone National Park, warming, drying conditions are leading to increased frequency of severe wildfires that kill existing trees and trigger regeneration of the forest. These trends are expected to continue. Many tree species in Yellowstone are well adapted to fire. Yet, early and abundant seedling establishment after fire is critical for forests to recover. Tree seedlings are particularly sensitive to drought, and evidence suggests that sufficiently dry postfire conditions could cause widespread tree-seedling mortality, with ecological consequences that may last decades. This research will determine how seedlings from two tree species respond to drought conditions expected in the mid-21st century and will identify the physiological mechanisms that drive seedling response to dry conditions. The results of this study will advance understanding of how forests in the western United States will respond to environmental change over coming decades and provide useful information for western land managers who are grappling with increased wildfires.
Experimentally evaluating effects of postfire drought on establishment, growth and survival of two widespread western conifer species; lodgepole pine and Douglas-fir (greenhouse portion of experiment)
This research seeks to understand how drought following wildfires affects the establishment of two widespread Rocky Mountain tree species, lodgepole pine and Douglas-fir. In Yellowstone National Park, warming, drying conditions are leading to increased frequency of severe wildfires that kill existing trees and trigger regeneration of the forest. These trends are expected to continue. Many tree species in Yellowstone are well adapted to fire. Yet, early and abundant seedling establishment after fire is critical for forests to recover. Tree seedlings are particularly sensitive to drought, and evidence suggests that sufficiently dry postfire conditions could cause widespread tree-seedling mortality, with ecological consequences that may last decades. This research will determine how seedlings from two tree species respond to drought conditions expected in the mid-21st century and will identify the physiological mechanisms that drive seedling response to dry conditions. The results of this study will advance understanding of how forests in the western United States will respond to environmental change over coming decades and provide useful information for western land managers who are grappling with increased wildfires.
LAGOS Lake nutrient, carbon and chlororphyll data to evaluate biases in lake water quality sampling practices in a 17-state region of the US
This dataset includes data for eight major limnological variables in LAGOS-NE_LIMNO v. 1.087.1 that were used to evaluate biases in lake water quality sampling and implications for macroscale research (Stanley et al. In Revision, Limnology and Oceanography). Most observations came from LAGOS-NE_LIMNO v. 1.087.1, an integrated database of lake ecosystems (Soranno et al. 2015, Soranno et al. 2017) but were supplemented with additional data from the State of New Hampshire. LAGOS-NE contains information on lakes great than or equal to 1 ha (originally derived from the U.S. Geological Survey's 2013 National Hydrography Dataset) for a 17-state region of the U.S., and a subset of the lakes has observational data on lake chemistry and productivity. Approximately 87 different sources of data were compiled for the LAGOS-NELIMNO v. 1.087.1 dataset and were mostly generated by government agencies (state, federal, tribal) and universities. In this analysis, we compiled data for eight major limnological variables (Secchi disk depth, chlorophyll, total phosphorus, total nitrogen, nitrate, ammonium, true water color, and dissolved organic carbon) and geographic characteristics of lakes (location, lake area, depth, perimeter, watershed area) to evaluate biases in different limnological properties over space and time.
temporalNEON: Repository containing raw and cleaned-up organismal data from the National Ecological Observatory Network (NEON) useful for evaluating the links between change in biodiversity and ecosystem stability
Organismal data include the following taxonomic groups: small mammals, fish, ground beetles, and aquatic macroinvertebrates. Data were retrieved from the National Ecological Observatory Network (NEON) database in November 2020. We submit both raw data retrieved from NEON as .rds files, R code used to process these data, as well as processed data as .csv files.
What could explain 13C signature of dryland biocrusts? Evaluating potential resource transfer between plants and biocrusts.
Dryland ecosystems are increasing in geographic extent and contribute greatly to interannual variability in global carbon dynamics. Disentangling interactions among dominant primary producers can help partition their contributions to dryland C dynamics. We measured the δ13C signatures of biological soil crusts and dominant plant species (C3 and C4) across a regional scale in the southwestern USA to determine if biocrust cyanobacteria were coupled to plant productivity (using plant-derived C mixotrophically), or independent of plant activity (and therefore purely autotrophic).
Evaluation of the impact of imprinted polymer particles on morphology and motility of breast cancer cells by using digital holographic cytometry
<p>Supplemented Videos used in "Evaluation of the impact of imprinted polymer particles on morphology and motility of breast cancer cells by using digital holographic cytometry"</p>
Dataset for WP6 KPI evaluation
<p>Performance of all WP6 use cases was evaluated through KPIs which were defined (together with the methodology) in the beginning of the InterFlex project. The detail definition of WP6 KPIs is included in deliverable D6.1 Design of solution as KPI annex. Following KPIs for WP6 were defined:<br> 1. KPI6_1: Increasing DER hosting capacity</p> <p>2. KPI6_2: Power quality (according to the standard EN 50160)3. KPI6_</p> <p>3: EV charging stations load curtailment in emergency situations</p> <p>4. KPI6_4: PV production peak shaving</p> <p><br> Dataset for each KPI evaluation is included in list of this excel sheet.<br> KPI evaluation conclusionAll expectations in terms of WP6 KPIs were fulfilled. Based on KPIs evaluation it is obvious that WP6 solutions are very promising to increase the DER hosting capacity and control EV charging. KPI evaluation for WP6 is an important input for Scalability and Replicability Analysis and for Cost Benefit Analysis which are further key inputs for the definition of viable business models part of the D6.3 deliverable.</p>
KPI evaluation within the InterFlex project under the D2.5 deliverable
<p>This dataset contains data used for KPI evaluation within the InterFlex project under the D2.5 deliverable.</p>
A dataset for evaluating one-shot categorization of novel object classes
<p>From just a single example, we can derive quite precise intuitions about what other class members look like. This stands in stark contrast to machine learning algorithms, which typically require tens or even hundreds of thousands of examples to learn a new category. One of the most important open questions in our field is: How do humans achieve this? The stimuli and data provided here (in MATLAB format) are from thousands of crowd-sourced human responses to novel objects. The data can be used to test machine learning generalization as compared to human and also can be used as a test bed for various kinds of category learning models. </p>
An evaluation of the accuracy and speed of metagenome analysis tools
<p>Metagenome studies are becoming increasingly widespread, yielding important insights into microbial communities covering diverse environments from terrestrial and aquatic ecosystems to human skin and gut. With the advent of high-throughput sequencing platforms, the use of large scale shotgun sequencing approaches is now commonplace. However, a thorough independent benchmark comparing state-of-the-art metagenome analysis tools is lacking. Here, we present a benchmark where the most widely used tools are tested on complex, realistic data sets. Our results clearly show that the most widely used tools are not necessarily the most accurate, that the most accurate tool is not necessarily the most time consuming and that there is a high degree of variability between available tools. These findings are important as the conclusions of any metagenomics study are affected by errors in the predicted community composition and functional capacity.</p>
Exploring the influence of core-self evaluations, situational factors, and coping on nurse burnout: A cross-sectional survey study
<p>Stress has become an inherent aspect of the nursing profession. Chronically experienced work stress can lead to burnout. Although situational stressors show a significant influence on burnout, their power to predict the complete syndrome is rather limited. After all, stressors only exist “in the eye of the beholder”. This study aimed to explore how individual vulnerability factors such as core-self evaluations and coping, contribute to burnout in relation to situational stressors within a population of hospital nurses.</p> <p>Cross-sectional data was collected in 2014, using five validated self-report instruments: Dutch Core Self Evaluations Scale, Nursing Work Index Revised, Utrecht Coping List, Ruminative Response Scale, and Utrecht Burnout Scale. 219 of the 250 questionnaires were returned. This database is available here.</p> <p>Core-self evaluations, situational factors and coping each contributed significantly to the predictive capacity of the models of the separate burnout dimensions. Core-self evaluations was significantly related to emotional exhaustion. It was suggested that Core-self evaluations might be placed at the initiation of the loss cycle. However, further research is warranted.</p>
Feature Model for Scrum Adaptation - Evaluation Survey
<p>Feature Model for Scrum Adaptation - Evaluation Survey</p>
Human annotation data for ACL 2020 paper "Designing Precise and Robust Dialogue Evaluators"
<p>Human annotation data for ACL 2020 paper "Designing Precise and Robust Dialogue Evaluators"</p>
Statistics and Evaluation Data for Publication "Using Supervised Learning to Classify Metadata of Research Data by Field of Study"
<p>Automated classification of metadata of research data by their discipline(s) of research can be used in scientometric research, by repository service providers, and in the context of research data aggregation services. Openly available metadata of the DataCite index for research data were used to compile a large training and evaluation set comprised of 609,524 records. This publication contains aggregated data for the paper. It also contains the evaluation data of all model/hyper-parameter training and test runs.</p>
WRF v3.9 evaluation statistics, April and October 2015 Ireland
<p>Dataset related to Chapter 4 of PhD thesis "Modelling of accidental radioactive releases for Ireland" By Cillian Joy.<br> This folder contains a file for each statistic used. Time series data is aggregated for each month of the WRF v3.9 evaluation, April and October 2015.<br> Data is for each station (28), meteorology variable (wind speed, wind direction, and precipitation) for each statistic (7 for wind speed and precipitation, and 3 for wind direction.<br> For each location, the value provided is for the month. For example, Fractional Bias for one location for the month is an average of all time steps (1464).</p> <p>The seven statistic are:</p> <ol> <li>Factor of data within a factor (FAC 1.5)</li> <li>Factor of data within a factor (FAC 2.0)</li> <li>Fractional Bias (FB)</li> <li>Normalised Mean Square Error (NMSE)</li> <li>Correlation (R)</li> <li>Geometric Mean Bias (GMB)</li> <li>Geometric Mean Variance (GMV)</li> </ol> <p>21 May 2020, Cillian Joy, cillian.joy@nuigalway.ie</p>
Scrum roles adaptations survey and feature model evaluation
<p>Scrum roles adaptations survey and feature model evaluation</p>
Distorted sound for evaluation of CNN
<p>Distorted sound for evaluation of CNN, original files are from urbansound8k. </p>
Cloud-hosted DBMS Performance, Scalability and Availability Evaluation Data
<p>These data sets contain evaluation data for the evaluation objectives performance, scalability and availability of different cloud-hosted, distributed DBMS. </p> <p>The data sets contain the raw performance metrics, monitoring data and evaluation metadata. For the higher-level evaluation objectives scalability and availability, supportive plots are provided.</p> <p><br> The performance and scalability evaluations have been carried out with the <a href="https://dl.acm.org/doi/10.1145/3297663.3310303">Mowgli framework</a> and the availability evaluation have been carried out with the <a href="https://dl.acm.org/doi/abs/10.1145/3341105.3373968">King Louie framework</a>.</p>
Replication Package for "Function-as-a-Service Performance Evaluation: A Multivocal Literature Review"
<p><strong>Paper</strong></p> <p>J. Scheuner and P. Leitner, “Function-as-a-Service Performance Evaluation: A Multivocal Literature Review,” Accepted at the <a href="https://www.journals.elsevier.com/journal-of-systems-and-software">Journal of Systems and Software (JSS)</a>, Preprint: <a href="https://arxiv.org/abs/2004.03276">arXiv:2004.03276</a>.</p> <blockquote> <p>Function-as-a-Service (FaaS) is one form of the serverless cloud computing paradigm and is defined through FaaS platforms (e.g., AWS Lambda) executing event-triggered code snippets (i.e., functions). Many studies that empirically evaluate the performance of such FaaS platforms have started to appear but we are currently lacking a comprehensive understanding of the overall domain. To address this gap, we conducted a multivocal literature review (MLR) covering 112 studies from academic (51) and grey (61) literature. We find that existing work mainly studies the AWS Lambda platform and focuses on micro-benchmarks using simple functions to measure CPU speed and FaaS platform overhead (i.e., container cold starts). Further, we discover a mismatch between academic and industrial sources on tested platform configurations, find that function triggers remain insufficiently studied, and identify HTTP API gateways and cloud storages as the most used external service integrations. Following existing guidelines on experimentation in cloud systems, we discover many flaws threatening the reproducibility of experiments presented in the surveyed studies. We conclude with a discussion of gaps in literature and highlight methodological suggestions that may serve to improve future FaaS performance evaluation studies.</p> </blockquote> <pre>@article{scheuner:20-jss, author = {Joel Scheuner and Philipp Leitner}, title = {Function-as-a-Service Performance Evaluation: A Multivocal Literature Review}, journal = {J. Syst. Softw.}, volume = {170}, pages = {110708}, year = {2020}, doi = {10.1016/j.jss.2020.110708} }</pre> <p><strong>Dataset</strong></p> <p>All extracted data originating from academic and grey literature studies are available as machine-readable CSV (<a href="https://github.com/joe4dev/faas-performance-mlr/blob/master/data/faas_mlr_raw.csv">./data/faas_mlr_raw.csv</a>) and human-readable XLSX (<a href="https://github.com/joe4dev/faas-performance-mlr/blob/master/data/faas_mlr_raw.xlsx">./data/faas_mlr_raw.xlsx</a>). The Excel file also contains all 700+ comments with guidance, decision rationales, and extra information. It is configured with a filtered view to display only <em>relevant</em> sources but contains the complete data (i.e., including discussion for sources considered to be <em>not relevant</em> in our context).</p> <p>Find further documentation in the README.md or on Github: <a href="https://github.com/joe4dev/faas-performance-mlr/">https://github.com/joe4dev/faas-performance-mlr/</a></p> <p>The latest version is also available online as an interactive Google spreadsheet (GSheet): <a href="https://docs.google.com/spreadsheets/d/1EK9yg9fMZIDybnbi7thsnBx1NdqDmkW86sMygH9r8q8">https://docs.google.com/spreadsheets/d/1EK9yg9fMZIDybnbi7thsnBx1NdqDmkW86sMygH9r8q8</a></p> <p> </p>
Learning Unsupervised Knowledge-Enhanced Representations to Reduce the Semantic Gap in Information Retrieval (Evaluation datasets)
<p>This dataset contains all the runs, pools, plots and analyses to reproduce the results presented in the paper: "Learning Unsupervised Knowledge-Enhanced Representations to Reduce the Semantic Gap in Information Retrieval ", 2020.</p>
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.