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91,407 results for “Effects With / Effects Of”
Evaluating Multi-Tenant Live Migrations Effects on Performance
<p>Results for Evaluating Multi-Tenant Live Migrations Effects on Performance article (Coopis 2018)</p> <p>Framework used to generate this data available on <a href="https://github.com/guillaumerosinosky/migration_bpms">https://github.com/guillaumerosinosky/migration_bpms</a></p> <p>Code for data interpretation available on <a href="https://github.com/guillaumerosinosky/migration_bpms">https://github.com/guillaumerosinosky/migration_bpms/coopis2018/xp_paper.ipynb</a></p> <p>Files description :</p> <ul> <li>png images : BPM process schemas used for the experimentations (AdditionalApproval, HumanTask and M3Process)</li> <li>xp1.csv : data for the <em>Migration duration </em>experiment</li> <li>xp3.csv : data for the <em>Migration effects on migrated tenant</em> and <em>Migration effects on co-located tenants</em> experiments</li> </ul>
Integrated pedagogical methods effectiveness in Physics' preliminary undergraduate education within the context of large size lectures.
<p>Three files relating the first round of analysis testing active methods for large size lectures. The Presentation including the research design, methods, main results in synthesis is available here: https://www.researchgate.net/project/Getting-started-with-Physics-preliminary-undergraduated-strategies/update/5a44cac6b53d2f0bba475104</p> <p>2- Dataset on Students' Learning Outcomes. Dataset adopted in the first experimental round. The dataset includes data used for the first type of analysis (learning outcomes) carried out for the ICEM2017 Conference presentation "Integrating MOOCs in Physics preliminary undergraduate education: beyond large size lectures". The data includes the results of the initial, baseline Test, the final Test, and two other variables that could be used to analyse covariance: Sex and Type of Group (Large/Small).</p> <p>3- Dataset on Students' Opinion. Dataset adopted in the first experimental round. The dataset includes data used for the second type of analysis (students' opinion) carried out for the ICEM2017 Conference presentation "Integrating MOOCs in Physics preliminary undergraduate education: beyond large size lectures". The data includes the results of a final questionnaire gathering the students opinion on the four types of pedagogical factors affecting their experience within a large size lecture: MOOCs, Active Learning, Self-Assessment tools, Tutors’ guidance.</p> <p>4- Codes and analysis adopted in the first experimental round. The Document includes two analysis carried on for the ICEM2017 Conference presentation "Integrating MOOCs in Physics preliminary undergraduate education: beyond large size lectures". These are: Test (measuring students' knowledge on the subject taught) and Students' Opinion/satisfaction on the several pedagogical methods adopted along the experimental intervention.</p>
The effectiveness of freshwater connectivity as a predictor of species distribution
<p>The attached dataset contains three dataframes used in the affiliated papers.</p> <p>1) DirectSlopeData.rda - Recolonisation success of two species, northern pike and European perch, in rotenone-treated lakes in Sweden, alongside connectivity parameters for the associated lakes.</p> <p>2) HPD.rda - Credible intervals for the beta estimates generated by the BORAL model in 3.</p> <p>3) Presence/absence data for seven species in lakes throughout the Kautokeino catchment in Northern Norway, alongside selected environmental covariates for associated lakes.</p>
Effect of Low Irradiance on Seagrass from Rødsand lagoon (DK)
<p>This dataset provides the raw and postprocessed data from laboratory experiments investigating the effect of reduced light irradiance on the health status and biomechanical properties of seagrass species Zostera marina collected in Rødsand lagoon (DK). The experiments were conducted as part of Hydralab+ and were completed at Loughborough University. Plants used in experiments were supplied by DHI.</p> <p>During experiments seagrass health status was monitored with a chlorophyll fluorometer. The morphological properties of seagrass blades were measured using rulers, Vernier scales and a scale, and their flexural rigidity was measured via cantilever tests.</p>
Effects With and Effects Of Representation Control - wahlkarte05
<p>Data and analysis script for lab study "wahlkarte05", conducted at Leibniz-Institut für Wissensmedien, Tübingen.</p> <p>Immediate effects with and subsequent effects of representation control on time on task and proportion correct are inverstigated with a fully crossed experimental design. We also differentiated between reproduction and near transfer.</p> <p>For more information see meta information and codebooks.</p>
Effects With and Effects Of Representation Control - wahlkarte03
<p>Data and analysis script for lab study "wahlkarte03", conducted at Leibniz-Institut für Wissensmedien, Tübingen.</p> <p>Immediate effects with and subsequent effects of different types of representation control on time on task and proportion correct are inverstigated. We compared three types of representation control: 1)Active representation control: Participants adjusted visualization layout (displayed information and information organization) by using check boxes 2)Modeling: Participants adjusted visualization layout with one click, individual steps were shown 3) Short-circuit: Participants adjusted visualization layout with one click, individual steps were not shown</p> <p>For more information see meta information and codebooks.</p>
Effects With and Effects Of Representation Control - wahlkarte02
<p>Data and analysis script for lab study "wahlkarte02", conducted at Leibniz-Institut für Wissensmedien, Tübingen.</p> <p>Immediate effects with and subsequent effects of representation control on time on task and proportion correct are inverstigated.</p> <p>For more information see meta information and codebooks.</p>
Group size effects and critical mass in public goods games
<pre>This dataset accompanies the paper "Group size effects and critical mass in public goods games", https://doi.org/10.1038/s41598-019-41988-3 It records participant decisions in a set of binary one-shot Public Goods Games with curvilinear payoff function (see details in the paper). The explanation of the data fields is present also in the metadata of the file: # Cooperation: 0=defection, 1=cooperation, 99=the participant did not make the decision # Group: interacting group size (N) # Treatment: Critical Mass Nc # dropout: 1=the participant did not make the decision, 0=the participant made the decision # incompleted: 1=participant did not make all the decisions of the experiment; 0=participant did made all the decisions of the experiment # female: 1=female, 0=male </pre> <p> </p>
Processed data for the study on "Chromatin 3D interactions mediate genetic effects on gene expression"
<p>This repository contains the processed data that was generated as part of the following study:</p> <p>Delaneau et al. (2019) <strong>Chromatin 3D interactions mediate genetic effects on gene expression.</strong></p> <p><em>Abstract:</em> Studying the genetic basis of gene expression and chromatin organization is key to characterize the effect of genetic variability on the function and structure of the human genome. Here, we unravel how genetic variation perturbs gene regulation using a dataset combining activity of regulatory elements, gene expression and genetic variants across 317 individuals and two cell types. We show that variability in regulatory activity is structured at the intra- and inter-chromosomal levels within 12,583 Cis Regulatory Domains and 30 Trans Regulatory Hubs that highly reflect the local (i.e. Topologically Associating Domains) and global (i.e. open/close chromatin compartments) nuclear chromatin organization. These structures delimit cell type specific regulatory networks that control gene expression/co-expression and mediate the genetic effects of <em>cis</em>- and <em>trans</em>-acting regulatory variants on genes.</p> <p> </p> <p>This repository contains:</p> <ol> <li>Chromatin QTLs for H3K27ac, H3K4me1 and H3K4me3 discovered in 317 Lymphoblastoids Cell Lines (LCLs) and 78 Fibroblasts.</li> <li>Molecular QTLs affecting the activity and structure of Cis Regulatory Domains (CRDs) in LCLs.</li> <li>Basic information about the full set of genetic variants being analyzed in the study.</li> <li>The peak coordinates, their hierarchy based on inter-individual correlation and the CRD calls for both LCLs and Fibroblasts.</li> <li>The functional links discovered in LCLs between CRDs and genes.</li> <li>eQTLs for LCLs.</li> <li>A README file containing the description of the file format for each file.</li> </ol>
Large scale and information effects on cooperation in public good games
<pre>This dataset accompanies the paper "Large scale and information effects on cooperation in public good games", https://doi.org/10.1038/s41598-019-50964-w It records participant decisions in a set of Public Goods Games (see details in the paper). Explanation of the data fields: #participant_id: identification number for each participant in each treatment #player_alive: (for each round) 1=participant is still playing; 0=participant has been banned for forgetting three decisions or did not show up the first day. #group_avg_contribution: Average contribution #player_contribution: participant individual contribution to the PGG #round_number: round number #gender #age #treatment: see the publication for details regarding the different treatments # IBSEN metadata # 2017A2ECOCOOPANSA01ONL0000ESPMAD </pre>
Detecting edge effects of geese grazing at the boundary of woodland and grassland
<p>The presence of geese on different areas of lawn was estimated by the length of droppings on the lawn. Geese defecate frequently and seemingly indiscriminately. Counting dropping is a well-known method for estimating their density on areas of land (Owen, 1971). However, we found it difficult to distinguish individual defecation events as the dropping tend to break apart as they are released. Therefore, we measured the total length of dropping in an area. Geese dropping are more or less cylindrical and we consider a measure related to the volume of droppings is more reliable than a count of their number.</p> <p>Observations were conducted in July 2014 and March and April 2015 at Meise Botanic Garden, Meise, Belgium. Rectangular plots were laid out perpendicular to a woodland-lawn boundary on sections of a Botanic Garden frequently used by geese. These plots are detailed in file DroppingsPlots.csv. The sites for these plots were chosen because they were well separated from each other; were away from other trees and faced different directions. The plots were marked out using bamboo canes and a tape measure. Then either 20 or 30 randomly chosen 1 m<sup>2</sup> square quadrats were surveyed within the rectangular plot. The cumulative length of dropping in a quadrat was measured to the nearest centimeter with a ruler.</p> <p>The results are found in file DroppingsMeasurements.csv.</p> <p>The columns of this file are as follows</p> <p>Plot - The identifying number given to the plot</p> <p>X - The distance parallel to the woodland-lawn boundary</p> <p>Y - The distance from the woodland-lawn boundary</p> <p>Length - The total length in centimeters of the dropping found in a 1m<sup>2</sup> quadrat</p> <p>Prunella - coverage of <em>Prunella vulgaris</em> L. in the 1m<sup>2</sup> quadrat on the DAFOR Scale (dominant 5, abundant 4, frequent 3, occasional 2, rare 1, absent 0)</p> <p>Renoncule - coverage of <em>Ranunculus</em> sp. in the 1m<sup>2</sup> quadrat on the DAFOR Scale (dominant 5, abundant 4, frequent 3, occasional 2, rare 1, absent 0)</p> <p>Bellis - coverage of <em>Bellis perennis</em> L. in the 1m<sup>2</sup> quadrat on the DAFOR Scale (dominant 5, abundant 4, frequent 3, occasional 2, rare 1, absent 0)</p> <p>Lotus - coverage of <em>Lotus</em> sp. in the 1m<sup>2</sup> quadrat on the DAFOR Scale (dominant 5, abundant 4, frequent 3, occasional 2, rare 1, absent 0)</p> <p>Glechoma - coverage of <em>Glechoma hederacea</em> L. in the 1m<sup>2</sup> quadrat on the DAFOR Scale (dominant 5, abundant 4, frequent 3, occasional 2, rare 1, absent 0)</p> <p>Four species of geese are present in the Botanic Garden and may have contributed droppings to the observations. These species are <em>Alopochen aegyptiaca</em> (L. 1766) (Egyptian geese), <em>Branta canadensis</em> (L. 1758) (Canada geese), <em>Anser anser</em> (L. 1758) (greylag geese) and <em>Branta leucopsis</em> (Bechstein, 1803) (barnacle geese).</p>
Investigating the effect of cochlear synaptopathy on envelope following responses using a model of the auditory nerve
<p>Dataset containing the recorded and simulated data reported in the manuscript "Investigating the effect of cochlear synaptopathy on envelope following responses using a model of the auditory nerve" published in the Journal of the Association for Research in Otolaryngology, JARO (<a href="https://doi.org/10.1007/s10162-019-00721-7">https://doi.org/10.1007/s10162-019-00721-7</a>):</p> <ol> <li>RECORDED Envelope Following Responses (EFR) in normal-hearing (NH) threshold and hearing-impaired (HI) human listeners using deeply (m = 85%) and shallowly (m = 25%) modulated sinusoidally amplitude modulated (SAM) tones.</li> <li>SIMULATED EFRs using the auditory nerve (AN) model by Zilany et al. (2009, 2014).</li> </ol> <p>Files content and structure:</p> <p><strong>Recorded EFRs</strong></p> <p><strong>Fig. 2:</strong></p> <ul> <li><em>fig2__recorded_efr.csv</em>: <br> EFR recordings as a function of stimulus level (EFR magnitude-level functions) for the NH and HI listeners using two modulation depths.</li> </ul> <p>The file containing the recorded EFR data have the following columns:</p> <ul> <li><em>lvl</em>: Stimulation level</li> <li><em>m85_ok: </em>EFR magnitude (dB re to 1 µV) using m = 85%. Significant responses (F-test = 1)</li> <li><em>m85_ko: </em>EFR magnitude (dB re to 1 µV) using m = 85%. Non-significant responses (F-test = 0)</li> <li><em>m85_bkg: </em>Estimated background noise magnitude (dB re to 1 µV) for the recordings when m = 85%</li> <li><em>m25_ok: </em>EFR magnitude (dB re to 1 µV) using m = 25%. Significant responses (F-test = 1)</li> <li><em>m25_ko: </em>EFR magnitude (dB re to 1 µV) using m = 25%. Non-significant responses (F-test = 0)</li> <li><em>m25_bkg: </em>Estimated background noise magnitude (dB re to 1 µV) for the recordings when m = 25% </li> <li><em>subj: </em>Listener id</li> <li>hearing: Hearing group (nh | hi) of the listener</li> </ul> <p><strong>Simulated EFRs:</strong></p> <p><strong><em>Files with the simulation results summing across frequency and SR fiber type</em></strong></p> <p><strong>Fig. 4:</strong></p> <ul> <li><em>fig4a__simul_efr__nh_23ohc_13ihc__no_cs.csv</em>: <br> Simulated EFR magnitude-level function for the NH threshold listeners (average) assuming 2/3 of OHC loss and 1/3 of IHC loss (Fig. 4a).</li> <li><em>fig4b__simul_efr__nh_slp_thres_all_ohc__no_cs.csv</em>: <br> Simulated EFR magnitude-level function for the NH threshold listeners (average) assuming sloping threshold at extended high frequencies (EHF) and all of OHC loss (Fig. 4b).</li> <li><em>fig4c__simul_efr__nh_slp_thres_all_ihc__no_cs.csv</em>: <br> Simulated EFR magnitude-level function for the NH threshold listeners (average) assuming sloping threshold at extended high frequencies (EHF) and all of IHC loss (Fig. 4c).</li> <li><em>fig4d__simul_efr__hi_23ohc_13ihc__no_cs.csv</em>: <br> Simulated EFR magnitude-level function for the HI listeners (average) assuming 2/3 of OHC loss and 1/3 of IHC loss (Fig. 4d).</li> <li><em>fig4e__simul_efr__hi_all_ohc__no_cs.csv</em>: <br> Simulated EFR magnitude-level function for the HI threshold listeners (average) assuming all of OHC loss (Fig. 4e).</li> <li><em>fig4f__simul_efr__hi_all_ihc__no_cs.csv</em>: <br> Simulated EFR magnitude-level function for the HI threshold listeners (average) assuming all of IHC loss (Fig. 4f).</li> <li><em>fig4g__simul_efr__hi_slp_thres_23ohc_13ihc__no_cs.csv</em>: <br> Simulated EFR magnitude-level function for the HI listeners (average) assuming sloping threshold at EHF and 2/3 of OHC loss and 1/3 of IHC loss (Fig. 4d).</li> <li><em>fig4h__simul_efr__hi_slp_thres_all_ohc__no_cs.csv</em>: <br> Simulated EFR magnitude-level function for the HI threshold listeners (average) assuming sloping threshold at EHF and all of OHC loss (Fig. 4e).</li> <li><em>fig4i__simul_efr__hi_slp_thres_all_ihc__no_cs.csv</em>: <br> Simulated EFR magnitude-level function for the HI threshold listeners (average) assuming sloping threshold at EHF and and all of IHC loss (Fig. 4f).</li> </ul> <p> </p> <p><strong>Fig. 5:</strong></p> <ul> <li><em>fig5a__simul_efr__nh__cs_ms_ls_100p.csv</em>: <br> Simulated EFR magnitude-level function for the NH threshold listeners (average) assuming cochlear synaptopathy (CS) of a 100% of loss of only medium- and low-spontaneous rate (SR) AN fibers (Fig. 5a).</li> <li><em>fig5b__simul_efr__nh09_cs__approx</em>.<em>csv</em>: <br> Simulated EFR magnitude-level function to approximate the data for the NH threshold listener NH09 including CS (Fig. 5b).</li> <li><em>fig5c__simul_efr__hi04_cs__approx</em>.<em>csv</em>: <br> Simulated EFR magnitude-level function to approximate the data for the HI listener HI04 including CS (Fig. 5c).</li> </ul> <p> </p> <p><strong>Fig. 6:</strong></p> <p>Files with the simulation results for the NH threshold listener in different characteristic frequency (CF) bands and SR fiber types</p> <ul> <li><em>fig6__simul_efr__nh_cf_band_analys__no_cs__m12.mat</em>: <br> Simulated EFR magnitude-level function for the NH using a SAM tone with m = 12%.</li> <li><em>fig6__simul_efr__nh_cf_band_analys__no_cs__m25.mat</em>: <br> Simulated EFR magnitude-level function for the NH using a SAM tone with m = 25%.</li> <li><em>fig6__simul_efr__nh_cf_band_analys__no_cs__m50.mat</em>: <br> Simulated EFR magnitude-level function for the NH using a SAM tone with m = 50%.</li> <li><em>fig6__simul_efr__nh_cf_band_analys__no_cs__m85.mat</em>: <br> Simulated EFR magnitude-level function for the NH using a SAM tone with m = 85%.</li> <li><em>fig6__simul_efr__nh_cf_band_analys__no_cs__m100.mat</em>: <br> Simulated EFR magnitude-level function for the NH using a SAM tone with m = 100%.</li> </ul> <p> </p> <p><strong>Fig. 7:</strong></p> <ul> <li><em>fig7a__simul_efr__bw_analys__32oct_[20, 40, 60, 80, 100]p.csv</em>: <br> Simulated EFR magnitude-level function for the NH threshold listeners assuming CS of a bandwidth (BW) of 3/2-octave for a loss of AN fibers ranging from 20% to 100% (Fig. 7a).</li> <li><em>fig7b__simul_efr__bw_analys__1oct_[20, 40, 60, 80, 100]p.csv</em>: <br> Simulated EFR magnitude-level function for the NH threshold listeners assuming CS of a bandwidth (BW) of 1-octave for a loss of AN fibers ranging from 20% to 100% (Fig. 7b).</li> <li><em>fig7c__simul_efr__bw_analys__13oct_[20, 40, 60, 80, 100]p.csv</em>:<br> Simulated EFR magnitude-level function for the NH threshold listeners assuming CS of a bandwidth (BW) of 1/3-octave for a loss of AN fibers ranging from 20% to 100% (Fig. 7c).</li> </ul> <p> </p> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p> </p> <p>The structure of the .csv files that contain the EFR simulations is:</p> <ul> <li><em>lvl</em>: Stimulation level</li> <li><em>mgn_mxxx: </em>Simulated EFR magnitude (a.u. in dB) for each modulation depth (100%, 85%, 50%, 25% and 12%)</li> <li><em>bkg_mxxx: </em>Estimate of the background noise floor (a.u. in dB) for each modulation depth. </li> <li><em>ftest_mxxx: </em>Result of the F-test statistical test (0 or 1) for each modulation depth.</li> </ul> <p> </p> <p>The structure of the .mat files that contain the EFR simulations is:</p> <ul> <li><em>exper_type</em>: Name of the simulated experiment</li> <li><em>species</em>: Species used in the AN model (in this study is always 2: human)</li> <li><em>species_age</em>: (Not used in this work). Age of the animal when species is 4: mouse</li> <li>modulation<em>: </em>Modulation depth of the SAM tone used in the simulation <em>(100%, 85%, 50%, 25% or 12%)</em></li> <li><em>f_on: </em>Center frequency of the on-frequency band (<em>2000 Hz</em>)</li> <li><em>f_off_hf: </em>Center frequency of the first off-frequency band (<em>3000 Hz</em>)</li> <li><em>f_off_vhf: </em>Center frequency of the second off-frequency band (<em>7000 Hz</em>) </li> <li><em>f_off_uvhf: </em>Center frequency of the third off-frequency band (<em>12000 Hz</em>)</li> <li><em>lvl_vect: </em>Stimulus level vector <em>(from 5 to 100 dB SPL, in steps of 5 dB)</em></li> <li><em>simul_efr </em> Structure with the simulated data <ul> <li>The data structure contains many fields which are matricies of size 3x20. The columns are the 20 stimulus levels defined in <em>lvl_vect</em>, and the first row is the simulated EFR, the second row is the estimates background noise floor in the simulation, and the third row is the output of the F-test statistics.</li> <li>The structure fields can be divided in 4 groups: <ul> <li><em>ihc_</em> Responses from the IHC (not shown in the paper)</li> <li><em>an_hs_</em> Responses from the High-SR fibers in the AN</li> <li><em>an_ms_</em> Responses from the Medium-SR fibers in the AN</li> <li><em>an_ls_</em> Responses from the Low-SR fibers in the AN</li> </ul> </li> <li>Each group has 5 responses corresponding to the on-frequency band (<em>_on</em>) and the three off-frequency bands (<em>_off_hf</em>, <em>_off_vhf</em>, <em>_off_uvhf</em>); and the sum across frequencies (<em>_across_f</em>)</li> </ul> </li> </ul>
Effects of a Delayed Expansion of Interconnector Capacities in a High RES-E European Electricity System - Dataset
<p>Input and output data of the modelling work for the paper Effects of a Delayed Expansion of Interconnector Capacities in a High RES-E European Electricity System</p> <ul> <li>Considered scenario years: 2030, 2040 and 2050</li> <li>The profiles are based on the historical year 2016.</li> <li>Two scenarios are included: lower connectivity and high connectivity</li> <li>The data cover the ENTSO-E member countries except Iceland and Cyprus and is given in country-specific resolution.</li> </ul> <p><strong>Input:</strong></p> <ul> <li>Demand as hourly profile in MWh</li> <li>Variable RES-E as hourly profile in MWh</li> <li>Power plant fleet as capacities in MW</li> <li>NTCs as capacities in MW</li> </ul> <p><strong>Output:</strong></p> <ul> <li>CO2 emissions as annual data in Mt</li> <li>Variable electricity generation costs as annual data in MEur</li> <li>Variable electricity generation costs per generation as annual data in Euro/MWh</li> <li>Electricity generation as annual data in TWh</li> <li>Electricity export as annual data in TWh</li> <li>Electricity import as annual data in TWh</li> <li>Transit flows as annual data in TWh</li> </ul> <p>The sources are described in the corresponding paper under the following link: <a href="https://www.mdpi.com/1996-1073/12/16/3098">https://www.mdpi.com/1996-1073/12/16/3098</a></p>
Artifact for the ESEC/FSE 2019 Paper: Effects of Explicit Feature Traceability on Program Comprehension
<p>This is the artifact for our ESEC/FSE 2019 paper "Effects of Explicit Feature Traceability on Program Comprehension", providing 1) the experiment as reusable source code and 2) the anonymized results of our experiment. For more details on how to use the source code and interpret the data, please refer to the readme file.</p>
"Effects of forestry on summertime low flows and physical fish habitat in snowmelt-dominant headwater catchments of the Pacific Northwest" -- data sets
<p>These files contain the data used in the analysis and production of graphs reported in a manuscript titled "Effects of forestry on summertime low flows and physical fish habitat in snowmelt-dominant headwater catchments of the Pacific Northwest," by Stefan Gronsdahl, R. Dan Moore, Jordan Rosenfeld, Rich McCleary, Rita Winkler. The paper will be published in the journal Hydrological Processes. The file named "readme.txt" explains the contents of the files.</p>
Datasets for ``Dynamo effect in decaying helical turbulence''
<p>In this supplemental material to the paper ``Dynamo effect in decaying helical turbulence,'' Phys. Rev. Fluids, 4, 024608 (2019), DOI: <a href="https://doi.org/10.1103/PhysRevFluids.4.024608">10.1103/PhysRevFluids.4.024608</a>, <a href="http://arXiv.org/abs/1710.01628">(arXiv:1710.01628</a>) we provide the underlying data to most of the figures. The file decdynamo.tar.gz contains a gzipped tar file to the website</p> <p> https://www.nordita.org/~brandenb/projects/decdynamo/</p> <p>For each of the Runs A-H plus 3 addition ones of Figures 13 and 14, the original run directories are given. They contain secondary data such as time series and spectra. The full snapshots are not stored, but all the relevant files needed for rerunning any particular simulation exist. In each directory, the input files (*.in) exist and data are under the directory data (including time series and spectra). In some cases, data directly relevant for particular plots are available, for example the directory "spectra" contains selected spectra used in some plots.</p>
Research data for: Preventing the coffee-ring effect and aggregate sedimentation by in situ gelation of monodisperse materials
<p>Raw data for the publication: Preventing the coffee-ring effect and aggregate sedimentation by in situ gelation of monodisperse materials</p>
PERCEIVE: WP3: Effectiveness of communication strategies of EU projects
<p>This data set contains all the relevant data referred to PERCEIVE WP3, as tasks within WP3 are logically connected. Data analyzed within WP3, but collected in WP5, are included in the data set “<em>PERCEIVE: WP5: The multiplicity of shared meanings of EU and Cohesion Regional and Urban Policy at different discursive levels</em>” (<a href="http://doi.org/10.5281/zenodo.1038041">http://doi.org/10.5281/zenodo.1038041</a>). Data analyzed in Task3.1 are mostly based on transcripts already included in the data set “<em>PERCEIVE. WP1. Framework for comparative analysis of the perception of Cohesion Policy and identification with the European Union at citizen level in different European countries. Task1.2. Focus group with Cohesion Policy practitioners</em>” (focus groups and other interviews).</p> <p>Task3.2 data are the results of a European wide online survey (at the moment this document is being compiled, the website of the survey is no longer online) targeting policy communicators and focused on three strategic aspects of communicating policy: a) factors of success and barriers, b) support from central institutions and c) communication mix and storytelling.</p> <p>Task3.3 data regard the analysis of social media communication from EU communication offices at both local and European level. Data covers a sentiment analysis performed on the Facebook homepages of Local Management Authorities (LMA) of PERCEIVE case study regions as well as twitter networks and timelines for international accounts and hashtags. </p> <p>Task3.4 data contain elements used in the statistical modeling of communication efforts (see Deliverable 3.4, <a href="http://doi.org/10.6092/unibo/amsacta/6111">http://doi.org/10.6092/unibo/amsacta/6111</a> or <a href="http://doi.org/10.5281/zenodo.1318144">http://doi.org/10.5281/zenodo.1318144</a>). Data cover the algorithmic clustering of topics detected in Task5.3, data derived from the PERCEIVE survey, the code (R programming environment) used to run regression analyses, as well as the results of the analyses themselves.</p> <p>Task3.5 data refer to secondary publicly available data. Namely the collection of the PANORAMA magazine available at INFOREGIO, the Directorate of Regional Policy web portal (<a href="https://ec.europa.eu/regional_policy/en/information/publications/panorama-magazine/">https://ec.europa.eu/regional_policy/en/information/publications/panorama-magazine/</a>), and Eurobarometer data on “awareness” available at the Open Data Portal of the EC (<a href="http://ec.europa.eu/commfrontoffice/publicopinion/index.cfm">http://ec.europa.eu/commfrontoffice/publicopinion/index.cfm</a>). The textual content of PANORAMA magazine has been content analyzed and the results are made available as a .csv table of concepts’ frequencies per magazine issue.</p>
Replication Data for: Determination of Intrinsic Effective Fields and Microwave Polarizations by High-Resolution Spectroscopy of Single NV Center Spins
<p>Data repository for: <strong>Determination of Intrinsic Effective Fields and Microwave Polarizations by High-Resolution Spectroscopy of Single NV Center Spins</strong></p> <p><em>Data description.pdf</em> describes the uploaded data.<br> <em>Data.xlsx</em> is the data represented in the paper.<br> <em>Esrfit_Npeak.m</em>, <em>Esrfit_xN.m</em>, <em>GaussianFunc.m</em>, <em>Gaussian_xN_Func.m</em>, <em>Lorentz_Func.m</em>, <em>Lorentz_xN_Func.m</em>, <em>Rabifit_xN.m</em>, <em>Rabi_xN_Func.m</em>, <em>FourierTransformRabi.m</em> are Matlab code files to transform and fit the data.</p>
Temporally enhanced RSEI and Nighttime Lights Reveal Long-Term Ecological Changes and Effective Protection in China's Inaugural National Parks
<p>China's inaugural national parks play a crucial role in preserving biodiversity and maintaining ecosystem services. These protected areas are characterized by diverse landscapes and sensitive ecological environments. Over recent decades, the interplay between intensified human activities and global climate change has posed significant challenges to the ecological quality of these regions. Accurate and scientific assessment of ecological quality is essential for informed management and policy-making.</p> <p>This dataset is based on multiple MODIS datasets, incorporating NDVI, LST, WET, and NDBSI as indicators. Using principal component analysis (PCA), we produced the Improved Remote Sensing Ecological Index (RSEI) for these parks from 2000 to 2022 at a 500m spatial resolution.</p> <p>The RSEI was calculated using four component indices: greenness, heat, dryness, and wetness. Data for dryness and wetness were derived from the 8-day composite 500m resolution surface reflectance product MOD09A1. Heat was calculated using the 8-day composite 1km resolution land surface temperature product MOD11A2, which was resampled to 500m resolution. Greenness was derived from the 16-day composite 500m resolution vegetation index product MOD13A1.</p> <p>The improved RSEI calculation method enhances the temporal stability and comparability of the data, making it more suitable for long-term ecological monitoring.</p> <p>The improved RSEI effectively integrates dynamic changes of multiple variables and offers better temporal comparability for long-term ecological monitoring. Our results indicate that the ecological environment quality within the inaugural national parks significantly improved over the study period, with more noticeable improvements following the implementation of pilot conservation programs.</p> <p>This dataset provides foundational information for understanding the long-term ecological trends in China's national parks. It serves as a crucial resource for researchers, policymakers, and conservationists dedicated to the sustainable management and development of these vital ecological regions.</p> <p>The dataset contains five RAR compressed files, each corresponding to one of the national parks. These files include the Remote Sensing Ecological Index (RSEI) data from 2000 to 2022 for each respective park:</p> <ul> <li><strong>NTLNP-RSEI.rar</strong>: Contains the RSEI data for the Northeast Tiger and Leopard National Park (NTLNP) from 2000 to 2022.</li> <li><strong>HTRNP-RSEI.rar</strong>: Contains the RSEI data for the Hainan Tropical Rainforest National Park (HTRNP) from 2000 to 2022.</li> <li><strong>WNP-RSEI.rar</strong>: Contains the RSEI data for the Wuyishan National Park (WNP) from 2000 to 2022.</li> <li><strong>SNP-RSEI.rar</strong>: Contains the RSEI data for the Sanjiangyuan National Park (SNP) from 2000 to 2022.</li> <li><strong>GPNP-RSEI.rar</strong>: Contains the RSEI data for the Giant Panda National Park (GPNP) from 2000 to 2022.</li> </ul> <p>Each of these compressed files includes the improved RSEI calculations for the respective national park, providing a comprehensive view of the ecological quality changes over the 22-year period.</p> <p>The details of the data are as follows:</p> <ul> <li><strong>Data Format</strong>: GeoTiff</li> <li><strong>Pixel Values</strong>: Represent RSEI, ranging from 0 to 1, with no units.</li> <li><strong>Compatibility</strong>: The data can be directly opened and processed using remote sensing and GIS software such as ENVI and ArcGIS.</li> <li><strong>Data Quality</strong>: Due to the application of water and snow masks to remove the influence of water bodies and snow/ice on the WET component, there are some missing data areas.</li> </ul> <p>These datasets offer valuable insights into the ecological quality changes within each national park over the specified period, making them essential for researchers, policymakers, and conservationists involved in the sustainable management and development of these protected areas.</p> <p>For using the data and code provided in this dataset, please cite the following paper:</p> <p>Wen, C., Long, T., He, G., Jiao, W., & Jiang, W. (2025). Temporally enhanced RSEI and nighttime lights reveal long-term ecological changes and effective protection in China’s inaugural national parks. <em>Ecological Indicators, 170</em>, 112981. <a href="https://doi.org/10.1016/j.ecolind.2024.112981" target="_new" rel="noopener">https://doi.org/10.1016/j.ecolind.2024.112981</a></p> <p>The calculation of the RSEI is completed using Google Earth Engine. The link to the calculation code is:</p> <p><a href="https://code.earthengine.google.com/fab5452cd224d1f06226aece4c1a1016">https://code.earthengine.google.com/089d74f423e91a0da9490f5098c55021</a></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.