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RAPID input and output files corresponding to "Numerical Modeling as a Service on the Cloud: A Case Study of River Modeling"
<p><strong>Corresponding peer-reviewed publication</strong></p> <p>This dataset consists of output files of the study reported in:</p> <ul> <li>Tom, M., David, C.H., Marlis, K.M., Zimdars, P.A., Bonassies, Q., Wade, J., Cerbelaud, A., Pavelsky T., Huang, T. (In Review), Numerical Modeling as a Service on the Cloud: A Case Study of River Modeling.</li> </ul> <p>When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein.</p> <p><strong>Summary<br></strong><br>This dataset contains the results of RAPID river discharge simulations (January 1980, February 1980) for the Mississippi river basin using the CURRNT framework. The surface and subsurface runoff data were retrieved from NASA GLDAS Phase 2 dataset (Rodell et al., 2004) at a 3-hourly temporal resolution.<strong><br></strong></p> <p><strong>Software</strong></p> <p>The software used to produce the files in this dataset is available at <a href="https://github.com/czarmanu/currnt" target="_blank" rel="noopener">https://github.com/czarmanu/currnt</a>.</p> <p><strong>Study domain</strong></p> <p>The files in this dataset correspond to Mississippi River Basin.</p> <p><strong>Description of files</strong></p> <p>All files below were prepared by Manu Tom, using the software mentioned above.<br><br>1980-01</p> <ul> <li><em>GLDAS_VIC_3H_1980-01_utc.nc4.</em> This <em>netCDF</em> file contains averaged and concatenated GLDAS 3-hourly products (1.0 degree, version 2.0, downloaded from NASA Earthdata using NSIDC earthaccess library) for January 1980.</li> <li><em>Qinit_pfaf_74_GLDAS_VIC_3H_1980-01.nc. This netCDF file contains the initial state of RAPID (zeros populated for Qout).</em></li> <li><em>Qout_pfaf_74_GLDAS_VIC_3H_1980-01.nc. </em>This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) for January 1980 from RAPID corresponding to the downstream point of each reach.</li> <li><em>m3_riv_pfaf_74_GLDAS_VIC_3H_1980-01_utc.nc4</em>. This netCDF file contains the 3-hourly accumulated inflows of water (in cubic meters) for January 1980 from surface and subsurface runoff into the upstream point of each river reach. </li> </ul> <p>1980-02</p> <ul> <li><em>GLDAS_VIC_3H_1980-01_utc.nc4.</em> This <em>netCDF</em> file contains averaged and concatenated GLDAS 3-hourly products (1.0 degree, version 2.0, downloaded from NASA Earthdata using NSIDC earthaccess library) for February 1980.</li> <li><em>Qinit_pfaf_74_GLDAS_VIC_3H_1980-01.nc. This netCDF file contains the final state of RAPID after a simulation ending on 1980-01-31.</em></li> <li><em>Qout_pfaf_74_GLDAS_VIC_3H_1980-01.nc. This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) for February 1980 from RAPID corresponding to the downstream point of each reach. </em></li> <li><em>m3_riv_pfaf_74_GLDAS_VIC_3H_1980-01_utc.nc4</em>. This netCDF file contains the 3-hourly accumulated inflows of water (in cubic meters) for February 1980 from surface and subsurface runoff into the upstream point of each river reach. </li> </ul> <p>1980-03</p> <ul> <li><em>Qinit_pfaf_74_GLDAS_VIC_3H_1980-03.nc. This netCDF file contains the final state of RAPID after a simulation ending on 1980-02-29.<br><br></em></li> </ul> <p><strong>Other necessary links associated with this dataset:</strong></p> <p>RAPID model (David et al., 2011): <a href="https://github.com/c-h-david/rapid">https://github.com/c-h-david/rapid</a></p> <p>RRR: RAPID model pre-processor (David et al., 2019): <a href="https://github.com/c-h-david/rrr">https://github.com/c-h-david/rrr</a></p> <p>GLDAS VIC 3H v2.0 outputs: <a href="https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS%20VIC%203H%20v2.0">https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS%20VIC%203H%20v2.0</a></p> <p>NSIDC Earthaccess library: <a href="https://github.com/nsidc/earthaccess">https://github.com/nsidc/earthaccess</a><br><br><strong>References</strong></p> <p>David, C. H., Maidment, D. R., Niu, G. Y., Yang, Z. L., Habets, F., and Eijkhout, V. (2011), River network routing on the NHDPlus dataset, J. Hydrometeorol., 12, 913–934, <a href="https://doi.org/10.1175/2011JHM1345.1">https://doi.org/10.1175/2011JHM1345.1</a></p> <p>David, C. H., Hobbs, J., Turmon, M., Emery, C., Reager, J. T., Famiglietti, J. (2019). Analytical propagation of runoff uncertainty into discharge uncertainty through a large river network. Geophys. Res. Lett. 46, 8102–8113, <a href="https://doi.org/10.1029/2019GL083342.492">https://doi.org/10.1029/2019GL083342.492</a></p> <p>Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng, C.-J., et al. (2004). The global land data assimilation system, Bull. Am. Meteorol. Soc. 85, 381–394, <a href="https://doi.org/10.1175/BAMS-85-3-381">https://doi.org/10.1175/BAMS-85-3-381</a></p>
The Correspondence of Otto Nicolai
<p>Metadata set of Otto Nicolai's complete known correspondence.</p>
Datasets corresponding to publication: Machine learning assisted Real-time deformability cytometry of CD34+ cells allows to identify patients with Myelodysplastic Syndromes
<p>This repository contains all dataset that correspond to the publication "Machine learning assisted Real-time deformability cytometry of CD34+ cells allows to identify patients with Myelodysplastic Syndromes". Furthermore, Python scripts are provided which allow to reproduce all analyses shown in the manuscript. Execution of the scripts requires a Python environment with packages as stated in the Methods section of the manuscript, or by using PyBox 0.1.0. PyBox is a readily installed Python environment containing all packages at the required version. PyBox is publicly available on GitHub: <a href="https://github.com/maikherbig/PyBox">https://github.com/maikherbig/PyBox</a>.</p>
Tagged Corpus of Early English Correspondence Extension Sampler (TCEECES)
<p>The <em>Tagged Corpus of Early English Correspondence Extension Sampler</em> (TCEECES) is the third public release from the 18th-century part of the <em>Corpora of Early English Correspondence</em> (CEEC-400).</p> <p>The TCEECES forms one part of the full CEECES. The other parts are the <a href="https://www.doi.org/10.5281/zenodo.4644243">CEECES part 1</a> (released 1 April 2021), and the <a href="https://www.doi.org/10.5281/zenodo.5887101">CEECES part 2</a> (released 11 April 2022).</p> <p>The TCEECES is an extract from the full <em>Tagged Corpus of Early English Correspondence Extension</em> (TCEECE), which remains unpublished.</p> <p>See the accompanying manual for more information on the TCEECES; see the manuals for the CEECES 1 and the CEECES 2 for more information on the CEECES. See <a href="https://varieng.helsinki.fi/CoRD/corpora/CEEC/">https://varieng.helsinki.fi/CoRD/corpora/CEEC/</a> for more on the CEEC-400.</p> <p>Citation:</p> <p>TCEECES = <em>Tagged Corpus of Early English Correspondence Extension Sampler</em>. Compiled by Terttu Nevalainen, Helena Raumolin-Brunberg, Samuli Kaislaniemi, Mikko Laitinen, Minna Nevala, Arja Nurmi, Minna Palander-Collin, Tanja Säily and Anni Sairio at the Department of Languages, University of Helsinki. Spelling standardised by Mikko Hakala, Minna Palander-Collin, Minna Nevala, Emanuela Costea, Anne Kingma and Anna-Lina Wallraff. Annotated by Lassi Saario and Tanja Säily. XML conversion and encoding by Lassi Saario. Helsinki: VARIENG, 2022.</p>
Corpus of Early English Correspondence Extension Sampler part 2 (CEECES 2)
<p>The <em>Corpus of Early English Correspondence Extension Sampler</em> part 2 (CEECES 2) is the second public release from the 18th-century part of the <em>Corpora of Early English Correspondence</em> (CEEC-400).</p> <p>The CEECES 2 forms one part of the full CEECES. The other parts are the <a href="https://www.doi.org/10.5281/zenodo.4644243">CEECES part 1</a> (released 1 April 2021), and the <a href="https://www.doi.org/10.5281/zenodo.5887231">TCEECES</a> (released 11 April 2022).</p> <p>See the accompanying manual for more information on the CEECES 2; see the manuals for the CEECES 1 and the TCEECES for more information on the CEECES. See <a href="https://varieng.helsinki.fi/CoRD/corpora/CEEC/">https://varieng.helsinki.fi/CoRD/corpora/CEEC/</a> for more on the CEEC-400.</p> <p>Citation:</p> <p>CEECES 2 = <em>Corpus of Early English Correspondence Extension Sampler</em> part 2. Compiled by Terttu Nevalainen, Helena Raumolin-Brunberg, Samuli Kaislaniemi, Mikko Laitinen, Minna Nevala, Arja Nurmi, Minna Palander-Collin, Tanja Säily and Anni Sairio at the Department of Languages, University of Helsinki. XML conversion and encoding by Lassi Saario. Helsinki: VARIENG, 2022.</p>
Database of non-target invertebrates recorded in field experiments of genetically engineered Bt maize and corresponding non-Bt maize: data files
<p><span>This database represents a comprehensive collection of experimental field data from all over the world on non-target invertebrates recorded in genetically engineered/modified Bt and non-Bt maize. The three data files deposited here are described by Meissle et al. (2022), BMC Research Notes, <span><a href="https://doi.org/10.1186/s13104-022-06021-3"><span>https://doi.org/10.1186/s13104-022-06021-3</span></a></span><span> </span>.</span></p> <p><span>The database was created for a systematic review with the question if growing Bt maize changes abundance or ecological function of non-target animals compared to growing of non-GM maize, published by Meissle et al. (2022), Environmental Evidence, <a href="https://doi.org/10.1186/s13750-022-00272-0"><span>https://doi.org/10.1186/s13750-022-00272-0</span></a></span>.</p> <p>Data file 1 contains the database with 7279 records of non-target invertebrate abundance, activity density, or predation or parasitism in Bt and non-Bt maize, extracted from 120 publications. Data file 2 includes the list and definitions of variables in the database, and data file 3 represents the critical appraisal questions and answer options.</p>
Data corresponding to "The Impact of Multi-sensor Land Data Assimilation on River Discharge Estimation"
<p>This dataset is corresponding to the input and output files that were used in this study:</p> <p>Wu, W.-Y., Z.-L. Yang, L. Zhao, P. Lin (2022), Joint Multi-sensor Data Assimilation for Constraining Water Storages and its Impact on Global Discharge Estimation (<em>in revision, RSE</em>)</p>
Corresponding spreadsheet to the Paper 'Comparative analysis of pre-Covid19 child immunization rates across 30 European countries and identification of underlying positive societal and system influences'
<p>This study provides a macro-level societal and health system focused analysis of child vaccination rates in 30 European countries, exploring the effect of context on coverage. The importance of demography and health system attributes on health care delivery are recognized in other fields, but generally overlooked in vaccination. The analysis is based on correlating systematic data built up by the Models of Child Health Appraised (MOCHA) Project with data from international sources, so as to exploit a one-off opportunity to set the analysis within an overall integrated study of primary care services for children, and the learning opportunities of the ‘natural European laboratory’. The descriptive analysis shows an overall persistent variation of coverage across vaccines with no specific vaccination having a low rate in all the EU and EEA countries. However, contrasting with this, variation between total uptake per vaccine across Europe suggests that the challenge of low rates is related to country contexts of either policy, delivery, or public perceptions. Econometric analysis aiming to explore whether some population, policy and/or health system characteristics may influence vaccination uptake provides important results - GDP per capita and the level of the population’s higher education engagement are positively linked with higher vaccination coverage, whereas mandatory vaccination policy is related to lower uptake rates. The health system characteristics that have a significant positive effect are a cohesive management structure; a high nurse/doctor ratio; and use of practical care delivery reinforcements such as the home-based record and the presence of child components of e‑health strategies.</p>
Spatz neutron reflectometer commissioning data and corresponding models to fits
<p>This is the data to the hot commissioning of the Spatz neutron reflectometer at the OPAL Research Reactor, ANSTO. The data also contains the Jupyter notebook to reduce the data to give complete reflectivity profile and contains the models used to fit the data within refnx using the GUI option. </p>
The visualization of the collected data corresponding to the following paper: "The development of a Self-Rated ICF-based questionnaire (HEAR-COMMAND Tool) to evaluate Hearing, Communication, and Conversation disability: multinational experts' and patients' perspectives"
<p>These two PDF files include the data collected for a study conducted by Afghah et.al, 2022. They include the responses of the participant in this study to a newly developed self-rated ICF-based questionnaire. One file includes the responses to 30 demographic questions and the other one 88 ICF-based questions. The presented data were collected in Germany, the USA, and Egypt as well as overall data.</p> <p>The design of the questionnaire is described here:<br> Afghah, T., Alfakir, R., Meis, M., van Leeuwen, L. M., Kramer, S. E., Hammady, M., Youssif, M., & Wagener, K. C. (2021). The development of a Self-Rated ICF-based questionnaire (HEAR-COMMAND Tool) to evaluate hearing, communication, and conversation disability: multinational experts' and patients' perspectives. Zenodo. https://doi.org/10.5281/zenodo.5534360.</p> <p>This study was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Project ID: 352015383 – SFB 1330, C4.</p>
Datasets corresponding to "Real-time intelligent classification of COVID-19 and thrombosis via massive image-based analysis of platelet aggregates"
<p>Datasets corresponding to "Real-time intelligent classification of COVID-19 and thrombosis via massive image-based analysis of platelet aggregates"</p> <p> </p> <p>Please find below an explanation for the <strong>files </strong>in this repository:</p> <p><br> <br> <strong>DiseaseClassifPaper_Dataset_01.7z, DiseaseClassifPaper_Dataset_02.7z</strong></p> <p>Experimental data. To reproduce the analyses, unzip both files and put the content into a folder called "Dataset"</p> <p><strong>02_CNN_PhenotypeClassif.7z</strong></p> <p>CNN Phenotype classification. Model was trained using AIDeveloper. using manually labelled data. Labelled Data is contained in folder "03_GatedData". The AIDeveloper session file in "02_Model\M10_Nitta6l_32pix_8class_meta.xlsx" shows, which files correspond to which subpopulation. The final model "M10_Nitta6l_32pix_8class_448.model" and corresponding .pb files are also located in that folder.</p> <p><strong>03_ExampleMeasurement.zip</strong></p> <p>One measurement file and a corresponding scatterplot</p> <p><strong>04_Dataset_load.zip</strong></p> <p>The python script "03_ExtractFeatures.py" loads the list of available experiment files (01_Dataset_Table_v02.csv). The experiment files are contained in DiseaseClassifPaper_Dataset_01.7z, DiseaseClassifPaper_Dataset_02.7z. The scrip then evaluates each experiment file to obtain distribution parameters for Area and Solidity. These values are written to new "01_Dataset_Table_v03.csv".</p> <p><strong>05_RF_training</strong></p> <p>Scripts to train and evaluate the Random Forest model (using features contained in "01_Dataset_Table_v03.csv").</p> <p><strong>07_pytranskit</strong></p> <p>Scripts for training and evaluating CDT-PLDA classifier</p> <p> </p> <p> </p>
Raw images corresponding to article "Relative assessment of cloth mask protection against ballistic droplets: a frugal approach"
<p>The images correspond to scans used to obtain the data reported on V. Márquez-Alvarez, J. Amigó-Vega, A. Rivera, A. J. Batista-Leyva, and E. Altshuler, <em>Relative assessment of cloth mask protection against ballistic droplets: A frugal approach</em>. PLOS ONE 17, e0275376 (2022). doi: <a href="https://doi.org/10.1371/journal.pone.0275376">10.1371/journal.pone.0275376</a>.</p>
LaMEM source code and input files corresponding to Present‐day upper‐mantle architecture of the Alps: Insights from data‐driven dynamic modelling
<p>This repository contains LaMEM source code and input files for the models presented in Kumar, A., Cacace, M., Scheck-Wenderoth, M., Götze, H.-J., & Kaus, B. J. P. (2022). Present-day upper-mantle architecture of the Alps: Insights from data-driven dynamic modeling. Geophysical Research Letters, 49, e2022GL099476. https://doi. org/10.1029/2022GL099476</p>
Variations of the urban PM2.5 chemical components and corresponding light extinction for three heating seasons in the Guanzhong Plain, China
<p>The basic data of the thesis “Variations of the urban PM2.5 chemical components and corresponding light extinction for three heating seasons in the Guanzhong Plain, China”.</p>
Intraspecific trait variation in a dryland tree species corresponds to regional climate gradients
<p><strong>Aim:</strong> Intraspecific trait variation is fundamental to understanding a species' adaptive capacity. Assessing phenotypic variation at different ecological scales is useful for evaluating species' vulnerability to changing environmental conditions. Here we quantify the ecological scale of variation of phenotypic functional traits for a widespread dryland tree species in the western United States, <em>Pinus monophylla</em>, and evaluate the strength of trait-environment relationships across spatial gradients and in response to interannual variability in weather conditions.</p> <p><strong>Location:</strong> Western United States</p> <p><strong>Taxon:</strong> Conifer tree (Pinaceae): <em>Pinus monophylla</em></p> <p><strong>Methods:</strong> Nine reproductive and foliar morphological traits were measured from 23 sampling locations in nine mountain ranges, stratified across broad-scale gradients of precipitation timing and quantity and local gradients of elevation. We partitioned trait variation within and among nested ecological scales (mountain range, site, tree, and tree growth year). We then used multivariate methods to quantify the association between environmental variables and ecological trade-offs associated with both reproductive and foliar traits.</p> <p><strong>Results:</strong> Most variation was explained at the scale of individual trees (18–46%) and between growth years (20–30%), with smaller but variable contributions at the scale of sites (8–25%) and mountain ranges (0–15%). Trait values had low correlation with environmental variables (13%); however, 94% of the trait-environment covariance was explained by two major axes of variation: (1) the drought-stress gradient covarying with reproductive traits, and (2) precipitation patterns covarying with foliar morphology.</p> <p><strong>Main conclusions:</strong> High levels of individual trait variation indicate within-population adaptive capacity. Consistent relationships among range-wide patterns of foliar and reproductive traits and broad-scale climate gradients provide indirect evidence of local adaptation and may inform seed sourcing for restoration or the potential for assisted migration efforts. Quantification of intraspecific trait variation across multiple ecological scales is important for understanding the potential climate change response of dryland tree species.</p>
Compounds with multi-target activity from X-ray structures, corresponding analog series, and associated scaffolds
<p>A total of 702 crystallographic ligands with activity against multiple targets from different protein families (multi-family ligands) were extracted from the Protein Data Bank (PDB). These ligands are made available as aromatic non-stereo SMILES strings together with their target information. A subset of these ligands were also found in the ChEMBL database yielding additional target annotations. Target and family assignments were made following the UniProt classification scheme. In addition, 133 analog-series-based (ASB) scaffolds were derived from series of PDB ligands and structural analogs identified in ChEMBL, which are also made available. For each ASB scaffold, the number of analogs, targets, and target families is provided (union of PDB and ChEMBL annotations).</p>
Lower face images labeled with corresponding motion sickness scores
<p>This is the dataset associated with the manuscript "Detecting in-car VR Motion Sickness from Lower Face Action Units". This dataset includes 1) all lower face images captured by the HP Omnicept VR headset during a motion sickness induction study; 2) Each image is labeled with a corresponding motion sickness score reported by a participant immediately after each motion sickness induction block finished</p> <p>For example,</p> <p>if the name of an image is 1-0_2, then it means that the participant whose ID=1 reported a motion sickness score=2 after the 1st motion sickness induction block finished;</p> <p>if the name of an image is 1-1_4, then it means that the participant whose ID=1 reported a motion sickness score=4 after the 2nd motion sickness induction block finished.</p>
Raw data corresponding to Huestegge, S. M., Raettig, T., & Huestegge, L. (2019). "Are face-incongruent voices harder to process? Effects of face-voice gender incongruency on basic cognitive information processing." Journal: Experimental Psychology.
<p>Raw data file prior to subject-based aggregation. Variables and values are decribed within the file. For further reference and specifications please also refer to the original publication in the journal Experimental Psychology.</p>
Corresponding-Colour Datasets - Luo and Rhodes (1999)
<p><strong>Source URL</strong>: <a href="https://web.archive.org/web/20031123133629/http://colour.derby.ac.uk:80/colour/info/catweb/">https://web.archive.org/web/20031123133629/http://colour.derby.ac.uk:80/colour/info/catweb/</a><br> <strong>Source DOI</strong>: <a href="https://doi.org/10.1002/(SICI)1520-6378(199908)24:4%3C295::AID-COL10%3E3.0.CO;2-K">https://doi.org/10.1002/(SICI)1520-6378(199908)24:4%3C295::AID-COL10%3E3.0.CO;2-K</a></p> <p><strong>M. Ronnier Luo and Peter A. Rhodes</strong></p> <p><em>Colour & Imaging Institute<br> University of Derby<br> Derby<br> England</em></p> <p><strong>INTRODUCTION</strong></p> <p>A <em>chromatic adaptation transform</em> is capable of predicting corresponding colours. <em>Corresponding colours</em> are described by two sets of tristimulus values that give rise to the same perceived colour when the two samples are viewed under test and reference light sources or illuminants. The two light sources or illuminants differ in terms of their colour temperatures (or chromaticity coodinates). A chromatic adaptation transform can be effectively used for numerous industrial applications such as the evaluation of colour inconstancy for surface samples, the calculation of colour difference between pairs of samples assessed under non-daylight sources or illuminants, the provision of a colour rendering index for assessing the quality of light sources, or the prediction of coloured images across different sources or illuminants.</p> <p>In October 1998, the <a href="https://web.archive.org/web/20031123133629/http://www.cie.co.at/cie/">CIE</a> formed a new technical committee, TC 1-52, on Chromatic Adaptation Transforms during its interim meeting in Baltimore, USA with <a href="https://web.archive.org/web/20031123133629/http://colour.derby.ac.uk/colour/people/luo/">Professor M. R. Luo</a> as its chairman. The objective of this committee is to review certain chromatic adaptation transforms with a view to making a CIE recommendation. The performance of chromatic adaptation transforms is normally evaluated using corresponding-colour experimental data sets in which each colour is defined by two sets of tristimulus values under two illuminants. Many experiments were carried out using a variety of psychophysical methods under different viewing conditions. A comprehensive collection of these data sets has been accumulated by Luo and Hunt <a href="https://web.archive.org/web/20031123133629/http://colour.derby.ac.uk:80/colour/info/catweb/#R1">[1]</a> for the purposes of deriving and evaluating the CIE colour appearance model, CIECAM97s <a href="https://web.archive.org/web/20031123133629/http://colour.derby.ac.uk:80/colour/info/catweb/#R2">[2]</a>, and the CMC chromatic adaptation transform, CMCCAT97 <a href="https://web.archive.org/web/20031123133629/http://colour.derby.ac.uk:80/colour/info/catweb/#R3">[3]</a>. The Committee has decided to make these data sets available via the Internet for public assessment. This task has been completed and the resulting database is now available via the world wide web at <a href="https://web.archive.org/web/20031123133629/http://colour.derby.ac.uk/">http://colour.derby.ac.uk</a>. Researchers or industrialists are welcome to acquire this database for further study. This paper gives a brief description of each data set and describes the format of the data.</p> <p><strong>EXPERIMENTAL DATA SETS</strong></p> <p>Fourteen data sets have been accumulated from nine sources <a href="https://web.archive.org/web/20031123133629/http://colour.derby.ac.uk:80/colour/info/catweb/#R4">[4-11]</a>: the Color Science Association of Japan (CSAJ), Helson, Lam and Rigg, LUTCHI, Kuo and Luo, Breneman, Braun and Fairchild, and McCann. Each data set includes a number of corresponding-colour pairs in which both colours in a pair appear the same when each is viewed under different viewing conditions. <a href="https://web.archive.org/web/20031123133629/http://colour.derby.ac.uk:80/colour/info/catweb/table1.html">Table I</a> summarises the experimental conditions in each data set including the number of phases (as defined by a set of viewing conditions), the number of corresponding-colour pairs and the viewing parameters used. The parameters considered are the light sources used for the test and reference conditions, illuminance (lux), the luminance factor of the neutral background (Y%), sample size, media and psychophysical method.</p> <p>The CSAJ <a href="https://web.archive.org/web/20031123133629/http://colour.derby.ac.uk:80/colour/info/catweb/#R4">[4]</a> data was divided into three sets: -C, -Hunt and -Stevens according to studies on chromatic adaptation, Hunt and Stevens effects respectively. The Helson <a href="https://web.archive.org/web/20031123133629/http://colour.derby.ac.uk:80/colour/info/catweb/#R5">[5]</a>, Lam and Rigg <a href="https://web.archive.org/web/20031123133629/http://colour.derby.ac.uk:80/colour/info/catweb/#R6">[6]</a> data sets include corresponding colours between the test source (A) and reference source (D65). The LUTCHI <a href="https://web.archive.org/web/20031123133629/http://colour.derby.ac.uk:80/colour/info/catweb/#R7">[7]</a> data includes three sets - A, D50 and WF - which are the test illuminants against a reference D65 simulator. Similarly, there are two sets for Kuo and Luo <a href="https://web.archive.org/web/20031123133629/http://colour.derby.ac.uk:80/colour/info/catweb/#R8">[8]</a> data: A and TL84, which are the test light sources against a reference D65 simulator. The only data set based upon transparent media in this category is the Breneman <a href="https://web.archive.org/web/20031123133629/http://colour.derby.ac.uk:80/colour/info/catweb/#R9">[9]</a> data which was divided into two sets: -C and -L according to investigations on chromatic adaptation and illuminance effects respectively. The Braun and Fairchild <a href="https://web.archive.org/web/20031123133629/http://colour.derby.ac.uk:80/colour/info/catweb/#R10">[10]</a> data was accumulated by asking observers to adjust monitor colours to match those presented on reflection prints. The McCann <a href="https://web.archive.org/web/20031123133629/http://colour.derby.ac.uk:80/colour/info/catweb/#R11">[11]</a> data were obtained by investigating the chromatic adaptation effect using a Mondrain figure viewed under highly chromatic test illuminants with low illuminances. Its original data was further analysed to obtain corresponding tristimulus values by Nayatani <em>et al</em> <a href="https://web.archive.org/web/20031123133629/http://colour.derby.ac.uk:80/colour/info/catweb/#R12">[12]</a>.</p> <p>In total, 746 corresponding-colour pairs were gathered from experiments involving 38 phases of viewing conditions. The psychophysical methods used are haploscopic matching, memory matching and magnitude estimation.<br> <br> <strong>Table I: Summary of the corresponding-colour data sets</strong></p> <pre><code class="language-markdown">| Data Set | No. of Phases | No. of Samples | Illuminant | | Illuminance(lux) | Background(Y%) | Sample Size | Medium | Experimental Method | |-------------------|---------------|----------------|------------|---------------|------------------|----------------|-------------|-------------|---------------------| | | | | Test | Ref. | | | | | | | CSAJ-C | 1 | 87 | D65 | A | 1000 | 20 | S | Refl. | Haploscopic | | CSAJ-Hunt | 4 | 20 | D65 | D65 | 10-3000 | 20 | S | Refl. | Haploscopic | | CSAJ-Stevens | 4 | 19 | D65 | D65 | 10-3000 | 20 | S | Refl. | Haploscopic | | Helson | 1 | 59 | D65 | A | 1000 | 20 | S | Refl. | Memory | | Lam & Rigg | 1 | 58 | D65 | A | 1000 | 20 | L | Refl. | Memory | | Lutchi (A) | 1 | 43 | D65 | A | 1000 | 20 | S | Refl. | Magnitude | | Lutchi (D50) | 1 | 44 | D65 | D50 | 1000 | 20 | S | Refl. | Magnitude | | Lutchi (WF) | 1 | 41 | D65 | WF | 1000 | 20 | S | Refl. | Magnitude | | Kuo & Luo (A) | 1 | 40 | D65 | A | 1000 | 20 | L | Refl. | Magnitude | | Kuo & Luo (TL84) | 1 | 41 | D65 | TL84 | 1000 | 20 | S | Refl. | Magnitude | | Breneman-C | 9 | 107 | D65, D55 | A, P, G | 50-3870 | 30 | S | Trans. | Magnitude | | Breneman-L | 3 | 36 | D55 | D55 | 50-3870 | 30 | S | Trans. | Haploscopic | | Braun & Fairchild | 4 | 66 | D65 | D30, D65, D95 | 129 | 20 | S | Mon., Refl. | Matching | | McCann | 5 | 85 | D65 | R, Y, G, B | 14-40 | 30 | S | Refl. | Haploscopic |</code></pre> <p><strong>DATA FILE DESCRIPTION</strong></p> <p><a href="https://web.archive.org/web/20031123133629/http://colour.derby.ac.uk:80/colour/info/catweb/table2.html">Table II</a> summarises the data file names and number of samples in each experimental data set. For each file, the data were arranged in a fixed format. In the top row, there are six figures corresponding to the tristimulus values of the reference and test illuminants, i.e. X<sub>r</sub>, Y<sub>r</sub>, Z<sub>r</sub> and X<sub>t</sub>, Y<sub>t</sub>, Z<sub>t</sub>. There is only one figure in the second row denoting the number of samples in the file. The other rows are also arranged in the same manner as the first row. These are the corresponding tristimulus values under the reference and test illuminants for each sample.<br> <br> <strong>Table II: The data file names and number of samples in each experimental data set</strong></p> <pre><code class="language-markdown">| Data Set | No. of Specimen | File Names | |-------------------|-----------------|----------------| | CSAJ-C | 87 | CSAJ.da.dat | | CSAJ-Stevens | 5 | Steve.10.dat | | | 5 | Steve.50.dat | | | 5 | Steve.1000.dat | | | 4 | Steve.3000.dat | | CSAJ-Hunt | 5 | CSAJ.10.dat | | | 5 | CSAJ.50.dat | | | 5 | CSAJ.1000.dat | | | 5 | CSAJ.3000.dat | | Helson | 59 | helson.ca.dat | | Lam & Rigg | 58 | lam.da.dat | | Lutchi (A) | 43 | lutchi.da.dat | | Lutchi (D50) | 44 | lutchi.dd.dat | | Lutchi (WF) | 41 | lutchi.dw.dat | | Kuo & Luo (A) | 40 | Kuo.da.dat | | Kuo & Luo (TL84) | 41 | Kuo.dt.dat | | Breneman-C | 12 | Brene.p1.dat | | | 12 | Brene.p2.dat | | | 12 | Brene.p3.dat | | | 12 | Brene.p4.dat | | | 11 | Brene.p6.dat | | | 12 | Brene.p8.dat | | | 12 | Brene.p9.dat | | | 12 | Brene.p11.dat | | | 12 | Brene.p12.dat | | Breneman-L | 12 | Brene.p5.dat | | | 12 | Brene.p7.dat | | | 12 | Brene.p10.dat | | Braun & Fairchild | 17 | RIT.1.dat | | | 16 | RIT.2.dat | | | 17 | RIT.3.dat | | | 16 | RIT.4.dat | | McCann | 17 | mcan.b.dat | | | 17 | mcan.g.dat | | | 17 | mcan.grey.dat | | | 17 | mcan.r.dat | | | 17 | mcan.y.dat |</code></pre> <p><strong>SUMMARY</strong></p> <p>A data library has been established to include fourteen corresponding-colour data sets and is available at from <a href="https://web.archive.org/web/20031123133629/http://colour.derby.ac.uk/">http://colour.derby.ac.uk</a>. It is publicly available for all interested parties who are doing research in the areas of colour appearance and chromatic adaptation. Users are encouraged to report new findings to the Chairman of CIE TC 1-52.</p> <p><strong>REFERENCES</strong></p> <ol> <li>LUO M.R. and HUNT R.W.G, <a href="https://web.archive.org/web/20031123133629/http://colour.derby.ac.uk/colour/abstracts/98/luo98b.html"><em>Testing colour appearance models using corresponding-colour and magnitude-estimation data sets</em></a>, Color Res. Appl. <strong>23</strong> 147-153 (1998).</li> <li>LUO M.R. and HUNT R.W.G, <a href="https://web.archive.org/web/20031123133629/http://colour.derby.ac.uk/colour/abstracts/98/luo98a.html"><em>The structure of the CIE 1997 colour appearance model (CIECAM97)</em></a>, Color Res. Appl. <strong>23</strong> 138-146 (1998).</li> <li>LUO M.R. and HUNT R.W.G, <a href="https://web.archive.org/web/20031123133629/http://colour.derby.ac.uk/colour/abstracts/98/luo98c.html"><em>A chromatic adaptation transform and a colour inconstancy index</em></a>, Color Res. Appl. <strong>23</strong> 154-158 (1998).</li> <li>MORI L., SOBAGAKI H., KOMATSUBARA H. and IKEDA K., <em>Field trials on the CIE chromatic adaptation formula</em>, Proceedings of the CIE 22nd Session, 55-58 (1991).</li> <li>HELSON H., JUDD D. B., and WARREN M. H., <em>Object-color changes from daylight to incandescent filament illumination</em>, Illum. Eng. <strong>47</strong>, 221-233 (1952).</li> <li>LAM K. M., <em>Metamerism and colour constancy</em>, Ph.D. Thesis, <a href="https://web.archive.org/web/20031123133629/http://www.brad.ac.uk/">University of Bradford</a>, 1985.</li> <li>LUO M. R., CLARKE A. A., RHODES P. A., SCRIVENER, S. A. R., SCHAPPO A. and TAIT C.J., <a href="https://web.archive.org/web/20031123133629/http://colour.derby.ac.uk/colour/abstracts/91/luo91a.html"><em>Quantifying Colour Appearance. Part I. LUTCHI Colour Appearance Data</em></a>, Color Res. Appl., <strong>16</strong> 166-180 (1991).</li> <li>KUO W.G., LUO M.R., BEZ H. E., <a href="https://web.archive.org/web/20031123133629/http://colour.derby.ac.uk/colour/abstracts/96/luo96c.html"><em>Various chromatic-adaptation transforms tested using new colour appearance data in textiles</em></a>, Color Res. Appl., <strong>21</strong> 313-327 (1995).</li> <li>BRENEMAN E. J., <em>Corresponding chromaticities for different states of adaptation to complex visual fields</em>, J. Opt. Soc. Am. <strong>4:6</strong> 1115-1129 (1987).</li> <li>MCCANN J.J., MCKEE S. P. and TAYLOR T. H., <em>Quantitative studies in retinex theory</em>, Vision Res. <strong>16</strong> 445-458 (1976).</li> <li>BRAUN K.M. and FAIRCHILD M.D., <em>Psychophysical generation of matching images for cross-media color reproduction</em>, in IS&T and SID's 4th Color Imaging Conference: Color Science, Systems and Applications, 214-220, IS&T, Springfield, Va., (1996).</li> <li>NAYATANI Y., TAKAHAMA K. and SOBAGAKI H., <em>Prediction of color appearance of object colors in a complex visual field</em>, J. Light & Vis. Env. <strong>19</strong> 5-14 (1995).</li> </ol>
Instances and corresponding solutions for the ergonomic tow train loading problem (ETTLP)
<p>The set of instances and corresponding solutions, which were used in the computational study of the paper “Loading tow trains ergonomically for just-in-time part supply”.</p>
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