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4,681 results for “light”
Light curves for M4 millimagnitude RR Lyrae (mmRR)
<p>Included are files with light curve (measured brightness over time) data, about 80 days' worth, for three stars in the globular cluster M4 that exhibit millimagnitude level, RR Lyrae-like variability. The data are derived from images taken by NASA's Kepler/K2 mission. The enclosed README provides details on the data contained in the files. The variability shown by these stars has not been seen in similar kinds of stars before, and represents a new class of stellar variability as far as we have been able to determine.</p>
Elevation modulates the phenotypic responses to light of four co-occurring Pyrenean forest tree species
<p>Data on plant water potential for seedlings of four Pyrenean tree species planted along an elevation gradient. The dataset contains three files:</p> <ol> <li><strong>Biomass.txt: </strong>Data on plant biomass per fraction (leaf, stem and roots) 4 years after plantation. Included variables:<br> - Piso (factor): elevational stage at which the seedling was planted. Two levels: montane (M) or subalpine (S)<br> - Luz (factor): whether the seedling was plantes at a gap or in the understory. Two levels: gap (O) or understroy (T)<br> - N (numeric): number of plant in that plot<br> - Sp (factor): species. Four levels: BEPE (Betula pendula) / PISY (Pinus sylvestris) / PIUN (Pinus uncinata) / ABAL (Abies alba)<br> - Planta (factor): code to identify uniquely each plant<br> - File (factor): code to identify uniquely each plant<br> - GLI (num): Global Light Index, the amount of irradiance that receives each seedling<br> - Code (factor): code to identify uniquely each plant<br> - PLB (numeric): total plant biomass (g)<br> - LFB (numeric): leaf biomass (g)<br> - STB (numeric): stem biomass (g)<br> - RTB (numeric): root biomass (g)<br> - LMF (numeric): leaf mass fraction (LFB/PLB)<br> - SMF (numeric): stem mass fraction (STB/PLB)<br> - RMF (numeric): root mass fraction (RTB/PLB)<br> - SLA (numeric): specific leaf area<br> - H (numeric): plant height (mm)<br> - D (numeric): plant diameter at root collar (mm)<br> - PB2 (numeric): total plant biomass without considering leaves (g)<br> - SF2 (numeric): stem mass fraction without considering leaves (STB/PB2)<br> - RF2 (numeric): root mass fraction without considering leaves (RTB/PB2)</li> <li><strong>init_biomass.txt:</strong> for biomass at the moment of plantation<br> - Piso (factor): elevational stage at which the seedling was planted. Two levels: montane (M) or subalpine (S)<br> - N (numeric): number of plant<br> - Sp (factor): species. Four levels: BEPE (Betula pendula) / PISY (Pinus sylvestris) / PIUN (Pinus uncinata) / ABAL (Abies alba)<br> - Planta (factor): code to identify uniquely each plant<br> - File (numeric): code to identify uniquely each plant<br> - PB (numeric): total plant biomass (g)<br> - LB (numeric): leaf biomass (g)<br> - SB (numeric): stem biomass (g)<br> - RB (numeric): root biomass (g)</li> <li><strong>WaterPot.txt</strong>: data on plant water potential for seedlings of four Pyrenean tree species planted along an elevation gradient during a period of intense drought<br> - Piso (factor): elevational stage at which the seedling was planted. Two levels: montane (M) or subalpine (S)<br> - Luz (factor): whether the seedling was plantes at a gap or in the understory. Two levels: gap (O) or understroy (T)<br> - N (numeric): number of plant <br> - Parcela (factor): identifier ofthe plot<br> - Sp (factor): species. Four levels: BEPE (Betula pendula) / PISY (Pinus sylvestris) / PIUN (Pinus uncinata) / ABAL (Abies alba)<br> - Estacion (factor): the moment for the measurement. One level: September<br> - GLI (numeric): global light index, the ration of total irradiance received by the plant at the moment of plantation<br> - WPt (numeric): water potential (bars)</li> </ol>
Nocturnal Light Emitting Diode Induced Fluorescence (LEDIF): A new technique to measure the chlorophyll a fluorescence emission spectral distribution of plant canopies in situ
<p>This repository contains data reported in the below study:</p> <p>Atherton, J., Liu, W. and Porcar-Castell, A., 2019. Nocturnal Light Emitting Diode Induced Fluorescence (LEDIF): A new technique to measure the chlorophyll a fluorescence emission spectral distribution of plant canopies in situ. <em>Remote Sensing of Environment</em>.</p> <p>Each text file contains the data-set used to produce the relevant figure (see file name). You can find the data to produce A.4. online at https://avaa.tdata.fi/web/smart/smear/ </p> <p>Please pay attention to the following before using this data.</p> <ol> <li><strong>Figure2_lampRadPanel_Wm2srnm.txt</strong>: Note that the shapes are of interest here. The magnitude is not the same as the incident light at top of canopy, as these spectra were measured in a laboratory. See paper section A.1. for more details. </li> <li><strong>Figure3_LEDIFspectra_Wm2srnm.txt</strong>: This data contains the whole observed spectrum including the non-fluorescence regions, which were saturated (warped) in the visible. The fluorescence region is approximately > 650 nm. </li> <li><strong>Figure4_AQYspectra_nm.txt</strong>: As with Figure3 the whole spectrum is included here.</li> <li><strong>FigureA3_repLEDIFspectra_[pmay/psep/usep]._nm.txt</strong>: Data from which the mean spectra (Figure3) were calculated, including the uncorrected red spectra. I have split these by canopy type to avoid name conflicts.</li> </ol> <p> </p>
SuperNNova light-curve simulations
<p>Simulations used in SuperNNova: an open-source framework for Bayesian, Neural Network based supernova classification. Publication can be found in https://arxiv.org/abs/1901.06384</p> <p>Supernova light-curves (1,983,213) simulated using SNANA and their SALT2 fit. Seven supernova templates are used: Ia, Ib, Ic, II-n, IIL1, IIL2, II-P. </p> <p>Data is similar to the SPCC data (Kessler et al. 2010). DES light-curves are built from supernova templates and use SALT2 SN Ia SED models (Guy et al. 2007) and the trained model from JLA (Betoule et al. 2014). Observing logs specifying the simulated cadence and conditions were included when available.</p> <p>Data format:</p> <p>SNANA format is structured in the following way:</p> <p>_HEAD.FITS provide the supernova light-curve ID (SNID) and global properties like redshift, coordinates, etc.</p> <p>_PHOT.FITS provides the photometry. It is order in the same way as _HEAD. Separators between light-curves are given by 'MJD'==-777.00</p> <p>Other things to note:</p> <p>Subtypes can be identified by column SNTYPE in header "101": "Ia", "120": "IIP", "121": "IIn", "122": "IIL1", "123": "IIL2", "132": "Ib", "133": "Ic"</p> <p>To read and reformat to csv you can use <a href="https://github.com/supernnova/SuperNNova">SuperNNova</a> or this utility <a href="https://github.com/anaismoller/reformatting_snippets/blob/master/SNANA_FITS_to_pd.py">SNANA_FITS_to_pd.py</a></p>
The terrestrial carnivorous plant Utricularia reniformis sheds light on environmental and life-form genome plasticity: Annotation, Gene Ontology and raw data
<p><strong>Description:</strong> In this work, we deeply sequenced (genome and transcriptome of different organs), assembled, and analyzed the 311-Mbp genome of the terrestrial carnivorous plant <em>U. reniformis</em> (Lentibulariaceae). This project presents great importance to the understanding of genomic, evolutive and functional aspects of<em> U. reniformis</em>, which may, with the next-generation sequencing and computational biology approaches shed light to a better understanding not only for the biology and evolution of <em>Utricularia</em> genus, but also for other genera and lineages of the Lentibulariaceae family. Here we present all the raw data generated, including annotation and gene ontology files.</p> <p><strong>External Information</strong></p> <p><a href="https://genomevolution.org/coge/GenomeInfo.pl?gid=54799">Genome Browser</a> avaliable at CoGe Portal (https://genomevolution.org/coge/GenomeInfo.pl?gid=54799)</p> <p><a href="http://https://www.ncbi.nlm.nih.gov/bioproject/290588">GenBank </a><a href="http://https://www.ncbi.nlm.nih.gov/bioproject/290588">Bioproject</a> (https://www.ncbi.nlm.nih.gov/bioproject/290588) for raw genomic and transcriptomic reads</p> <p><a href="https://bv.fapesp.br/en/auxilios/84264/genomics-and-transcriptomics-of-utricularia-reniformis-lentibulariaceae-an-evolutive-and-function/">FAPESP grant website</a> contaning the project abstract and other information.</p> <p><strong>Papers published related to <em>Utricularia reniformis</em> genome</strong></p> <pre><strong>[1]</strong> Silva SR, Diaz YC, Penha HA, Pinheiro DG, Fernandes CC, Miranda VF, MichaelTP, Varani AM. <strong>The Chloroplast Genome of Utricularia reniformis Sheds Light on the Evolution of the ndh Gene Complex of Terrestrial Carnivorous Plants from the Lentibulariaceae Family</strong>. PLoS One. 2016 Oct 20;11(10):e0165176. doi:<strong><a href="https://www.ncbi.nlm.nih.gov/pubmed/27764252">10.1371/journal.pone.0165176</a></strong>. </pre> <pre><strong>[2] </strong>Silva SR, Alvarenga DO, Aranguren Y, Penha HA, Fernandes CC, Pinheiro DG, Oliveira MT, Michael TP, Miranda VFO, Varani AM. <strong>The mitochondrial genome of the terrestrial carnivorous plant Utricularia reniformis (Lentibulariaceae): Structure, comparative analysis and evolutionary landmarks.</strong> PLoS One. 2017 Jul19;12(7):e0180484. doi: <strong><a href="https://www.ncbi.nlm.nih.gov/pubmed/28723946">10.1371/journal.pone.0180484</a></strong>.</pre> <pre><strong>[3] </strong>Silva SR, Moraes AP, Penha HA, Julião MHM, Domingues DS, Michael TP, Miranda VFO, Varani AM. <strong>The Terrestrial Carnivorous Plant Utricularia reniformis Sheds Light on Environmental and Life-Form Genome Plasticity.</strong> Int J Mol Sci. 2019 Dec 18;21(1). pii: E3. doi: <strong><a href="https://www.ncbi.nlm.nih.gov/pubmed/31861318">10.3390/ijms21010003</a></strong>.</pre> <p><strong>Acknowledgements</strong></p> <p>This work was supported by Sao Paulo Research Foundation FAPESP, Grant ID: [1325164-6]</p> <p> </p> <p><strong>---------------------------------------------------------</strong><br> <strong>FILES DESCRIPTION</strong><br> <strong>---------------------------------------------------------</strong><br> <br> ----------------<br> <strong>ANNOT-vFinal.sql: </strong>MySQL database containing all integrated annotation information of Urenif and Ugibba<br> ----------------<br> <strong>TABLE fields description</strong><br> gene_name gene name generated by EVidence Modeler + PASA<br> length gene lenght<br> status duplicate_gene_classifier status (0:singleton, 1:dispersed, 2:proximal, 3: tandem, 4:WGD)<br> product gene product <br> GOterms Blast2GO/OmicsBox GOterms<br> GO_mapping Blast2GO/OmicsBox GOterms derived from direct mapping (UniProt)<br> GO_annotation Blast2GO/OmicsBox annotated GOterms<br> GO_interpro Blast2GO/OmicsBox derived from InterProScan<br> EC Blast2GO/OmicsBox EC number<br> EC_name Blast2GO/OmicsBox enzyme name<br> NOG_annot EggNOG annotation description<br> NOG_EC EggNOG EC number<br> NOG_GO EggNOG GOterms<br> NOG_class EggNOG COG/KOG classfication<br> KEGG_Pathway EggNOG KEGG pathyways<br> KEGG_ko EggNOG KEGG ko<br> CAZy EggNOG CAZy enzymes<br> TAIR_gene Closest A. thaliana gene name (homologous) TAIR database lasted version<br> TAIR_annot Closest A. thaliana gene product (homologous) TAIR database lasted version <br> ortho MCL clustering among Vvinifera, Athaliana, and Slycopersicum (S:singleton, C: clustered, Y: shared)<br> ortho_two MCL clustering among Urenif and Ugibba (S:singleton, C: clustered, Y: shared)<br> -<br> -<br> ----------------<br> <strong>CEGs.zip </strong> 336 shared and concatenated CEGs from Urenif, U. gibba, Genlisea nigrocaulis, G. hispidula, G. aurea, G. pygmaea, and G. repens.<br> ----------------</p> <p><strong>ProcessRepeats_mod</strong> Modified version of RepeatMasker, ProcessRepeats script for detection of plant evolutionary lineages<br> ----------------</p> <p><strong>----------------------------------------------------------------------------------------------------------------------------------------------<br> <em>Utricularia gibba</em> files<br> ----------------------------------------------------------------------------------------------------------------------------------------------</strong><br> <strong>Ugibba</strong><strong>-no-masked.fa </strong> Ugibba genome excluding organellar genomes (provided by Lan et al., 2017)<br> <strong>Ugibba-softmasked.fa</strong> Ugibba genome RepeatMasker softmasked and excluding organellar genomes (provided by Lan et al., 2017)<br> <strong>Ug.collinearity </strong> MCScanX collinearity file<br> <strong>Ug-duplicates.txt</strong> MCScanX duplicate_gene_classifier short report<br> <strong>Ug.gene_type </strong> MCScanX duplicate_gene_classifier full report<br> <strong>Ug.tandem </strong> Ugibba tandem genes generated by MCScanX tool<br> <strong>Ugibba_annot.annot </strong> Blast2GO/OmicsBox annotation file (eudicotyledons filtered and Viridiplantae GOSlim) <strong>Ugibba_annot-</strong><strong>noclean</strong><strong>.</strong><strong>annot</strong><strong> </strong> Blast2GO/OmicsBox annotation file (not filtered)<br> <strong>Ugibba</strong><strong>.cDNA</strong> Ugibba cDNAs fasta file<br> <strong>Ugibba</strong><strong>.CDS </strong> Ugibba CDSs fasta file<br> <strong>Ugibba</strong><strong>-EVM.all-no-TEs-PASA-ANNOTATED.gff3</strong> Ugibba GFF3 file fully annotated (including gene products and GO terms)</p> <p><strong>Ugibba</strong><strong>-EVM.all-no-TEs-PASA.gff3</strong> Ugibba GFF3 file fully annotated (genes only)<br> <strong>Ugibba_export.txt</strong> Blast2GO/OmicsBox full exported table<br> <strong>Ugibba_fasta.fasta</strong> Blast2GO/OmicsBox Ugibba fasta proteins containg annotation (product and GO terms)<br> <strong>ugibba_frozen_cleaned-validated.box</strong> Full Blast2GO/OmicsBox file</p> <p><strong>ugibba_frozen.box</strong> Full Blast2GO/OmicsBox file (containing TEs genes annotation)</p> <p><strong>ugibba_nogs_emapper_annotations.box</strong> Full Blast2GO/OmicsBox EggNOG file (containing TEs genes annotation)</p> <p><strong>Ugibba_GAF.txt</strong> GAF file<br> <strong>Ugibba</strong><strong>.gene</strong> Ugibba gene fasta file<br> <strong>Ugibba_GOstat.txt </strong> GOstat file<br> <strong>Ugibba</strong><strong>-PASA-assemblies.fasta </strong> Ugibba PASA assemblies<br> <strong>Ugibba</strong><strong>-PASA.stats </strong> Ugibba annotation STATS<br> <strong>Ugibba</strong><strong>.</strong><strong>prot</strong><strong> </strong> Ugibba protein fasta file<br> <strong>Ugibba</strong><strong>-RepeatMasker.gff </strong> Ugibba RepeatMasker gff file<br> <strong>Ugibba</strong><strong>-RepeatMasker.gff3 </strong> Ugibba RepeatMasker gff3 file<br> <strong>Ugibba</strong><strong>-RepeatMasker.tbl </strong> Ugibba RepeatMasker results<br> <strong>Ugibba</strong><strong>-RepeatMasker-v2.gff3</strong> Ugibba RepeatMasker gff3 second version file<br> <strong>Ugibba</strong><strong>-RNAseq-assembled.fasta </strong> Ugibba RNAseq assembled transcriptome (Trinity)<br> <strong>Ugibba_TEs_DANTE_2019.fa </strong> Ugibba TEs library, detected by REPET and annotated by PASTEC and DANTE<br> <strong>Ugibba_WEGO.txt </strong> WEGO file</p> <p><strong>----------------------------------------------------------------------------------------------------------------------------------------------<br> <em>Utricularia reniformis</em> files<br> ----------------------------------------------------------------------------------------------------------------------------------------------</strong><br> <strong>Urenif</strong><strong>-no-masked.fa </strong> Urenif genome excluding organellar genomes<br> <strong>Urenif</strong><strong>-</strong><strong>softmasked</strong><strong>.fa</strong> Urenif genome RepeatMasker softmasked and excluding organellar genomes<br> <strong>Ur.collinearity </strong> MCScanX collinearity file<br> <strong>Ur-duplicates.txt </strong> MCScanX duplicate_gene_classifier short report<br> <strong>Ur.gene_type</strong> MCScanX duplicate_gene_classifier full report<br> <strong>Ur.tandem</strong> Urenif tandem genes generated by MCScanX tool<br> <strong>Urenif_annot.annot</strong> Blast2GO/OmicsBox annotation file (eudicotyledons filtered and Viridiplantae GOSlim)<br> <strong>Urenif_annot-</strong><strong>noclean</strong><strong>.</strong><strong>annot</strong> Blast2GO/OmicsBox annotation file (not filtered)<br> <strong>Urenif</strong><strong>.cDNA</strong> Urenif cDNAs fasta file<br> <strong>Urenif</strong><strong>.CDS </strong> Urenif cDNAs fasta file<br> <strong>Urenif</strong><strong>-EVM.all-no-TEs-PASA-ANNOTATED.gff3</strong> Urenif GFF3 file fully annotated (including gene products and GO terms)</p> <p><strong>Urenif</strong><strong>-EVM.all-no-TEs-PASA.gff3</strong> Urenif GFF3 file fully annotated (genes only)<br> <strong>Urenif_export.txt</strong> Blast2GO/OmicsBox full exported table<br> <strong>Urenif_fasta.fasta</strong> Blast2GO/OmicsBox Urenif fasta proteins containg annotation (product and GO terms)<br> <strong>urenif_frozen_cleaned-validated.box</strong> Full Blast2GO/OmicsBox file</p> <p><strong>urenif_frozen.box</strong> Full Blast2GO/OmicsBox file (containing TEs genes annotation)</p> <p><strong>urenif_nogs_emapper_annotations.box</strong> Full Blast2GO/OmicsBox EggNOG file (containing TEs genes annotation)<br> <strong>Urenif_GAF.txt </strong> GAF file<br> <strong>Urenif</strong><strong>.gene</strong> Urenif gene fasta file<br> <strong>Urenif_GOStat.txt </strong> GOstat file<br> <strong>Urenif</strong><strong>-PASA-assemblies.fasta</strong> Urenif PASA assemblies<br> <strong>Urenif</strong><strong>-PASA.stats </strong> Urenif annotation STATS<br> <strong>Urenif</strong><strong>.</strong><strong>prot</strong><strong> </strong> Urenif protein fasta file<br> <strong>Urenif</strong><strong>-RepeatMasker.gff </strong> Urenif RepeatMasker gff file<br> <strong>Urenif</strong><strong>-RepeatMasker.gff3 </strong> Urenif RepeatMasker gff3 file<br> <strong>Urenif</strong><strong>-RepeatMasker.tbl </strong> Urenif RepeatMasker results<br> <strong>Urenif</strong><strong>-RepeatMasker-v2.gff3 </strong> Urenif RepeatMasker gff3 second version file<br> <strong>Urenif</strong><strong>-RNAseq-assembled.fasta </strong> Urenif RNAseq assembled transcriptome (Trinity)<br> <strong>Urenif_TEs_DANTE_2019.fa </strong> Urenif TEs library, detected by REPET and annotated by PASTEC and DANTE<br> <strong>Urenif_WEGO.txt </strong> WEGO file<br> <strong>----------------------------------------------------------------------------------------------------------------------------------------------<br> ----------------------------------------------------------------------------------------------------------------------------------------------</strong></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>
A New Spherical Light Field Database for Immersive Telecommunication and Telepresence Applications
<p>This database is created by the Realistic 3D research group at Mid Sweden University, Sundsvall, Sweden. The database details are explained thoroughly in the publication which was published at the 16th International Conference on Quality of Multimedia Experience (QoMEX) in 2024. This database was also reviewed as part of the submission and publication process.</p> <p>You can use this database in your work under the Creative Commons Attribution 4.0 International (CC-BY 4.0) licence, provided that you cite the database as below:</p> <blockquote> <p>Zerman, E., Gond, M., Takhtardeshir, S., Olsson, R., & Sjöström, M. (2024). A Spherical Light Field Database for Immersive Telecommunication and Telepresence Applications. <em>The 16th International Conference on Quality of Multimedia Experience (QoMEX)</em>. IEEE. DOI: <a href="https://ieeexplore.ieee.org/abstract/document/10598264">10.1109/QoMEX61742.2024.10598264</a></p> </blockquote> <p>BibTeX:</p> <blockquote> <p>@inproceedings{zerman2024spherical,<br> title = {A Spherical Light Field Database for Immersive Telecommunication and Telepresence Applications},<br> author = {Zerman, Emin and Gond, Manu and Takhtardeshir, Soheib and Olsson, Roger and Sj{\"o}str{\"o}m, M{\aa}rten},<br> booktitle = {The 16th International Conference on Quality of Multimedia Experience (QoMEX)},<br> year = {2024},<br> organization = {IEEE},<br> doi = {10.1109/QoMEX61742.2024.10598264}<br>}</p> </blockquote> <p>This database contains 20 spherical light fields of 1 x 60 views, captured with a consumer-grade 360-degree camera: Insta360 X3. The capture was done using a dolly to ensure the separation between consecutive views is exactly 1 cm. In addition to the original captures, this database also provides outputs for two different use cases: compression and view synthesis. Several parameters, features, and objective quality metric values are also included.</p> <p>N.B. Only the README file and this description have been updated after the initial submission on 2024-02-09.</p>
Dimensional crossover in a quantum gas of light: Datasets
<p>This repository contains the data presented in the manuscript titled "Dimensional crossover in a quantum gas of light" by K. Karkihalli Umesh et al. published in Nature Physics, https://doi.org/10.1038/s41567-024-02641-7</p> <p>Files "Figxy_data.zip" contain the data required to reproduce Figure xy</p> <p>The file "Raw Data.zip" contains the raw data used to determine chemical potential, internal energy, photon number shown in the manuscript in Fig. 4 anf Fig. 5. It also contains a python script which can be used to plot the raw as well as the transmission corrected spectra.</p> <p>Each zip-folder contains a readme with more detailed information.</p> <p>Files "ExtDataFigxy.zip" contain the data for additional images not in the main manuscript. </p> <p>Compared to version 1, the scaling in the figures was changed (the definition of \tilde{N} was changed slightly), and the additional images where added. </p>
Light-Induced Metallic and Paramagnetic Defects in Halide Perovskites from Magnetic Resonance
<p>EPR and NMR data for the research article titled "Light-Induced Metallic and Paramagnetic Defects in Halide Perovskites from Magnetic Resonance". For further details see the readme.txt file. DOI: https://doi.org/10.1021/acsenergylett.4c02557</p>
Cows, Pigs and People: Example data of cubic insulin from three different species recorded on Diamond Light Source I24
<p>Data collected at 100K on 10th May 2024 at I24 (Diamond Light Source) to investigate automatic grouping of datasets containing very subtle differences. Crystals grown by Cicely Tam following standard techniques with coordination from Felicity Bertram. For each of bovine, porcine, and human insulin, 10 degree wedges are included. Insulin from these three sources differ by 1-3 amino acids, but are otherwise structurally isomorphous. </p> <p>The purpose of the data upload is to make data available for tutorials using the DIALS toolchain (see e.g. examples at https://github.com/graeme-winter/dials_tutorials) however data are available for all purposes without limitation. </p> <p>Key:</p> <p>CIX - bovine insulin</p> <p>PIX - porcine insulin</p> <p>X - human insulin</p>
Optimizing the Shelling Process of InP/ZnS Quantum Dots Using a Single-Source Shell Precursor: Implications for Lighting and Display Applications
<p>This is the data supporting the manuscript "Optimizing the Shelling Process of InP/ZnS Quantum Dots Using a Single-Source Shell Precursor: Implications for Lighting and Display Applications".</p> <p>Abstract</p> <p>InP/ZnS core/shell quantum dots (QDs), recognized as highly promising heavy-metal-free emitters, are increasingly utilized in lighting and display applications. Their synthesis in a tubular flow reactor enables production in a highly efficient, scalable, and reproducible manner, particularly when combined with a single-source shell precursor, such as zinc diethyldithiocarbamate (Zn(S2CNEt2)2). However, the photoluminescence quantum yield (PLQY) of QDs synthesized with this route remains significantly lower compared to those synthesized in batch reactors involving multiple steps for the shell growth. Our study identifies the formation of absorbing, yet non-emissive ZnS nanoparticles during the ZnS shell formation process as a main contributing factor to this discrepancy. By varying the shelling conditions, especially the shelling reaction temperature and InP core concentration, we investigated the formation of pure ZnS nanoparticles and their impact on the optical properties, particularly PLQY, of the resultant InP/ZnS QDs through UV-vis absorption, steady-state and time-resolved photoluminescence (PL) spectroscopy, scanning transmission electron microscopy (STEM) and analytical ultracentrifugation (AUC) measurements. Our results suggest that process conditions, such as lower shelling temperatures or reduced InP core concentrations (resulting in a lower external surface area), encourage the homogeneous nucleation of ZnS. This reduces the availability of shell precursors necessary for an effective passivation of the InP core surfaces, ultimately resulting in lower PLQYs. These findings explain the origin of persistently underperformed PLQY of InP/ZnS QDs synthesized from this synthesis route and suggest further optimization strategies to improve their emission for lighting and display applications.</p> <p>The data are sorted per techniques used for characterization. Information about the measurement details can be found in README files attached to each technique folder.</p>
Data from "Robust sensory traits across light habitats: Visual signals but not receptors vary in centrarchids inhabiting distinct photic environments"
<p>Visual communication in fish is often shaped by the light environment they inhabit, influencing both sensory (e.g., eye size, opsin gene expression), and signaling traits (e.g., body reflectance). This study explores the phenotypic variation in the visual communication traits of six species of centrarchids (Centrarchidae) inhabiting two contrasting light environments. We measured morphological, molecular, and signaling traits to determine their responses to photic conditions. Our findings reveal significant interspecific variation in sensory traits but no consistent phenotypic variation between light environments. Centrarchids showed robust visual systems with red-green dichromatic vision, which was largely unaffected by the different light habitats. We also found significant molecular evolution in the visual opsin genes, although these changes were not associated with environmental conditions. However, body reflectance displayed species-specific responses to environmental conditions, suggesting that signaling traits may be more flexible than sensory traits. Overall, our results challenge the generality of the current paradigm in visual ecology, which portrays visual systems in fish as highly tunable owing to photic conditions. Our study highlights the potential evolutionary or developmental constraints on centrarchid visual systems and their implications for adaptability to various habitats and novel environmental threats.</p> <p>This dataset includes underwater light measurements, retinal transcriptomics, eye morphology, and spectral reflectance data to assess the effects of environment and species identity on eye size, opsin gene expression, chromophore usage, and body reflectance of centrarchids. Furthermore, we test for signatures of molecular evolution on the amino acid sequence of visual opsin genes across species and populations. By combining data on the visual ecology of different species from two distinct light environments, we ask i) do the visual traits of centrarchids vary across photic environments? and ii) are phenotypic responses to light conditions shared among species or are they species-specific? Overall, we found robust visual systems across species (no environmental effect) but variable body reflectance across species and environments (genotype-by-environment interaction, G × E). This suggests that divergent species-specific responses in signaling might help offset the lack of fine-tuning in the visual system of centrarchids. </p> <p>For more information see ReadMe file.</p>
Data from: "DarkSide-20k sensitivity to light dark matter particles"
<p>These files provide expected limits from the preprint of arXiv:2407.05813, "DarkSide-20k sensitivity to light dark matter particles" and are made available by the DarkSide-20k Collaboration.</p>
NMR data of all compounds appearing in the study "Visible Light-Mediated Formal Alkylation and [4+1]-Cycloaddition Strategies of Silyl Enol Ethers with Aryldiazoacetates"
<p>FID files of NMR data (<em>e.g.</em> 1H, 13C{1H}, 19F{1H}, COSY, HSQC, DEPT135 and HMBC) associated with all compounds synthesized during the study entitled "Visible Light-Mediated Reactions of Silyl Enol Ethers with Aryldiazoacetates: Formal Alkylation and [4+1]-Cycloaddition Strategies" (a research project in organic synthesis)</p>
Dataset for "Scaling of ultrashort-pulsed laser structuring processes for electromobility applications using a spatial light modulator"
<p>The dataset represents the experimental data for publication "<span>Scaling of ultrashort-pulsed laser structuring processes for electromobility applications using a spatial light modulator</span>"</p>
Vacuum-Sublimed Cocrystalline Thin Films of Naphthalene Bisimide and Pt(II) Complex for Phosphorescent Light-Emitting Diodes
<p>Additional data to report <a href="https://doi.org/10.1002/adom.202402117">https://doi.org/10.1002/adom.202402117</a>:</p> <p>Cocrystals employing small organic molecules with platinum(II)-complexes are known to exhibit organic room-temperature phosphorescence (RTP). However, this desirable property was so far only demonstrated in the macroscopic 1:1 cocrystalline state, which limitsdevice applications outside of small single-crystal devices. Here, we show that vacuum cosublimed thin films of both components in various mixing ratios form layers with selfassembled small cocrystalline domains which exhibit RTP. This Pt(II) doping improved the photoluminescence (PL) quantum yield (ΦPL) of the now phosphorescence emitting 1,8:4,5-naphthalene bisimide (NBI) from below 0.1% to 9% in respective thin films. These doped layers were employed as active layers in light-emitting diodes to emit red electroluminescence (EL). Via time-resolved measurements the lifetime of the device EL was determined in accordance with the PL to be around 50 µs. Maximum external quantum efficiencies (EQEs) of over 0.2% with RTP emission signatures consistent with the PL of solution-grown cocrystals could be achieved.</p>
Single objective light-sheet acquired large-scale 3D dataset
<p>This dataset covers Fig. 5 of the following publication:</p> <p>Title: Tilt-invariant scanned oblique plane illumination microscopy for large-scale volumetric imaging<br> Authors: Manish Kumar and Yevgenia Kozorovitskiy <br> Optics Letters Vol. 44, Issue 7, pp. 1706-1709 (2019)<br> https://doi.org/10.1364/OL.44.001706</p> <p>Briefly: The sample imaged is a Thy1GFP expressing transgenic mice brain slice - fixed and coverslipped. No clearing was performed for these.</p> <p>See "readme.txt" for additional info and details about how to use "shearNscaleObliq" file.</p>
First estimation of global trends in nocturnal power emissions reveals acceleration of light pollution
<p>The power emitted by different countries at night is based on DMSP and VIIRS data. Inclued also, some extra data from Spain, Portugal, Italy, UK and Greece.</p>
Dataset and Code for Manuscript "Multi-angle pulse shape detection of scattered light in flow cytometry for label-free cell cycle classification"
<p>Dataset of measurements for cell cycle analysis with description:</p> <ul> <li>ReadMe file with explanations on the data set and analysis</li> <li>exemplary Matlab script file for analysis</li> <li>binary data files conatining the pulse shapes in all channels</li> <li>FCS data files containing common flow cytometry parameters in each channel</li> </ul> <p>Data on unsorted HEK cells, HEK cells sorted for cell cycle phases, and unsorted Jurkat cell are included.</p>
Landscape composition drives the impacts of artificial light at night on insectivorous bats
<p>Abstract of the related publication :</p> <p>Among the most prevalent sources of biodiversity declines, Artificial Light At Night (ALAN) is an emerging threat<br> to global biodiversity. Much knowledge has already been gained to reduce impacts. However, the spatial variation<br> of ALAN effects on biodiversity in interaction with landscape composition remains little studied, though it is<br> of the utmost importance to identify lightscapes most in need of action. Several studies have shown that, at local<br> scale, tree cover can intensify positive or negative effects of ALAN on biodiversity, but none have – at landscape<br> scale – studied a wider range of landscape compositions around lit sites. We hypothesized that the magnitude of<br> ALAN effects will depend on landscape composition and species’ tolerance to light. Taking the case of insectivorous<br> bats because of their varying sensitivity to ALAN, we investigated the species-specific activity response to<br> ALAN. Bat activity was recorded along a gradient of light radiance. We ensured a large variability in landscape<br> composition around 253 sampling sites. Among the 13 bat taxa studied, radiance decreased the activity of two<br> groups of the slow-flying gleaner guild (Myotis and Plecotus spp.) and one species of the aerial-hawking guild<br> (Pipistrellus pipistrellus), and increased the activity of two species of the aerial-hawking guild (Pipistrellus kuhlii<br> and Pipistrellus pygmaeus). Among these five effects, the magnitude of four of them was driven by landscape composition.<br> For five other species, ALAN effects were only detectable in particular landscape compositions, making<br> the main effect of radiance undetectable without account for interactions with landscape. Specifically, effects<br> were strongest in non-urban habitats, for both guilds. Results highlight the importance to prioritize ALAN reduction<br> efforts in non-urban habitats, and how important is to account for landscape composition when studying<br> ALAN effects on bats to avoid missing effects.</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.