cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7903589
title: HiTIC-Monthly: A Monthly High Spatial Resolution (1 km) Human Thermal Index Collection over China during 2003–2020 [GeoTIFF]

description: The monthly <strong>Hi</strong>gh spatial resolution human <strong>T</strong>hermal <strong>I</strong>ndex <strong>C</strong>ollection (<strong>HiTIC-Monthly</strong>) includes <strong><em>near-surface air temperature</em> </strong>(SAT) and 11 commonly used<strong> <em>human-perceived temperature</em> </strong>indices: indoor Apparent Temperature (AT<sub>in</sub>), outdoor shaded Apparent Temperature (AT<sub>out</sub>), Discomfort Index (DI), Effective Temperature (ET), Heat Index (HI), Humidex (HMI), Modified Discomfort Index (MDI), Net Effective Temperature (NET), simplified Wet Bulb Globe Temperature (sWBGT), Wet-Bulb Temperature (WBT), and Wind Chill Temperature (WCT). This dataset has a high spatial resolution of 1 km × 1 km and covers mainland China from January 2003 to December 2020. The overall R-square, root mean square error, and mean absolute error of the 12 thermal indices in the HiTIC dataset are 0.996, 0.693°C, and 0.512°C, respectively. It is compressed by year, and each zip package is composed of 12 monthly images in the<strong> GeoTIFF format</strong>. The unit of the dataset is 0.01 degree Celsius (°C), and the values are stored in an integer type (Int16) for saving storage space, and need to be divided by 100 to get the values in degree Celcius when in use. The projection coordinate system of the dataset is Albers Equal Area Conic Projection. The naming rule and other detailed information can be found in “README.pdf”. If you have any questions when using the HiTIC-Monthly dataset, please feel free to contact Miss Hui Zhang via zhangh573@mail2.sysu.edu.cn, Dr. Ming Luo via luom38@mail.sysu.edu.cn, or Dr. Yongquan Zhao via zhaoyq66@mail.sysu.edu.cn. More details on the procedure of producing the HiTIC-Monthly dataset and its accuracy assessment can be found in: Zhang, H., Luo, M., Zhao, Y., Lin, L., Ge, E., Yang, Y., Ning, G., Cong, J., Zeng, Z., Gui, K., Li, J., Chan, T. O., Li, X., Wu, S., Wang, P., and Wang, X.: HiTIC-Monthly: a monthly high spatial resolution (1 km) human thermal index collection over China during 2003–2020, Earth Syst. Sci. Data, 15, 359–381, https://doi.org/10.5194/essd-15-359-2023, 2023.
description: For HiTIC-Monthly dataset in NetCDF format, please refer to https://zenodo.org/record/6895533.
DOI: 10.5281/zenodo.7895982
title: Shallow Convection Datasets Simulated by Different Large Eddy Models

description: The datasets contain four shallow convection cases (RICO, BOMEX, ATEX, and ARM-SGP) simulated by three large eddy models (SAM, WRF, UCLA-LES). For each convection case, there are nineteen two-dimensional variables, including winds, temperature, and humidity. Three types of plumes are provided, i.e., convective core refers to rising plumes that have positive buoyancy and contain condensed water, and convective updraft defined as plumes containing liquid water and upward vertical velocity, and cloud defined as grid points containing liquid water. Conditionally sampled variables such as in-cloud temperature, moisture and vertical velocity are also provided, which are indispensable for calculating entrainment rate. These datasets will be used as a reference to help users verify and improve parameterization schemes of shallow convection. Table 2 List of the LES output variables Variable Long name Unit U Zonal wind m/s V Meridional wind m/s W Vertical velocity m/s W2 Variance of vertical velocity m<sup>2</sup>/s<sup>2</sup> QT Total water g/kg QV Water vapor g/kg QC Cloud condensate g/kg P Pressure hPa Z Height m A_cor Core fraction 100% A_upd Updraft fraction 100% A_cld Cloud fraction 100% QT_cor Mean qt in core g/kg QT_upd Mean qt in updraft g/kg QT_cld Mean qt in cloud g/kg W_cor Mean w in core m/s W_upd Mean w in updraft m/s W_cld Mean w in cloud m/s QTFLUX Total water flux W/m<sup>2</sup>
DOI: 10.5281/zenodo.7895981
title: Shallow Convection Datasets Simulated by Different Large Eddy Models

description: The datasets contain four shallow convection cases (RICO, BOMEX, ATEX, and ARM-SGP) simulated by three large eddy models (SAM, WRF, UCLA-LES). For each convection case, there are nineteen two-dimensional variables, including winds, temperature, and humidity. Three types of plumes are provided, i.e., convective core refers to rising plumes that have positive buoyancy and contain condensed water, and convective updraft defined as plumes containing liquid water and upward vertical velocity, and cloud defined as grid points containing liquid water. Conditionally sampled variables such as in-cloud temperature, moisture and vertical velocity are also provided, which are indispensable for calculating entrainment rate. These datasets will be used as a reference to help users verify and improve parameterization schemes of shallow convection. Table 2 List of the LES output variables Variable Long name Unit U Zonal wind m/s V Meridional wind m/s W Vertical velocity m/s W2 Variance of vertical velocity m<sup>2</sup>/s<sup>2</sup> QT Total water g/kg QV Water vapor g/kg QC Cloud condensate g/kg P Pressure hPa Z Height m A_cor Core fraction 100% A_upd Updraft fraction 100% A_cld Cloud fraction 100% QT_cor Mean qt in core g/kg QT_upd Mean qt in updraft g/kg QT_cld Mean qt in cloud g/kg W_cor Mean w in core m/s W_upd Mean w in updraft m/s W_cld Mean w in cloud m/s QTFLUX Total water flux W/m<sup>2</sup>
DOI: 10.14272/jgucylisnorqfw-uhfffaoysa-n.1
title: Tetrakis(4-ethynylphenyl)methane
title: InChI=1S/C33H20/c1-5-25-9-17-29(18-10-25)33(30-19-11-26(6-2)12-20-30,31-21-13-27(7-3)14-22-31)32-23-15-28(8-4)16-24-32/h1-4,9-24H

description: This is a physical chemical entity[CHEBI_24431] associated with a molecule[CHEBI_25367]. The molecule[CHEBI_25367] can be described by the following structural desciptors[cheminf_000085]: InChI descriptor[cheminf_000113]: InChI=1S/C33H20/c1-5-25-9-17-29(18-10-25)33(30-19-11-26(6-2)12-20-30,31-21-13-27(7-3)14-22-31)32-23-15-28(8-4)16-24-32/h1-4,9-24H, and canonical SMILES descriptor[cheminf_000007]: C#Cc1ccc(cc1)C(c1ccc(cc1)C#C)(c1ccc(cc1)C#C)c1ccc(cc1)C#C, and by the IUPAC name[cheminf_000107]: 1-ethynyl-4-[tris(4-ethynylphenyl)methyl]benzene. The physical chemical entity[CHEBI_24431] has a component solvent[CHEBI_46787] which is described by the canonical SMILES descriptor[cheminf_000007]: C#Cc1ccc(cc1)C(c1ccc(cc1)C#C)(c1ccc(cc1)C#C)c1ccc(cc1)C#C The physical chemical entity[CHEBI_24431] has the following Sample ID as registered in the research data repository chemotion (www.chemotion-repository.net, https://doi.org/10.25504/FAIRsharing.iagXcR): CRS-33168 The physical chemical entity[CHEBI_24431] can be described by the physical descriptors [CHEMINF_000025]: Melting point descriptor[CHEMINF_000256]: Boiling point descriptor[CHEMINF_000257]: Refractive index descriptor[CHEMINF_000253]: The physical chemical entity[CHEBI_24431] can be further described by the following assays[OBI:0000070][CHMO:0001133]: CHMO:0000593 | 1H nuclear magnetic resonance spectroscopy (1H NMR) CHMO:0000595 | 13C nuclear magnetic resonance spectroscopy (13C NMR) CHMO:0000630 | infrared absorption spectroscopy (IR) CHMO:0000480 | electron ionisation mass spectrometry (EI-MS) CHMO:0000498 | high-resolution mass spectrometry (HRMS) The physical chemical entity[CHEBI_24431] was deposited to the Molecule Archive of the Karlsruhe Insitute of Technology (KIT) with the following Sample ID: Used ontologies: CHEBI - Chemical Entities of Biological Interest CHEMINF - chemical information ontology (information entities about chemical entities) CHMO - Chemical Methods Ontology OBI - Ontology for Biomedical Investigations
DOI: 10.14272/jgucylisnorqfw-uhfffaoysa-n/chmo0000630
title: infrared absorption spectroscopy (IR) (1-ethynyl-4-[tris(4-ethynylphenyl)methyl]benzene)
title: InChI=1S/C33H20/c1-5-25-9-17-29(18-10-25)33(30-19-11-26(6-2)12-20-30,31-21-13-27(7-3)14-22-31)32-23-15-28(8-4)16-24-32/h1-4,9-24H

description: dataset for infrared absorption spectroscopy (IR) IR (ATR, ṽ) = 3281 (s), 1730 (vw), 1496 (vs), 1400 (w), 1373 (vw), 1247 (w), 1208 (w), 1186 (vw), 1179 (vw), 1115 (w), 1017 (m), 959 (w), 841 (w), 826 (vs), 768 (w), 663 (vs), 629 (vs), 569 (s), 558 (vs), 518 (m), 503 (w), 414 (m) cm–1.
DOI: 10.14272/jgucylisnorqfw-uhfffaoysa-n/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) (1-ethynyl-4-[tris(4-ethynylphenyl)methyl]benzene)
title: InChI=1S/C33H20/c1-5-25-9-17-29(18-10-25)33(30-19-11-26(6-2)12-20-30,31-21-13-27(7-3)14-22-31)32-23-15-28(8-4)16-24-32/h1-4,9-24H

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR) 1H NMR (400 MHz, Chloroform-d [7.27 ppm], ppm) δ = 7.42–7.38 (m, 8H, CArH), 7.15–7.11 (m, 8H, CArH), 3.07 (s, 4H, CH).
DOI: 10.14272/jgucylisnorqfw-uhfffaoysa-n/chmo0000498
title: high-resolution mass spectrometry (HRMS) (1-ethynyl-4-[tris(4-ethynylphenyl)methyl]benzene)
title: InChI=1S/C33H20/c1-5-25-9-17-29(18-10-25)33(30-19-11-26(6-2)12-20-30,31-21-13-27(7-3)14-22-31)32-23-15-28(8-4)16-24-32/h1-4,9-24H

description: dataset for high-resolution mass spectrometry (HRMS) HRMS–EI (m/z): [M]+ calcd for C33H20 416.1560; found 416.1558.
DOI: 10.14272/jgucylisnorqfw-uhfffaoysa-n/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) (1-ethynyl-4-[tris(4-ethynylphenyl)methyl]benzene)
title: InChI=1S/C33H20/c1-5-25-9-17-29(18-10-25)33(30-19-11-26(6-2)12-20-30,31-21-13-27(7-3)14-22-31)32-23-15-28(8-4)16-24-32/h1-4,9-24H

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR) 13C NMR (100 MHz, Chloroform-d [77.0 ppm], ppm) δ = 146.2 (4C, Cq), 131.6 (8C, +, CArH), 130.7 (8C, +, CArH), 120.3 (4C, Cq), 83.2 (4C, Cq), 77.6 (4C, CH), 64.8 (Cq).
DOI: 10.14272/jgucylisnorqfw-uhfffaoysa-n/chmo0000480
title: electron ionisation mass spectrometry (EI-MS) (1-ethynyl-4-[tris(4-ethynylphenyl)methyl]benzene)
title: InChI=1S/C33H20/c1-5-25-9-17-29(18-10-25)33(30-19-11-26(6-2)12-20-30,31-21-13-27(7-3)14-22-31)32-23-15-28(8-4)16-24-32/h1-4,9-24H

description: dataset for electron ionisation mass spectrometry (EI-MS) MS (EI, 70 eV, 160 °C), m/z (%): 417 (35) [M+H]+, 416 (100) [M]+, 316 (26), 315 (94), 313 (28), 214 (16), 213 (51), 202 (11).
DOI: 10.5281/zenodo.7816711
title: MMpedia: A Large-scale Multi-modal Knowledge Graph

description: <strong>List of files:</strong> entity2image.json: The entity to map images file MMpedia_triples.ttl: The triples file EntlistXX.tar: The image file MMpedia dataset is split into 132 subsets and each subset is compressed into a <code>.tar</code> file. After unziping files under the folder "MMpedia", the data structure are as following: <pre><code>|-MMpedia |-Entitylist1 |-Entity1 |-1.jpg |-2.jpg |-3.jpg ... |-Entity2 |-Entity3 ... |-Entitylist2 |-Entitylist3</code></pre> For example, the path <code>MMpedia/Entlist141/Bart Tanski/Bart Tanski+1.jpg</code> means the image corresponding to the entity "Bart Tanski" <strong>Other image files can be found in following URLs:</strong> MMpedia2 | Zenodo MMpedia3 | Zenodo MMpedia4 | Zenodo <strong>For more details, please refer to </strong>Delicate2000/MMpedia (github.com)
DOI: 10.5281/zenodo.7816710
title: MMpedia: A Large-scale Multi-modal Knowledge Graph

description: <strong>List of files:</strong> entity2image.json: The entity to map images file MMpedia_triples.ttl: The triples file EntlistXX.tar: The image file MMpedia dataset is split into 132 subsets and each subset is compressed into a <code>.tar</code> file. After unziping files under the folder "MMpedia", the data structure are as following: <pre><code>|-MMpedia |-Entitylist1 |-Entity1 |-1.jpg |-2.jpg |-3.jpg ... |-Entity2 |-Entity3 ... |-Entitylist2 |-Entitylist3</code></pre> For example, the path <code>MMpedia/Entlist141/Bart Tanski/Bart Tanski+1.jpg</code> means the image corresponding to the entity "Bart Tanski" <strong>Other image files can be found in following URLs:</strong> MMpedia2 | Zenodo MMpedia3 | Zenodo MMpedia4 | Zenodo <strong>For more details, please refer to </strong>Delicate2000/MMpedia (github.com)
DOI: 10.6084/m9.figshare.22785104.v1
title: <strong>data for Projecting China’s future water footprints and water scarcity under socioeconomic and climate change pathways using an integrated simulation approach</strong>

description: data for <strong>Projecting China’s future water footprints and water scarcity under socioeconomic and climate change pathways using an integrated simulation approach</strong>
DOI: 10.6084/m9.figshare.22785104.v2
title: Supplementary data for the study by Sun et al. titled "Projecting China’s future water footprints and water scarcity under socioeconomic and climate change pathways using an integrated simulation approach".

description: Supplementary data for the study by Sun et al. titled "Projecting China’s future water footprints and water scarcity under socioeconomic and climate change pathways using an integrated simulation approach".
DOI: 10.5281/zenodo.7911840
title: Datasheet of non-operative prolapsed IVD patients

description: Datasheet of non-operative prolapsed IVD patients
DOI: 10.5281/zenodo.7911839
title: Datasheet of non-operative prolapsed IVD patients

description: Datasheet of non-operative prolapsed IVD patients
DOI: 10.5281/zenodo.7911850
title: Datasheet of operative prolapsed IVD patients

description: Datasheet of operative prolapsed IVD patients
DOI: 10.5281/zenodo.7911849
title: Datasheet of operative prolapsed IVD patients

description: Datasheet of operative prolapsed IVD patients
DOI: 10.26183/99ap-f764
title: Survey of rural infant feeding practices dataset

description: Survey study of rural mothers infant feeding practices relating to the introduction of allergens and breastfeeding. 
Spreadsheet contains sample of 376 quantitative surveys gathered from more than 50 rural NSW postcodes. Also includes open-ended responses to questions of attitude to ASCIA infant feeding guidelines. 
Dataset contains sensitive data. To discuss the data, please contact Peter Denyer-Simmons @ WSU. p.denyer-simmons@westernsydney.edu.au ORCID 0000-0003-0985-5913.
DOI: 10.6084/m9.figshare.22776173
title: Dataset of Orthosiphon aristatus effect on DNCB-induced Atopic Dermatitis Mice Model

description: this dataset consist: Clinical Scores.xlsx (dataset of skin lesion severity score) Data Collective.docx (dataset of cytokines serum levels in experimental groups) HPLC.pdf (dataset of HPLC analysis of Orthosiphon aristatus extract) Raw Data.xls (dataset of raw data before statistical analysis)
DOI: 10.6084/m9.figshare.22776173.v1
title: Dataset of Orthosiphon aristatus effect on DNCB-induced Atopic Dermatitis Mice Model

description: this dataset consist: Clinical Scores.xlsx (dataset of skin lesion severity score) Data Collective.docx (dataset of cytokines serum levels in experimental groups) HPLC.pdf (dataset of HPLC analysis of Orthosiphon aristatus extract) <br>
DOI: 10.11588/data/yvyeo8/aulhez
title: Figure 2-figure supplement 1-source data.zip
DOI: 10.11588/data/yvyeo8/jtpfdj
title: Figure 3-source data.zip
DOI: 10.11588/data/yvyeo8/phr6aa
title: Figure 2-source data.zip
DOI: 10.11588/data/yvyeo8/qjwexf
title: Figure 4-figure supplement 1-source data.zip
DOI: 10.11588/data/yvyeo8/wtqang
title: Figure 1-source data.zip
DOI: 10.11588/data/yvyeo8/0u6juc
title: Figure 5-source data.zip
DOI: 10.11588/data/yvyeo8/1ghqlf
title: Figure 4-figure supplement 2-source data.zip
DOI: 10.11588/data/yvyeo8/mfqkgd
title: Figure 7-figure supplement 1-source data.zip
DOI: 10.11588/data/yvyeo8/ov6wzn
title: Figure 6-figure supplement 1.zip
DOI: 10.11588/data/yvyeo8/zyji1c
title: Figure 7-source data.zip
DOI: 10.11588/data/yvyeo8/v9vdlf
title: Figure 4-figure supplement 3-source data.zip
DOI: 10.11588/data/yvyeo8/x8ufos
title: Source_Data_Legend_Sadhu_et_al.txt

description: :unav
DOI: 10.5281/zenodo.7906828
title: MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 1km SIN Grid in 2015

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global FVC product at 1 km spatial resolution and 8-day temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 1 km resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous. This dataset is the MUSES FVC product in 2015. <em>Please <strong>click here</strong> to download the MUSES FVC product <strong>in 2014</strong></em>, <em>and <strong>click here</strong> to download the MUSES FVC product <strong>in 2016</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2015 Spatial Resolution: 1 km Temporal Resolution: 8 days Projection: Sinusoidal Data Format: HDF Scale: 0.004 Valid Range: 0 – 250 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.5281/zenodo.7906827
title: MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 1km SIN Grid in 2015

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global FVC product at 1 km spatial resolution and 8-day temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 1 km resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous. This dataset is the MUSES FVC product in 2015. <em>Please <strong>click here</strong> to download the MUSES FVC product <strong>in 2014</strong></em>, <em>and <strong>click here</strong> to download the MUSES FVC product <strong>in 2016</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2015 Spatial Resolution: 1 km Temporal Resolution: 8 days Projection: Sinusoidal Data Format: HDF Scale: 0.004 Valid Range: 0 – 250 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.15151/esrf-es-1059302893
title: Molecular machines in energy transduction, cellular signaling and defense processes

description: Since this BAG is composed of seven different working groups from three different locations (university of Cologne, university of Bochum, university of Düsseldorf), the projects for this proposal are of rather heterogeneous nature. Nonetheless, we are focusing on 5 main topics: 1.) proteins involved in transport processes, transporters or channels, either embedded in the membrane or acting in the cytosol (e.g. ABC transporters, fusion proteins, regulators) 2.) proteins involved in the immune system, pathogen invasion or defense/resistance processes (dehydratases, toxins, binding proteins) 3.) proteins involved or associated with diseases like cancer or developmental disorders (e.g. protein kinase, toxins, antibodies) 4.) enzymes of putative industrial use to encounter increasing environmental pollution (e.g. transaminases, estereases) 5.) proteins involved in light harvesting, photosynthesis (C3/C4 pathways) and chromophore binding proteins (e.g. synthases, photosensor domains)
DOI: 10.15151/esrf-es-1078532652
title: ITABAG: 3D structures of macromolecules related to human health

description: The ITABAG includes structural biologists from Universities, the National Research Council and Hospital research centres in Italy. The laboratories take advantage of single crystal and small angle X-ray diffraction methods to study the structure-function relationships in various macromolecules. A unifying factor for the vast majority of the macromolecules under study is that they are related to human health, either directly, being human proteins involved in some disease, or indirectly, as proteins expressed by human pathogens and validated as potential pharmacological targets. Other projects investigate basic chemical processes, such as crystal growth, or aim at characterising macromolecules with potential biotechnological applications.
DOI: 10.15151/esrf-es-1191059039
title: continuation of MA4929 - MA5656

description: continuation of previous users battery experiments
DOI: 10.15151/esrf-es-1113444864
title: Resolving the microscopic nature of electronic phase separation in the CMR manganites via 3DXRD

description: Colossal magnetoresistance (CMR) in the manganite perovskites arises from intrinsic electronic phase separation (EPS) occurring because of the competition between ferromagnetic conducting and antiferromagnetic orbitally ordered insulating phases, at specific doping levels. However, the very nature of the EPS state has frustrated any attempts over the past 3 decades to accurately characterise the microscopic structures of its constituents. We propose a diffraction resolved tomography experiment with nano-focused beam on ID11 to tackle this long-standing problem. The results will allow for accurate microscopic structural models of the competing states to be ascertained for the first time and will also provide a 3-dimensional map of strain fields within the electronically separated phases on a 0.1-100 μm length scale. Our insights will lead to a broader understanding of how these functional properties are linked with EPS phenomena so that these can be systematically tuned and enhanced.