cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57745/eek00n
title: Teneur Thallium HF QRF C2.png

description: :unav
DOI: 10.57745/mjvmnf
title: Teneur Cuivre HF QRF C2.png

description: :unav
DOI: 10.57745/vmeoou
title: Teneur Thallium HF KED C1.png

description: :unav
DOI: 10.57745/1h99cu
title: cu edta KED STot.png

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DOI: 10.57745/9sjxhd
title: hg hf QRF S30.png

description: :unav
DOI: 10.57745/vwdvf4
title: cd hf KED S30.png

description: :unav
DOI: 10.57745/n369qd
title: Teneur Cuivre HF KED C1.png

description: :unav
DOI: 10.57745/tsulvv
title: Teneur Zinc HF CE VibC1.png

description: :unav
DOI: 10.57745/et3ggp
title: pb edta KED STot.png

description: :unav
DOI: 10.57745/8c9w8a
title: ni edta KED STot.png

description: :unav
DOI: 10.57745/upaeju
title: Teneur Zinc HF QRF C1.png

description: :unav
DOI: 10.57745/ywj4yu
title: cd hf KED STot.png

description: :unav
DOI: 10.57745/da1ief
title: Teneur Plomb EDTA KED C2.png

description: :unav
DOI: 10.57745/vbnhkv
title: Teneur Nickel HF pts VibC1.png

description: :unav
DOI: 10.57745/7vn22a
title: Teneur Cadmium HF QRF C2.png

description: :unav
DOI: 10.57745/wnghjj
title: Zinc EDTA Canton VibC1.png

description: :unav
DOI: 10.57745/2k9ca7
title: pb edta QRF S30.png

description: :unav
DOI: 10.57745/aa4nms
title: Teneur Zinc EDTA pts VibC1.png

description: :unav
DOI: 10.57745/eleumt
title: Teneur Zinc HF QRF C2.png

description: :unav
DOI: 10.57745/wfwgpw
title: cd hf QRF S30.png

description: :unav
DOI: 10.57745/wm8xiy
title: Teneur Nickel EDTA QRF C1.png

description: :unav
DOI: 10.57745/69uioh
title: Teneur Plomb HF CE VibC1.png

description: :unav
DOI: 10.57745/yx0tki
title: zn edta KED S30.png

description: :unav
DOI: 10.57745/k01k6x
title: zn edta QRF STot.png

description: :unav
DOI: 10.57745/3w1wlb
title: Teneur Thallium HF pts VibC1.png

description: :unav
DOI: 10.57745/ryocab
title: Teneur Chrome HF QRF C1.png

description: :unav
DOI: 10.57745/zfweas
title: Teneur Cadmium EDTA QRF C2.png

description: :unav
DOI: 10.57745/sbwkly
title: Teneur Chrome HF KED C1.png

description: :unav
DOI: 10.57745/7sdtbn
title: Teneur Chrome HF QRF C2.png

description: :unav
DOI: 10.57745/j2gmfg
title: Chrome EDTA Canton VibC1.png

description: :unav
DOI: 10.57745/ssvphz
title: Teneur Zinc HF KED C2.png

description: :unav
DOI: 10.57745/bp8m6d
title: ni hf QRF STot.png

description: :unav
DOI: 10.57745/dcxkp9
title: tl hf QRF S30.png

description: :unav
DOI: 10.57745/mlfbdl
title: Teneur Nickel EDTA KED C1.png

description: :unav
DOI: 10.57745/ryigsy
title: Teneur Arsenic HF QRF C1.png

description: :unav
DOI: 10.57745/d2vjqf
title: pb hf KED S30.png

description: :unav
DOI: 10.57745/71tu7o
title: mo hf QRF STot.png

description: :unav
DOI: 10.57745/u5hhpm
title: pb hf KED STot.png

description: :unav
DOI: 10.57745/lsbdro
title: zn hf KED STot.png

description: :unav
DOI: 10.57745/3w9mtj
title: Molybdene HF Canton VibC1.png

description: :unav
DOI: 10.57745/u8ampl
title: cr edta QRF STot.png

description: :unav
DOI: 10.57745/sgj9oc
title: pb edta KED S30.png

description: :unav
DOI: 10.57745/pf5ujh
title: Chrome HF Canton VibC1.png

description: :unav
DOI: 10.57745/canw3b
title: Teneur Cadmium EDTA QRF C1.png

description: :unav
DOI: 10.57745/bcz24i
title: Nickel HF Canton VibC1.png

description: :unav
DOI: 10.57745/ryzjgv
title: cr hf KED S30.png

description: :unav
DOI: 10.57745/zo2lio
title: ni hf KED S30.png

description: :unav
DOI: 10.57745/bn1gey
title: pb hf QRF STot.png

description: :unav
DOI: 10.57745/kq55qg
title: Teneur Chrome EDTA CE VibC1.png

description: :unav
DOI: 10.57745/8lr0gm
title: Teneur Cadmium HF pts VibC1.png

description: :unav
DOI: 10.57745/boorsa
title: Teneur Plomb EDTA CE VibC1.png

description: :unav
DOI: 10.57745/l99guk
title: Plomb HF Canton VibC1.png

description: :unav
DOI: 10.57745/evr7ku
title: Teneur Cuivre EDTA CE VibC1.png

description: :unav
DOI: 10.57745/aww784
title: Teneur Mercure HF pts VibC1.png

description: :unav
DOI: 10.57745/cyak9e
title: Teneur Molybdene HF CE VibC1.png

description: :unav
DOI: 10.57745/etno06
title: co hf QRF STot.png

description: :unav
DOI: 10.57745/k1935b
title: Teneur Zinc EDTA KED C2.png

description: :unav
DOI: 10.57745/dljpp4
title: cu edta QRF S30.png

description: :unav
DOI: 10.57745/vyrqio
title: zn edta QRF S30.png

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DOI: 10.57745/2q7hv2
title: Teneur Cadmium HF KED C2.png

description: :unav
DOI: 10.57745/9eul5r
title: mo hf KED STot.png

description: :unav
DOI: 10.57745/ysgbjn
title: cu hf KED S30.png

description: :unav
DOI: 10.57745/hyfymi
title: cr edta KED STot.png

description: :unav
DOI: 10.57745/gm5aex
title: Teneur Molybdene HF QRF C1.png

description: :unav
DOI: 10.57745/pcpecp
title: hg hf KED S30.png

description: :unav
DOI: 10.57745/tmm4sg
title: Teneur Cuivre EDTA QRF C1.png

description: :unav
DOI: 10.57745/ieqiqg
title: Teneur Cuivre HF KED C2.png

description: :unav
DOI: 10.57745/j4wpso
title: Teneur Nickel HF CE VibC1.png

description: :unav
DOI: 10.57745/3po5od
title: Teneur Mercure HF CE VibC1.png

description: :unav
DOI: 10.57745/kee50k
title: Teneur Nickel HF QRF C1.png

description: :unav
DOI: 10.57745/qlf69l
title: as hf QRF STot.png

description: :unav
DOI: 10.57745/hsvewt
title: Teneur Chrome EDTA KED C2.png

description: :unav
DOI: 10.57745/dtjt4t
title: pb hf QRF S30.png

description: :unav
DOI: 10.57745/wtzmvw
title: Teneur Molybdene HF KED C2.png

description: :unav
DOI: 10.57745/4or5vl
title: cd edta KED S30.png

description: :unav
DOI: 10.57745/vnwqio
title: Teneur Nickel EDTA KED C2.png

description: :unav
DOI: 10.57745/tccbww
title: Teneur Mercure HF QRF C1.png

description: :unav
DOI: 10.57745/pur8zw
title: Teneur Zinc EDTA QRF C2.png

description: :unav
DOI: 10.57745/lfetq8
title: Cuivre EDTA Canton VibC1.png

description: :unav
DOI: 10.57745/zmb5fe
title: Teneur Nickel HF KED C2.png

description: :unav
DOI: 10.57745/zkmy6n
title: Teneur Zinc EDTA QRF C1.png

description: :unav
DOI: 10.57745/u9gaml
title: Teneur Plomb EDTA QRF C1.png

description: :unav
DOI: 10.57745/nzvuiv
title: Teneur Chrome EDTA QRF C2.png

description: :unav
DOI: 10.57745/ug7vfh
title: Teneur Nickel EDTA pts VibC1.png

description: :unav
DOI: 10.57745/w1mc1f
title: Teneur Cobalt HF KED C1.png

description: :unav
DOI: 10.57745/8ajrok
title: cd edta QRF STot.png

description: :unav
DOI: 10.57745/zdyily
title: Cadmium EDTA Canton VibC1.png

description: :unav
DOI: 10.57745/ff0lzv
title: Teneur Arsenic HF CE VibC1.png

description: :unav
DOI: 10.57745/tgdvbf
title: Teneur Plomb EDTA KED C1.png

description: :unav
DOI: 10.57745/9iz7nc
title: Teneur Cobalt HF KED C2.png

description: :unav
DOI: 10.57745/tbrvps
title: tl hf QRF STot.png

description: :unav
DOI: 10.57745/lpywcr
title: cd edta KED STot.png

description: :unav
DOI: 10.57745/xby38j
title: cu hf KED STot.png

description: :unav
DOI: 10.57745/51vusc
title: Teneur Thallium HF QRF C1.png

description: :unav
DOI: 10.57745/dkyzh9
title: Teneur Zinc EDTA KED C1.png

description: :unav
DOI: 10.5281/zenodo.7849293
title: Raw Datasets for Mg2V12

description: Experimental datasets for the Mg2V12 manuscript published at ChemRxiv:
description: S.R., D.S. and C.S. gratefully acknowledge financial support by the DFG Cluster of Excellence EXC 2154 "POLiS" (project no: 390874152) and the ERC Consolidator Grant "SupraVox" (grant no: 101002212). C.S. gratefully acknowledges the Gutenberg Research College Mainz and the Top-Level Research Area SusInnoScience for generous support. M.Sc. D. Kowalcyk and Prof. D. Ziegenbalg (Ulm University) are gratefully acknowledged for characterization of the emission features of the LED light source.
DOI: 10.5281/zenodo.8316822
title: Raw Dataset for Mg2V12 manuscript

description: Experimental datasets for manuscript "Coupled reaction equilibria enable the light-driven formation of metal-functionalized molecular vanadium oxides"
description: Financial support by the Deutsche Forschungsgemeinschaft DFG, project no 390874152 (S.R., C.S.), 389183496 (C.S.), 404530119 (C.S.), 510966757 (D.G.) and the ERC Consolidator Grant "SupraVox", rant no: 101002212 (C.S.) is gratefully acknowledged. C.S. gratefully acknowledges the Gutenberg Research College Mainz and the Top-Level Research Area SusInnoScience for financial support. M.Sc. D. Kowalcyk and Prof. D. Ziegenbalg (Ulm University) are acknowledged for characterization of the emission features of the LED light source. Prof. Sven Rau (Ulm), M.Sc. Ludwig Schwiedrzik (Vienna University) and M.Sc. David Hernández-Castillo (Vienna University) are gratefully acknowledged for constructive comments and discussions.
DOI: 10.5281/zenodo.7849292
title: Raw Dataset for Mg2V12 manuscript

description: Experimental datasets for manuscript "Coupled reaction equilibria enable the light-driven formation of metal-functionalized molecular vanadium oxides"
description: Financial support by the Deutsche Forschungsgemeinschaft DFG, project no 390874152 (S.R., C.S.), 389183496 (C.S.), 404530119 (C.S.), 510966757 (D.G.) and the ERC Consolidator Grant "SupraVox", rant no: 101002212 (C.S.) is gratefully acknowledged. C.S. gratefully acknowledges the Gutenberg Research College Mainz and the Top-Level Research Area SusInnoScience for financial support. M.Sc. D. Kowalcyk and Prof. D. Ziegenbalg (Ulm University) are acknowledged for characterization of the emission features of the LED light source. Prof. Sven Rau (Ulm), M.Sc. Ludwig Schwiedrzik (Vienna University) and M.Sc. David Hernández-Castillo (Vienna University) are gratefully acknowledged for constructive comments and discussions.
DOI: 10.25345/c5j960m4v
title: MassIVE MSV000092960 - CCREM_Microbiome_LCMS_HILIC_MS1_DDA
title: These samples are a part of a study investigating microbial responses to cover crop root exudates. We utilized 4 cover crop species (each with unique root exudate profiles), collected the pure root exudates, and applied them to soil mirocosms. metaG, metaT, metaP, and targeted and untargeted metabolomics were applied to assess the microbial responses.
DOI: 10.5281/zenodo.8380413
title: Pilot experiment DSS Hasmo/Herod fraglet-based dataset bipartitioning (70x70 Kohonen map)

description: As a follow up on a pilot experiment for manuscript dating in the DSS/IAA ERC project, 590 scans, each labeled on global style (Hasmonean/Herodian) were binarized using Maruf Dhali's BiNet neural network. Fragmented connected component contours were computed and compared to a precomputed <strong>70x70</strong> Kohonen map of DSS fragmented connected-component contours (*.fco3). Kohonen cells obtained the frequency counts for the labels Hasmonean and Herodian. Individual scans were characterized by their total Hasmo, Herod counts. Augmentation using random elastic morphing was used to obtain a comparable number of FCO3s per scan. The 2D feature vector with total counts #Hasmo and #Herod was subjected to PCA, with the goal of identifying the most informative axis for the Hasmonean/Herodian distinction. 91% of the samples (scans) could be correctly classified. Although this is a simple, within dataset bipartitioning experiment, the good results were taken as an indicator that the FCO3 fraglet feature would be useful in a more fine-grained style-based manuscript dating attempt using the radiocarbon-labeled data. See https://zenodo.org/deposit/8380279 for an accompanying .pdf technical report.
DOI: 10.5281/zenodo.8380412
title: Pilot experiment DSS Hasmo/Herod fraglet-based dataset bipartitioning (70x70 Kohonen map)

description: As a follow up on a pilot experiment for manuscript dating in the DSS/IAA ERC project, 590 scans, each labeled on global style (Hasmonean/Herodian) were binarized using Maruf Dhali's BiNet neural network. Fragmented connected component contours were computed and compared to a precomputed <strong>70x70</strong> Kohonen map of DSS fragmented connected-component contours (*.fco3). Kohonen cells obtained the frequency counts for the labels Hasmonean and Herodian. Individual scans were characterized by their total Hasmo, Herod counts. Augmentation using random elastic morphing was used to obtain a comparable number of FCO3s per scan. The 2D feature vector with total counts #Hasmo and #Herod was subjected to PCA, with the goal of identifying the most informative axis for the Hasmonean/Herodian distinction. 91% of the samples (scans) could be correctly classified. Although this is a simple, within dataset bipartitioning experiment, the good results were taken as an indicator that the FCO3 fraglet feature would be useful in a more fine-grained style-based manuscript dating attempt using the radiocarbon-labeled data. See https://zenodo.org/deposit/8380279 for an accompanying .pdf technical report.
DOI: 10.6084/m9.figshare.24199248
title: most_recent_proteomics_restructure_with_knn_impute.tsv

description: final set of IMPROVE cell line restructured proteomics data
DOI: 10.6084/m9.figshare.24199248.v1
title: most_recent_proteomics_restructure_with_knn_impute.tsv

description: final set of IMPROVE cell line restructured proteomics data
DOI: 10.25345/c5dn4061g
title: MassIVE MSV000092961 - Epitope Mapping of CCHFV Nucleoprotein
title: Mapping the binding site of mAb-9D5 on CCHFV Nucleoprotein as part of a larger study describing NP as an important target of protective mAb in mice
DOI: 10.5281/zenodo.5745983
title: SMAP-derived Perennial Firn Aquifer and Ice Slab Extents 2015-2019 Version 0

description: SMAP-derived Perennial Firn Aquifer and Ice Slab Extents 2015-2019 Version 0
DOI: 10.5281/zenodo.5745982
title: SMAP-derived Perennial Firn Aquifer and Ice Slab Extents 2015-2019 Version 1

description: SMAP-derived Perennial Firn Aquifer and Ice Slab Extents 2015-2019 Version 1
DOI: 10.5281/zenodo.8380493
title: SMAP-derived Perennial Firn Aquifer and Ice Slab Extents 2015-2019 Version 1

description: SMAP-derived Perennial Firn Aquifer and Ice Slab Extents 2015-2019 Version 1
DOI: 10.5281/zenodo.7943802
title: Monk data set - Miscellaneous script style line-strip images without transcription V0

description: Miscellaneous line-strip images <em>without transcription</em>, e.g., for pretraining, e.g., autoencoders, GANs or shape-based transformers. Number: 274164 line-strip images <strong>Monk manuscripts:</strong> GilmanJournal-16pages 19th century US diary, connected cursive<br> GilmanJournal-177_174<br> GilmanJournal-vol15<br> GilmanJournal-vol18<br> GilmanJournal-vol20<br> GilmanLetters-Fam-A Idem, with some typewriting<br> NL-AsdUvA_UBAinv373 Connected cursive, Dutch<br> NNM001001033 Natuurlijk Commissie/Naturalis - biologists' jungle field notes, Java, [Indonesia]<br> NNM001001034<br> NNM001001035<br> NNM001001036<br> NNM001001037<br> NNM001001117<br> NNM001001118<br> PP_Part01_01 - Prize Papers - with Brill <br> navis-H2_7823_0001-1094 - Kabinet of the King (KdK, Dutch National Archive) early 20th century, administrative, connected cursive<br> navis-NL-HaNA_2.02.04_3960<br> navis-NL-HaNA_2.02.14_7826<br>
description: Miscellaneous grants: NWO 612.066.514 - Learning to learn: An Adaptive Reading System using a High-Performance Morphed-Image Correlator; NWO 652.001.001 - Making sense of Illustrated Historical Archives; NWO 634.000.434 - TriGraph. NWO 640.002.402 - SCRATCH- SCRipt Analysis Tools for the Cultural Heritage
description: {"references": ["Tijn van der Zant, Lambert Schomaker, Svitlana Zinger & Henny van Schie (2009) Where are the Search Engines for Handwritten Documents?, Interdisciplinary Science Reviews, 34:2-3, 224-235, DOI: 10.1179/174327909X441126", "Schomaker, L. (2020). Lifelong Learning for Text Retrieval and Recognition in Historical Handwritten Document Collections. In Series in Machine Perception and Artificial Intelligence (pp. 221\u2013248). WORLD SCIENTIFIC. https://doi.org/10.1142/9789811203244_0012", "Schomaker, Lambert (2016). \"Design considerations for a large-scale image-based text search engine in historical manuscript collections\" it - Information Technology, vol. 58, no. 2, pp. 80-88. https://doi.org/10.1515/itit-2015-0049", "Ameryan, M., Schomaker, L. (2021) A limited-size ensemble of homogeneous CNN/LSTMs for high-performance word classification. Neural Comput & Applic 33, 8615\u20138634. https://doi.org/10.1007/s00521-020-05612-0"]}
DOI: 10.5281/zenodo.7943801
title: Monk data set - Miscellaneous script style line-strip images without transcription V0

description: Miscellaneous line-strip images <em>without transcription</em>, e.g., for pretraining, e.g., autoencoders, GANs or shape-based transformers. Number: 274164 line-strip images <strong>Monk manuscripts:</strong> GilmanJournal-16pages 19th century US diary, connected cursive<br> GilmanJournal-177_174<br> GilmanJournal-vol15<br> GilmanJournal-vol18<br> GilmanJournal-vol20<br> GilmanLetters-Fam-A Idem, with some typewriting<br> NL-AsdUvA_UBAinv373 Connected cursive, Dutch<br> NNM001001033 Natuurlijk Commissie/Naturalis - biologists' jungle field notes, Java, [Indonesia]<br> NNM001001034<br> NNM001001035<br> NNM001001036<br> NNM001001037<br> NNM001001117<br> NNM001001118<br> PP_Part01_01 - Prize Papers - with Brill <br> navis-H2_7823_0001-1094 - Kabinet of the King (KdK, Dutch National Archive) early 20th century, administrative, connected cursive<br> navis-NL-HaNA_2.02.04_3960<br> navis-NL-HaNA_2.02.14_7826<br>
description: Miscellaneous grants: NWO 612.066.514 - Learning to learn: An Adaptive Reading System using a High-Performance Morphed-Image Correlator; NWO 652.001.001 - Making sense of Illustrated Historical Archives; NWO 634.000.434 - TriGraph. NWO 640.002.402 - SCRATCH- SCRipt Analysis Tools for the Cultural Heritage
description: {"references": ["Tijn van der Zant, Lambert Schomaker, Svitlana Zinger & Henny van Schie (2009) Where are the Search Engines for Handwritten Documents?, Interdisciplinary Science Reviews, 34:2-3, 224-235, DOI: 10.1179/174327909X441126", "Schomaker, L. (2020). Lifelong Learning for Text Retrieval and Recognition in Historical Handwritten Document Collections. In Series in Machine Perception and Artificial Intelligence (pp. 221\u2013248). WORLD SCIENTIFIC. https://doi.org/10.1142/9789811203244_0012", "Schomaker, Lambert (2016). \"Design considerations for a large-scale image-based text search engine in historical manuscript collections\" it - Information Technology, vol. 58, no. 2, pp. 80-88. https://doi.org/10.1515/itit-2015-0049", "Ameryan, M., Schomaker, L. (2021) A limited-size ensemble of homogeneous CNN/LSTMs for high-performance word classification. Neural Comput & Applic 33, 8615\u20138634. https://doi.org/10.1007/s00521-020-05612-0"]}
DOI: 10.5281/zenodo.7970369
title: supplement document

description: none非不
DOI: 10.5281/zenodo.7970370
title: supplement document

description: none非不
DOI: 10.5281/zenodo.8330709
title: supplement document

description: none非不
DOI: 10.6084/m9.figshare.24199383
title: Most Recent IMPROVE restructured proteomics data

description: The most recent file of the restructured proteomics data with knn impute.
DOI: 10.6084/m9.figshare.24199383.v1
title: Most Recent IMPROVE restructured proteomics data

description: The most recent file of the restructured proteomics data with knn impute.
DOI: 10.5281/zenodo.8262586
title: Masterarbeit KJH

description: Master thesis Karl-Julius Haas "Dynamische Konnektivität der Fließwege im Drainagesystem Schwarzer Hirsch Stehender (Lehr- und Forschungsbergwerk Reiche Zeche)"
DOI: 10.5281/zenodo.8262585
title: Masterarbeit KJH

description: Master thesis Karl-Julius Haas "Dynamische Konnektivität der Fließwege im Drainagesystem Schwarzer Hirsch Stehender (Lehr- und Forschungsbergwerk Reiche Zeche)"
DOI: 10.6084/m9.figshare.24198894
title: bulk RNA-seq data processed by Sanger

description: bulk RNA-seq data processed by SangerThe metadata is downloaded from bulk RNA-seq data processed by Sanger (https://cellmodelpassports.sanger.ac.uk/downloads, rnaseq_all_20220624.zip).converted to a csv file where the rows are models names and the columns are the symbols.random white space is removed.
DOI: 10.6084/m9.figshare.24198894.v1
title: bulk RNA-seq data processed by Sanger

description: bulk RNA-seq data processed by Sanger The metadata is donwloaded from bulk RNA-seq data processed by Sanger (https://cellmodelpassports.sanger.ac.uk/downloads, rnaseq_all_20220624.zip). converted to a csv file where the rows are models names and the columns are the symbols. random white space is removed.
DOI: 10.21227/y3dv-8b19
title: TODOS: Thermal sensOr Data-driven Occupancy Estimation System for Smart Buildings

description: This dataset is associated with TODOS: Thermal sensOr Data-driven Occupancy Estimation System for Smart Buildings. It is a novel system for estimating occupancy in intelligent buildings, TODOS uses a low-cost, low-power thermal sensor array along with a passive infrared sensor. We introduce a novel data processing pipeline that allows us to automatically extract features from the thermal images using an artificial neural network. Through an extensive experimental evaluation, we show that TODOS provides occupancy detection accuracy of 98% to 100% under different scenarios. In addition, it solves the issue of occupancy over/under-counting by overlapping sensing areas when using multiple thermal sensors in large rooms. This is done by treating the entire area being sensed as a single input thermal image instead of partitioning the area into multiple thermal images individually processed. Furthermore, TODOS introduces a data augmentation technique that allows the generation of training data for rooms of different sizes and shapes, without requiring specific training data from each room. Using these data, TODOS can train specifically designed neural networks optimized for any room size and shape, and achieve almost the same level of occupancy detection accuracy in rooms where experimental labeled training data is available, making it a viable solution that generalizes to the different rooms in large buildings.This dataset contains time series logs of 8 by 8 heat map images collected by thermal sensors spread out over the entire room. Each sensor mote contains a grid-eye sensor to collect thermal images underneath, and a PIR sensor to duty cycle the thermal sensor. The output data would be occupancy count for the entire room being used for smart building energy control. There are 10 rooms/zones tested with different geometrical shapes and sizes, so different number of sensors and sensor arrangments.
DOI: 10.5519/qd.t6sdqmxc
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1387522 records)
DOI: 10.5281/zenodo.7870593
title: Bayesian spatiotemporal modelling of wildfire occurrences and sizes for projections under climate change (Data)

description: This repository contains the data necessary to reproduce the study developed in Legrand et al. (2023) "Bayesian spatiotemporal modelling of wildfire occurrences and sizes for projections under climate change"
DOI: 10.5281/zenodo.7870592
title: Bayesian spatiotemporal modelling of wildfire occurrences and sizes for projections under climate change (Data)

description: This repository contains the data necessary to reproduce the study developed in Legrand et al. (2023) "Bayesian spatiotemporal modelling of wildfire occurrences and sizes for projections under climate change"
DOI: 10.5517/ccdc.csd.cc2b9d8b
title: CCDC 2153981: Experimental Crystal Structure Determination

description: Related Article: Jia-Wei Wang, Fan Ma, Tao Jin, Piao He, Zhi-Mei Luo, Stephan Kupfer, Michael Karnahl, Fengyi Zhao, Zihao Xu, Tao Jin, Tianquan Lian, Yong-Liang Huang, Long Jiang, Li-Zhi Fu, Gangfeng Ouyang, Xiao-Yi Yi|2022|J.Am.Chem.Soc.|145|676|doi:10.1021/jacs.2c11740
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.