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Supplementary Information: CHAPTER 3 - Classification of genomic features of plant-associated bacteria using machine learning
<p>Appendix A- List of all bacterial genomes used in orthologous genes clustering in the feature extraction step and in the further steps to build and test classifiers’ models. The list includes the isolation source information and the related category for the genome classification and features selection purposes.</p> <p>Appendix B - Distribution of genomes by phylum, family, and genus among the categories defined according to bacteria lifestyle association.</p> <p>Appendix C - Enriched orthogroups by genus according to each enrichment test (Material and Methods). Values for each test are "Y" (enriched), "N" (not enriched), or "Untested" (clusters were untested when there was insufficient phylogenetic signal, they were too small or were found in all genomes).</p> <p>Appendix D - Classification performance of random forest and logistic regression techniques applied to genus-specific datasets of genomic features (orthogroups) using both matrices from gene count number and presence/absence values. Sensitivity is a measure of how well a test identifies true positives; Specificity: is a measure how well a test or model avoids false positives; Positive Predictive Value (Pos. Pred. Value): The probability that a positive prediction is correct; Negative Predictive Value (Neg. Pred. Value): The probability that a negative prediction is correct; Precision: The accuracy of positive predictions; Recall (Sensitivity): The ability to find all relevant cases; F1 Score: A combined measure of precision and recall; Prevalence: The proportion of positive cases in the total; Detection Rate: The proportion of true positive cases identified; Detection Prevalence: The proportion of positive predictions; Balanced Accuracy: An average of sensitivity and specificity; Area Under the Curve (AUC): The overall performance of the model in distinguishing between positive and negative cases.</p> <p>Appendix E - Orthogroups assigned with predicted COGs as an important feature for classifying plant-associated genomes. COG categories: A - RNA processing and modification; B - Chromatin structure and dynamics; C - Energy production and conversion; D - Cell cycle control, cell division, chromosome partitioning; E - Amino acid transport and metabolism; F - Nucleotide transport and metabolism; G - Carbohydrate transport and metabolism; H - Coenzyme transport and metabolism; I - Lipid transport and metabolism; J - Translation, ribosomal structure and biogenesis; K - Transcription; L - Replication, recombination and repair; M - Cell wall/membrane/envelope biogenesis; N - Cell motility; O - Posttranslational modification, protein turnover, chaperones; P - Inorganic ion transport and metabolism; Q - Secondary metabolites biosynthesis, transport and catabolism; R - General function prediction only; S - Function unknown; T - Signal transduction mechanisms; U - Intracellular trafficking, secretion, and vesicular transport; V - Defense mechanisms; W - Extracellular structures; X - Mobilome: prophages, transposons; Y - Nuclear structure; Z - Cytoskeleton.</p>
Supplementary information
<p><strong>Supplementary information</strong></p> <p> </p> <p>The changes between version 1 to version 2 are described in README file.</p>
Supplementary Information "Key drivers and pressures of global water scarcity hotspots"
<p>Supplementary information of "Key drivers and pressures of global water scarcity hotspots".</p> <ul> <li>Supplementary table A - SCOPUS search strings</li> <li>Supplementary table B - DPSIR definitions</li> <li>Supplementary table C - Data resources</li> <li>Supplementary figures D - Lineplots of historical data analysis</li> <li>Supplementary table E - DPSIR case study results</li> <li>Supplementary figures F - Circular barplots DPSIR analysis per hotspot</li> </ul>
SympTEMIST Corpus: Gold Standard annotations for clinical symptoms, signs and findings information extraction
<p><strong>SympTEMIST</strong> stands for Symptoms TExt MIning Shared Task. It is a shared task and set of resources focused on the <strong>detection of mentions, normalization and indexing of symptoms, signs and findings in medical documents</strong> in <strong>Spanish</strong>. SympTEMIST is complementary to the DisTEMIST (https://temu.bsc.es/distemist) and MedProcNER/ProcTEMIST (https://temu.bsc.es/medprocner) corpora as they all use the same document collection.</p> <p> </p> <p><strong>Please cite if you use this dataset:</strong></p> <p>"Lima-López, S., Farré-Maduell, E., Gasco-Sánchez, L., Rodríguez-Miret, J. and Krallinger, M. (2023). Overview of SympTEMIST at BioCreative VIII: corpus, guidelines and evaluation of systems for the detection and normalization of symptoms, signs and findings from text. In: <em>Proceedings of the BioCreative VIII Challenge and Workshop: Curation and Evaluation in the era of Generative Models</em>."</p> <p> </p> <p>This repository includes the:</p> <ul> <li><strong>Train </strong>and<strong> Test Set </strong>for the three subtasks</li> <li><strong>SYMPTEMIST gazetteer </strong>of <strong>SNOMED symptoms, signs & findings </strong></li> <li><strong>Multilingual </strong>Silver Standard<strong> </strong>in <strong>9 languages:</strong> <ul> <li><em><strong>English</strong></em></li> <li><em><strong>Portuguese</strong></em></li> <li><em><strong>French</strong></em></li> <li><em><strong>Italian</strong></em></li> <li><em><strong>Romanian</strong></em></li> <li><em><strong>Catalan</strong></em></li> <li><em><strong>Swedish</strong></em></li> <li><em><strong>Dutch</strong></em></li> <li><em><strong>Czech</strong></em></li> </ul> </li> <li><strong>Background set</strong> of over 15,000 clinical cases.</li> <li><strong>Spanish</strong> Silver Standard (predictions by participants over the background set)</li> </ul> <p> </p> <p>Please read the README file attached for more information on folder structure and file format.</p> <p> </p> <p>SympTEMIST was developed by the Barcelona Supercomputing Center's NLP for Biomedical Information Analysis and used as part of BioCREATIVE 2023. For more information on the corpus, annotation scheme and task in general, please visit: https://temu.bsc.es/symptemist.</p> <p> </p> <p><strong>Resources:</strong></p> <ul> <li><a href="https://temu.bsc.es/symptemist/"><strong>Task web</strong></a></li> <li><a href="https://biocreative.bioinformatics.udel.edu/"><strong>BioCreative web</strong></a></li> <li><strong>Citation: </strong>Lima-López, S., Farré-Maduell, E., Gasco-Sánchez, L., Rodríguez-Miret, J. and Krallinger, M. (2023). Overview of SympTEMIST at BioCreative VIII: corpus, guidelines and evaluation of systems for the detection and normalization of symptoms, signs and findings from text. In: <em>Proceedings of the BioCreative VIII Challenge and Workshop: Curation and Evaluation in the era of Generative Models</em>.</li> <li><a href="../records/8246440"><strong>Annotation guidelines</strong></a></li> <li><a href="../records/10103191"><strong>BioCreative/AMIA workshop proceedings</strong></a></li> <li><a href="../doi/10.5281/zenodo.10104546"><strong>Overview paper</strong></a></li> <li><a href="https://www.youtube.com/playlist?list=PLyLDDulunoofv79Pci0-y2rGP5GOBoZhp"><strong>Youtube videos (overview & teams)</strong></a></li> <li><a href="https://www.slideshare.net/MartinKrallinger/symptemist-shared-task-on-symptoms-signs-and-findings-detection-and-normalization-task-overview-talk-at-biocreative-viii-workshop-amia-2023"><strong>SympTEMIST overview talk slides at BioCreative/AMIA workshop</strong></a></li> </ul> <p> </p> <p><strong>License</strong></p> <p>This work is licensed under a <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p> <p><strong>Contact</strong></p> <p>If you have any questions or suggestions, please contact us at:</p> <p>- Salvador Lima-López (<salvador [dot] limalopez [at] gmail [dot] com>)<br>- Martin Krallinger (<krallinger [dot] martin [at] gmail [dot] com>)</p> <p><strong>Additional resources and corpora</strong></p> <p>If you are interested in SympTEMIST, you might want to check out these corpora and resources:</p> <ul> <li><a href="../records/7614764">DisTEMIST</a> (Corpus of disease mentions and normalization to SNOMED CT, same document collection)</li> <li><a href="../records/8224056">MedProcNER </a>(Corpus of clinical procedure mentions and normalization to SNOMED CT, same document collection)</li> <li><a href="../records/4270158">PharmaCoNER</a> (Corpus of medications, drugs, chemical substances, genes, proteins and vaccine mentions and normalization, same document collection)</li> <li><a href="../records/7116201">MEDDOPROF</a> (Corpus of mentions of professions, occupations and working status and normalization, different document collection with some overlapping documents)</li> <li><a href="../records/8403498">MEDDOPLACE</a> (Corpus of mentions of place-related entity mentions, including departments, nationalities or patient movements etc.. and normalization, different document collection with some overlapping documents)</li> <li><a href="../records/4279323">MEDDOCAN</a> (Corpus of mentions of Personal Health Identifiers (PHI), modified synthetic verions of the document collection)</li> <li><a href="../records/3978041">CANTEMIST</a> (Corpus of cancer tumor morphology mentions and normalization, different document collection)</li> <li><a href="../records/3837305">CodiESp</a> (Corpus of clinical case reportes with assigned clinical codes from ICD10, Spanish version, same document collection)</li> <li><a href="../records/7684093">LivingNER</a> (Corpus of mentions of species, including human/family members, pathogens, food, etc.. and normalization to NCBI Taxonomy, different document collection with some overlapping documents)</li> <li><a href="../records/2560344">SPACCC-POS</a> (Corpus of clinical case reports in Spanish annotated with POS-tags, same document collection)</li> <li><a href="../records/2560338">SPACCC-TOKEN</a> (Corpus of clinical case reports in Spanish annotated with token-tags (word mention boundaries), same document collection)</li> <li><a href="../records/2560338">SPACCC-SPLIT</a> (Corpus of clinical case reports in Spanish annotated with sentence boundary-tags, same document collection)</li> <li><a href="../records/5602914">MESINESP-2</a> (Corpus of manually indexed records with DeCS /MeSH terms comprising scientific literature abstracts, clinical trials, and patent abstracts, different document collection)</li> </ul> <p> </p>
Supplementary Information of Multistage Provincial Power Expansion Planning and Market Design toward Energy Transition: A Case Study of Xinjiang in China
<p>Supplementary Information of Multistage Provincial Power Expansion Planning and Market Design toward Energy Transition: A Case Study of Xinjiang in China</p>
Data used in "Biologically informed deep neural network for prostate cancer discovery" publication
<p>Data used in the publication titled "<strong>Biologically informed deep neural network for prostate cancer discovery </strong>" </p> <p>Elmarakeby, Haitham A., et al. "Biologically informed deep neural network for prostate cancer discovery." <em>Nature</em> 598.7880 (2021): 348-352.</p> <p>These datasets were derived from the following public domain resources:</p> <ol> <li>Armenia J, Wankowicz SAM, Liu D, Gao J, Kundra R, Reznik E, et al. The long tail of oncogenic drivers in prostate cancer. Nat Genet. 2018;50: 645–651. DOI: <a href="https://doi.org/10.1038/s41588-018-0078-z">10.1038/s41588-018-0078-z</a></li> <li>Fraser M, Sabelnykova VY, Yamaguchi TN, Heisler LE, Livingstone J, Huang V, et al. Genomic hallmarks of localized, non-indolent prostate cancer. Nature. 2017;541: 359–364. https://doi.org/10.1038/nature20788</li> <li>Robinson DR, Wu Y-M, Lonigro RJ, Vats P, Cobain E, Everett J, et al. Integrative clinical genomics of metastatic cancer. Nature. 2017;548: 297–303. https://doi.org/10.1038/nature23306</li> <li>Fabregat A, Jupe S, Matthews L, Sidiropoulos K, Gillespie M, Garapati P, et al. The Reactome Pathway Knowledgebase. Nucleic Acids Res. 2018;46: D649–D655. DOI: <a href="https://doi.org/10.1093/nar/gkv1351">10.1093/nar/gkv1351</a></li> </ol> <p> </p>
Data for paper "Information flow between motor cortex and striatum reverses during skill learning"
<p>Simultaneous spiking activity and local field potential (LFP) recordings from motor cortex (M1) and dorsolateral striatum (DLS) during learning of a reach-to grasp task.<br>Deposited data include spiking activity and LFP recordings, reaching trajectories, andi single-trial success data for the 8 animals included in the paper "Information flow between motor cortex and striatum reverses during skill learning".</p>
Supplementary Material for the Paper: "Mining Architectural Information: A Systematic Mapping Study"
<p>The supplementary material is composed of three files:</p> <p><strong>1. SelectedStudies.pdf</strong><br>contains the 104 studies selected in this systematic mapping study.</p> <p><strong>2. PublicationVenues.pdf</strong></p> <p>contains the major publication venues of the selected studies.</p> <p><strong>3. Dataset.xlsx</strong></p> <p>contains the following Excel sheets:</p> <p><em><strong>(1) Architecting Activities</strong></em></p> <p>contains the 11 architecting activities, mined architectural information, datasets and sources used in the selected studies, and relevant studies.</p> <p><em><strong>(2) Automatic Approaches</strong></em></p> <p>contains tasks, automatic approaches, used sources, mined architectural information, and relevant studies.</p> <p><em><strong>(3) Semi-automatic Approaches</strong></em></p> <p>contains tasks, semi-automatic approaches, used sources, mined architectural information, and relevant studies.</p> <p><em><strong>(4) Manual Approaches</strong></em></p> <p>contains tasks, manual approaches, used sources, mined architectural information, and relevant studies.</p> <p><em><strong>(5) General Tools </strong></em></p> <p>contains general tool names, description of each tool, URL of each tool (if provided), and relevant studies.</p> <p><em><strong>(6) Automatic Tools</strong></em></p> <p>contains tasks, automatic tool names, used sources, mined architectural information, URL of each tool (if provided), and relevant studies.</p> <p><em><strong>(7) Semi-automatic Tools</strong></em></p> <p>contains tasks, semi-automatic tool, used sources, mined architectural information, URL of each tool (if provided), and relevant studies.</p>
LD information used in MUSSEL for EUR, AFR, AMR, EAS, and SAS
<p>Estimated LD block matrices and other LD information used in MUSSEL for EUR, AFR, AMR, EAS, and SAS for approximately 2.0 million SNPs in HapMap 3 plus MEGA. Given the limited storage space on Zenodo, we have deposited the LD information generated based on 1000 Genomes LD reference panel and provided the link to download the LD files below using:</p> <p>(1) 1000 Genomes LD reference panel:</p> <p>EUR: <a href="https://www.dropbox.com/s/wvxh4yqthm8m7uf/EUR.zip?dl=0">https://www.dropbox.com/s/wvxh4yqthm8m7uf/EUR.zip?dl=0</a> (~6.73G, unzip by: tar -zxvf EUR.tar.gz) </p> <p>AFR: <a href="https://www.dropbox.com/s/iwqg65uieevfzj2/AFR.zip?dl=0">https://www.dropbox.com/s/iwqg65uieevfzj2/AFR.zip?dl=0</a> (~7.69G, unzip by: tar -zxvf AFR.tar.gz)</p> <p>AMR: <a href="https://www.dropbox.com/s/mev5zyf4x6m076q/AMR.zip?dl=0">https://www.dropbox.com/s/mev5zyf4x6m076q/AMR.zip?dl=0</a> (~8.80G, unzip by: tar -zxvf AMR.tar.gz)</p> <p>EAS: <a href="https://www.dropbox.com/s/o28mlovtakv5n7v/EAS.zip?dl=0">https://www.dropbox.com/s/o28mlovtakv5n7v/EAS.zip?dl=0</a> (~5.63G, unzip by: tar -zxvf EAS.tar.gz)</p> <p>SAS: <a href="https://www.dropbox.com/s/idp02rgl8xv379b/SAS.zip?dl=0">https://www.dropbox.com/s/idp02rgl8xv379b/SAS.zip?dl=0</a> (~2.60G, unzip by: tar -zxvf SAS.tar.gz)</p> <p> </p> <p>(2) UK Biobank reference panel:</p> <p>EUR: <a href="https://www.dropbox.com/scl/fi/09yd12dest1tqxkt8p8ch/EUR.zip?rlkey=774vb1e5d6hfnyucilx160cyo&dl=0">https://www.dropbox.com/scl/fi/09yd12dest1tqxkt8p8ch/EUR.zip?rlkey=774vb1e5d6hfnyucilx160cyo&dl=0</a> (~13.15G, unzip by: tar -zxvf EUR.tar.gz) </p> <p>AFR: <a href="https://www.dropbox.com/scl/fi/jfymih83anr2vuevmfqok/AFR.zip?rlkey=r1lxpn1fnbk98ssf8f8ji4xkk&dl=0">https://www.dropbox.com/scl/fi/jfymih83anr2vuevmfqok/AFR.zip?rlkey=r1lxpn1fnbk98ssf8f8ji4xkk&dl=0</a> (~11.59G, unzip by: tar -zxvf AFR.tar.gz)</p> <p>AMR: <a href="https://www.dropbox.com/s/2ba4tsbhz03rg83/AMR.zip?dl=0">https://www.dropbox.com/s/2ba4tsbhz03rg83/AMR.zip?dl=0</a> (~4.88G, unzip by: tar -zxvf AMR.tar.gz)</p> <p>EAS: <a href="https://www.dropbox.com/s/uofu788707dp4xv/EAS.zip?dl=0">https://www.dropbox.com/s/uofu788707dp4xv/EAS.zip?dl=0</a> (~4.27G, unzip by: tar -zxvf EAS.tar.gz)</p> <p>SAS: <a href="https://www.dropbox.com/scl/fi/o635c86ylthbl3omfetbu/SAS.zip?rlkey=ot396toxl0phaiae15cnpbeyn&dl=0">https://www.dropbox.com/scl/fi/o635c86ylthbl3omfetbu/SAS.zip?rlkey=ot396toxl0phaiae15cnpbeyn&dl=0</a> (~11.44G, unzip by: tar -zxvf SAS.tar.gz)</p>
Extended Data Fig. 2-27 Geographical information of bioinformatic predicted samples based on the analysis of 16S rRNA gene in four PE degrading bacteria
Open the record for dataset details and reuse information.
Benchmark dataset for protein function prediction that integrates various protein information
<p>This research was funded by the National Science Centre in Poland (grant number 2021/41/N/ST6/01919)</p>
The best of both worlds: highlighting the synergies of combining knowledge modelling and automated techniques to improve information search and discovery in oil and gas exploration
<p><span>Research suggests organizations across all sectors waste a significant amount of time looking for information and often fail to leverage the information they have. In response, many organizations have deployed some form of enterprise search to improve the ‘findability’ of information. Debates persist as to whether thesauri and manual indexing or automated machine learning techniques should be used to enhance discovery of information. In addition, the extent to which a Knowledge Organization System (KOS) enhances discoveries or indeed blinds us to new ones remains a moot point. The oil and gas industry is used as a case study using a representative organization. Drawing on prior research, a theoretical model is presented which aims to overcome the shortcomings of each approach. This synergistic model could help to re-conceptualize the ‘manual’ versus ‘automatic’ debate in many enterprises, accommodating a broader range of information needs. This may enable enterprises to develop more effective information and knowledge management strategies and ease the tension between what are often perceived as mutually exclusive competing approaches. Certain aspects of the theoretical model may be transferable to other industries, which is an area for further research.</span></p>
Figure 18. Unassigned Emu Bay Shale radiodont setal blades. A, B, SAMA P54822. Body flap and setal blades. A, SAMA P54822a. B, camera lucida drawing incorporating information from counterpart SAMA P54822b. C, SAMA P50287. D, SAMA P43611a in The early Cambrian Emu Bay Shale radiodonts revisited: morphology and systematics
Figure 18. Unassigned Emu Bay Shale radiodont setal blades. A, B, SAMA P54822. Body flap and setal blades. A, SAMA P54822a. B, camera lucida drawing incorporating information from counterpart SAMA P54822b. C, SAMA P50287. D, SAMA P43611a. Scale bars: A, B, D = 10 mm; C = 5 mm.
Supplementary Information: Multimodal binding and inhibition of bacterial ribosomes by the 2 antimicrobial peptides Api137 and Api88
<div> <div>This dataset contains important data files for the MD simulation that are part of this publication.</div> <div> </div> <div>The "simulations" directory contains Gromacs parameter files (.mdp) and the run input files (.tpr) as well as the final coordinate files (.gro) of each individual production simulation.</div> <div> </div> <div>The directory "figure3" contains the raw data used to create Figure 3 in the manuscript.</div> <div> </div> <div>The subdirectory "a" contains the data for the PCA projection plot in subfigure 3a. It includes projections of the simulation ensembles of Api88 conformation I-III on to the two dominant conformational modes (.xvg) and the respective extreme conformations (.pdb). The projections of the three initial models and the optimized structure set are also included.</div> <div> </div> <div>Subdirectory "b" contains a numpy array (.npy) with the data for the correlation heatmap in subfigure 3b.</div> <div> </div> <div>Subdirectory "c" contains the results of several correlation-optimization searches. Each directory "N#_maps", where # is to be replaced by the number of structures in the set, contains the search results for N correlation-optimized structures in the Api88 trajectories in the form of a pickled python dictionary (state.pkl). The dictionary has the following keys:</div> <ul> <li>used: Already used sets of MD structures (frozenset)</li> <li>selection: Structure set selected in the last iteration (set)</li> <li>weights: weights of each structure in the selected structure set (numpy array)</li> <li>iteration: Counter of the last iteration (int)</li> </ul> <div> </div> <div>The directory "supplentary_figure_correlation_time" contains the data for a plot of the optimized correlation coefficient as a function of simulation time. The results of the optimization algorithms (as pickled python objects) are included in the subdirectories with the associated trajectory length as a name.</div> </div> <p> </p>
Information Flow in the Old Tibetan Annals-I
Open the record for dataset details and reuse information.
Integrating the interconnections between groundwater and land surface processes through the coupled NASA Land Information System and ParFlow environment
<p>This is a dataset used in the paper entitled "Integrating the interconnections between groundwater and land surface processes through the coupled NASA Land Information System and ParFlow environment" by Maina et al., 2024</p>
Dataset of synthetic, maturation-informed magnetic resonance images of the human fetal brain
<p>This dataset gathers synthetic-yet-highly-realistic T2-weighted magnetic resonance images (MRI) of the fetal brain based on the latest development of our prototype Fetal Brain magnetic resonance Acquisition Numerical phantom that now simulates local heterogeneities within white matter tissues throughout maturation (FaBiAN v2.0).<br>This dataset is associated with the following paper:</p> <p><strong>- Lajous H. et al. (2024) A dataset of synthetic, maturation-informed magnetic resonance images of the human fetal brain.</strong> Submitted to Nature Scientific Data, Pre-print available https://doi.org/10.1101/2024.04.08.588566</p> <p>We propose this unique, extensive fetal MRI dataset of simulated standard clinical fast spin echo sequences in both healthy and pathological neurodevelopmental trajectories to address data scarcity in this sensitive population, and therefore support the continuous endeavor of the community to develop advanced post-processing methods as well as cutting-edge artificial intelligence models. Automated brain tissue annotations of the two-dimensional, low-resolution series as well as super-resolution (SR) reconstructions of the fetal brain volumes are also included.</p> <p><strong>Work using any of these data should cite the following references:</strong></p> <ul> <li>Lajous, H. et al. A dataset of synthetic, maturation-informed magnetic resonance images of the human fetal brain. Submitted to Nature Scientific Data (2024), https://doi.org/10.1101/2024.04.08.588566</li> <li>Lajous, H. et al. Dataset of synthetic, maturation-informed magnetic resonance images of the human fetal brain. Zenodo (2024). 10.5281/zenodo.10940427</li> <li>Lajous, H., le Boeuf Fló, A., Esteban, O. & Bach Cuadra, M. Medical-Image-Analysis-Laboratory/FaBiAN: FaBiAN v2.0 (2.0). Zenodo (2023), 10.5281/zenodo.5471094</li> </ul> <p>This work was supported by the Swiss National Science Foundation through grant 182602, and by the ProTechno Foundation. We acknowledge access to the facilities and expertise of the CIBM Center for Biomedical Imaging, a Swiss research center of excellence founded and supported by Lausanne University Hospital (CHUV), University of Lausanne (UNIL), Ecole Polytechnique Fédérale de Lausanne (EPFL), University of Geneva (UNIGE) and Geneva University Hospitals (HUG).</p> <p>Medical Image Analysis Laboratory - Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland & CIBM Center for Biomedical Imaging. 2024.</p> <p>Note: <em>Terms of use for the original cohort (</em>Fidon, L., Aertsen, M., Emam, D., et al. Label-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation. MICCAI, 2021<em>) are for research and education purposes only.</em></p>
Exploiting market state information for index price forecasting
<p>This project encompasses a comprehensive database of stock prices, focusing on the components of Dow Jones and Nasdaq-100 indices. It collates historical price data alongside market state information, capturing the fluctuations and trends of stocks. The database serves as a foundation for analyzing market behaviors, testing financial theories, and developing predictive models for stock price movements. Designed with research and analysis in mind, this dataset provides a valuable resource for financial researchers, economists, and data scientists interested in stock market dynamics, forecasting, and the evaluation of investment strategies.</p> <p> </p> <p>SQL Format </p>
Supporting Information for "Tetrazole-functionalized Organoboranes Exhibiting Dynamic Intramolecular N→B-Coordination and Cyanide-selective Anion Bin"
<p>Abstract: Starting from two different cyano-functionalized organoboranes, we demonstrate that 1,3-dipolar [3+2] azide-nitrile cy-cloaddition can serve to generate libraries of alkyl-tetrazole-functionalized compounds capable of intramolecular NB-Lewis adduct formation. Due to the lower basicity of tetrazoles, structures can be generated that exhibit weak and labile NB-coordination. The reaction furnishes 1- and 2-alkylated regio-isomers that exhibit different effective Lewis-acidities at the boron centers, and vary in their optical absorption and fluorescence properties. Indeed, we identified derivatives capable of selectively binding cyanide over fluoride, as confirmed by 11B NMR. This finding demonstrates the potentialities of this synthetic strategy to systematically finetune the properties of lead structures that are of interest as chemical sensors.</p> <p>Content:</p> <ul> <li>Output filed from Computational Studies</li> <li>Raw data and Analysis from Fluorescence Titrations</li> <li>Crystal data</li> <li>ESI file in .pdf format containing experimental procedures and analytical data.</li> </ul>
Supporting Information for "Tetrazole-functionalized Organoboranes Exhibiting Dynamic Intramolecular N→B-Coordination and Cyanide-selective Anion Bin"
<p>Abstract: Starting from two different cyano-functionalized organoboranes, we demonstrate that 1,3-dipolar [3+2] azide-nitrile cy-cloaddition can serve to generate libraries of alkyl-tetrazole-functionalized compounds capable of intramolecular NB-Lewis adduct formation. Due to the lower basicity of tetrazoles, structures can be generated that exhibit weak and labile NB-coordination. The reaction furnishes 1- and 2-alkylated regio-isomers that exhibit different effective Lewis-acidities at the boron centers, and vary in their optical absorption and fluorescence properties. Indeed, we identified derivatives capable of selectively binding cyanide over fluoride, as confirmed by 11B NMR. This finding demonstrates the potentialities of this synthetic strategy to systematically finetune the properties of lead structures that are of interest as chemical sensors.</p> <p>Content:</p> <ul> <li>Output files from Computational Studies</li> <li>Raw data and Analysis from Fluorescence Titrations</li> <li>Crystal data</li> <li>ESI file in .pdf format containing experimental procedures and analytical data.</li> </ul>
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.