Web-based repository and analysis site for connectivity matrices that have been derived from neuroimaging data including different imaging modalities, subject groups, and studies. Users can analyze connectivity matrices that have been shared publicly and upload their own matrices to share or analyze privately.

Collection of digitized images.Maintained primarily to support research in image processing, image analysis, and machine vision.

Dataset

This dataset was created by Rushikesh Wayal

Contents

A virtual database of annotations made by 50 database providers (April 2014) - and growing (see below), that map data to publication information. All NIF Data Federation sources can be part of this virtual database as long as they indicate the publications that correspond to data records. The format that NIF accepts is the PubMed Identifier, category or type of data that is being linked to, and a data record identifier. A subset of this data is passed to NCBI, as LinkOuts (links at the bottom of PubMed abstracts), however due to NCBI policies the full data records are not currently associated with PubMed records. Database providers can use this mechanism to link to other NCBI databases including gene and protein, however these are not included in the current data set at this time. (To view databases available for linking see, http://www.ncbi.nlm.nih.gov/books/NBK3807/#files.Databases_Available_for_Linking ) The categories that NIF uses have been standardized to the following types: * Resource: Registry * Resource: Software * Reagent: Plasmid * Reagent: Antibodies * Data: Clinical Trials * Data: Gene Expression * Data: Drugs * Data: Taxonomy * Data: Images * Data: Animal Model * Data: Microarray * Data: Brain connectivity * Data: Volumetric observation * Data: Value observation * Data: Activation Foci * Data: Neuronal properties * Data: Neuronal reconstruction * Data: Chemosensory receptor * Data: Electrophysiology * Data: Computational model * Data: Brain anatomy * Data: Gene annotation * Data: Disease annotation * Data: Cell Model * Data: Chemical * Data: Pathways For more information refer to Create a LinkOut file, http://neuinfo.org/nif_components/disco/interoperation.shtm Participating resources ( http://disco.neuinfo.org/webportal/discoLinkoutServiceSummary.do?id=4 ): * Addgene http://www.addgene.org/pgvec1 * Animal Imaging Database http://aidb.crbs.ucsd.edu * Antibody Registry http://www.neuinfo.org/products/antibodyregistry/ * Avian Brain Circuitry Database http://www.behav.org/abcd/abcd.php * BAMS Connectivity http://brancusi.usc.edu/ * Beta Cell Biology Consortium http://www.betacell.org/ * bioDBcore http://biodbcore.org/ * BioGRID http://thebiogrid.org/ * BioNumbers http://bionumbers.hms.harvard.edu/ * Brain Architecture Management System http://brancusi.usc.edu/bkms/ * Brede Database http://hendrix.imm.dtu.dk/services/jerne/brede/ * Cell Centered Database http://ccdb.ucsd.edu * CellML Model Repository http://www.cellml.org/models * CHEBI http://www.ebi.ac.uk/chebi/ * Clinical Trials Network (CTN) Data Share http://www.ctndatashare.org/ * Comparative Toxicogenomics Database http://ctdbase.org/ * Coriell Cell Repositories http://ccr.coriell.org/ * CRCNS - Collaborative Research in Computational Neuroscience - Data sharing http://crcns.org * Drug Related Gene Database https://confluence.crbs.ucsd.edu/display/NIF/DRG * DrugBank http://www.drugbank.ca/ * FLYBASE http://flybase.org/ * Gene Expression Omnibus http://www.ncbi.nlm.nih.gov/geo/ * Gene Ontology Tools http://www.geneontology.org/GO.tools.shtml * Gene Weaver http://www.GeneWeaver.org * GeneDB http://www.genedb.org/Homepage * Glomerular Activity Response Archive http://gara.bio.uci.edu * GO http://www.geneontology.org/ * Internet Brain Volume Database http://www.cma.mgh.harvard.edu/ibvd/ * ModelDB http://senselab.med.yale.edu/modeldb/ * Mouse Genome Informatics Transgenes ftp://ftp.informatics.jax.org/pub/reports/MGI_PhenotypicAllele.rpt * NCBI Taxonomy Browser http://www.ncbi.nlm.nih.gov/Taxonomy/taxonomyhome.html * NeuroMorpho.Org http://neuromorpho.org/neuroMorpho * NeuronDB http://senselab.med.yale.edu/neurondb * SciCrunch Registry http://neuinfo.org/nif/nifgwt.html?tab=registry * NIF Registry Automated Crawl Data http://lucene1.neuinfo.org/nif_resource/current/ * NITRC http://www.nitrc.org/ * Nuclear Receptor Signaling Atlas http://www.nursa.org * Olfactory Receptor DataBase http://senselab.med.yale.edu/ordb/ * OMIM http://omim.org * OpenfMRI http://openfmri.org * PeptideAtlas http://www.peptideatlas.org * RGD http://rgd.mcw.edu * SFARI Gene: AutDB https://gene.sfari.org/autdb/Welcome.do * SumsDB http://sumsdb.wustl.edu/sums/ * Temporal-Lobe: Hippocampal - Parahippocampal Neuroanatomy of the Rat http://www.temporal-lobe.com/ * The Cell: An Image Library http://www.cellimagelibrary.org/ * Visiome Platform http://platform.visiome.neuroinf.jp/ * WormBase http://www.wormbase.org * YPED http://medicine.yale.edu/keck/nida/yped.aspx * ZFIN http://zfin.org

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 4, 2023. BODB offers a way to document computational models of brain function by linking each model to Brain Operating Principles (BOPs), related brain regions, Summaries of Simulation Results (SSRs)and Summaries of Experimental Data (SEDs) used either to design or to test the model. Tools are provided to search for related models and to compare their coverage of SEDs. This allows automatic benchmarking of a model against a cluster of models addressing similar BOPs or SEDs or brain regions. Tools allow display of brain imaging results against a human brain applet; a new tool will link data to a macaque brain applet.

This data set represents 8 USC Section 1367 protected data that is flagged in CIS2 when Class of Admission (COA codes identified as requiring Section 1367 protections is added to a CIS2 record and persisted in the CIS2 database. Historical 8 USC Section 1367 data migrated from CIS mainframe to CIS2 on 12/16/2018 is also available in the CIS2 database.

Context

Sample images for imaging experiments.

Copyright

Copyright is complicated, see here:

http://sipi.usc.edu/database/copyright.php

Content

Images kept in USC-SIPI Image Database

Acknowledgements

http://sipi.usc.edu/database/database.php

Inspiration

Image Processing algorithms created the need for sample images. They are kind of "vintage" now, but can still be used for interesting projects.

The loan-level Public Use Databases (PUDBs) are released annually to meet FHFA’s requirement under 12 U.S.C. 4543 and 4546(d) to publicly disclose data about the Enterprises’ single-family and multifamily mortgage acquisitions.

Seaweeds are produced for food and as industrial products throughout the Pacific and many communities rely on this production for significant portions of their income. This industry is diverse in the types of seaweeds produced, whether they are cultured or harvested from the coastline, the way that they are processed and in the final use of the seaweeds. However, the biochemical charactertistcs of seaweed across the Pacific region, and the corresponding range of product opportunities, is not well understood. The aim of this research was to sample and characterise the biochemical composition of key species of seaweed from three countries (Fiji, Samoa and Kiribati). More than 1000 individual data entries on the biochemical properties of seaweed were made including analysis of fibre, protein, lipid, ash (minerals) and moisture content. The following product sheets were created for different species in different countries: Kappaphycus (Fiji, Kiribati), Caulerpa (Fiji, Samoa), Hypnea (Fiji), Gracilaria/Hydropuntia (Fiji), Ulva (Fiji, Kiribati), Acanthophora (Kiribati), Tomatoes (grown with Acanthophora seaweed compost, Kiribati).

KAV 8015 cover memo. Visit https://dataone.org/datasets/sha256%3A7024246b1fb8cc48a99ee076baa2cd27ebca81ee749c0e2b857c4e9ebdab811e for complete metadata about this dataset.

The UC ClioMetric History Project is digitizing decades of administrative records from the University of California and other schools in the state (such as USC and Stanford). So far, they’ve uploaded data on more than 750,000 student enrollments, tens of thousands of faculty members, and 800,000 courses.

Bleemer, Zachary. 2018. "The UC ClioMetric History Project and Formatted Optical Character Recognition". CSHE Research and Occasional Papers Series 3.18.

Every Federal agency that operates or directs one or more Federal laboratories or that conducts research and development is required to prepare and submit an annual report of its technology transfer activities as described in 15 U.S.C. § 3710(f).

KAV 4699 cover memo

The Public Use Database (PUDB) is released annually to meet FHFA’s requirement under 12 U.S.C. 4543 and 4546(d) to publicly disclose data about the Enterprises’ single-family and multifamily mortgage acquisitions. The datasets supply mortgage lenders, planners, researchers, policymakers, and housing advocates with information concerning the flow of mortgage credit in America’s neighborhoods. Beginning with data for mortgages acquired in 2018, FHFA has ordered that the PUDB be expanded to include additional data that is the same as the data definitions used by the regulations implementing the Home Mortgage Disclosure Act, as required by 12 U.S.C. 4543(a)(2) and 4546(d)(1).The PUDB single-family datasets include loan-level records that include data elements on the income, race, and sex of each borrower as well as the census tract location of the property, loan-to-value (LTV) ratio, age of mortgage note, and affordability of the mortgage. New for 2018 are the inclusion of the borrower’s debt-to-income (DTI) ratio and detailed LTV ratio data at the census tract level. The PUDB multifamily property-level datasets include information on the unpaid principal balance and type of seller/servicer from which the Enterprise acquired the mortgage. New for 2018 is the inclusion of property size data at the census tract level. The multifamily unit-class files also include information on the number and affordability of the units in the property. Both the single-family and multifamily datasets include indicators of whether the purchases are from “underserved” census tracts, as defined in terms of median income and minority percentage of population.Prior to 2010 the single-family PUDB consisted of three files: Census Tract, National A, and National B files. With the 2010 PUDB a fourth file, National C, was added to provide information on high-cost mortgages acquired by the Enterprises. The single-family Census Tract file includes information on the location of the property based on the 2010 Census for acquisition years 2012 through 2021, and the 2020 Census beginning with the 2022 acquisition year. The National files contain other information but lack detailed geographic information in order to protect Enterprise proprietary data. The multifamily datasets also consist of a Census Tract file, and a National file without detailed geographic information.Several dashboards are available to analyze the data:Enterprise Multifamily Public Use Database DashboardThe Enterprise Multifamily Public Use Database (PUDB) Dashboard provides users an interactive way to generate and visualize Enterprise PUDB data of multifamily mortgage acquisitions by Fannie Mae and Freddie Mac. It shows characteristics about multifamily loans, properties and units at the national level, and characteristics about multifamily loans and properties at the state level. It includes key statistics, time series charts, and state maps of multifamily housing characteristics such as median loan amount, number of properties, average number of units per property, and unit affordability. The underlying aggregate statistics presented in the dashboard come from three multifamily data files in the Enterprise PUDB, updated annually since 2008, including two property-level datasets and a data file on the size and affordability of individual units.Enterprise Multifamily Public Use DashboardPress Release - FHFA Releases Data Visualization Dashboard for Enterprises’ Multifamily Mortgage AcquisitionsMortgage Loan and Natural Disaster DashboardFHFA published an interactive Mortgage Loan and Natural Disaster Dashboard that combines FHFA’s PUDB reports on single-family and multifamily acquisitions for the regulated entities, FEMA’s National Risk Index (NRI), and FHFA’s Duty to Serve 2023 High-Needs rural areas. Desired geographies can be exported to .pdf and Excel from the Public Use Database and National Risk Index Dashboard.Mortgage Loan and Natural Disaster DashboardMortgage Loan and Natural Disaster Dashboard FAQs

TIAS 17-920.4 cover memo. Visit https://dataone.org/datasets/sha256%3A0fd5f6c7017fed3fe2c912b96119a49a2b83e61f530ab67c948bc1a1e1c62380 for complete metadata about this dataset.

KAV 6992 First signed 04/22/1998 Last signed 06/09/1998 Entry into force (supplemented by last signed) 06/09/1998 stamped 04-708 C06550472 cover memo

The USC_IEMOCAP database contains audio, transcriptions, video, and motion-capture(MoCap) recordings of dyadic mixed-gender pairs of actors. There are five sessions in the database (ten actors total). For complete information about IEMOCAP please refer to:

Busso, Carlos et al. "IEMOCAP: Interactive emotional dyadic motion capture database." Journal of Language Resources and Evaluation. Volume 42, Number 4. pp. 335-359. 2008

as well as our website: http://sail.usc.edu/iemocap/

Presented here is lighter version of the IEMOCAP data

The Interactive Emotional Dyadic Motion Capture (IEMOCAP) database is an acted, multimodal and multispeaker database, recently collected at SAIL lab at USC. It contains approximately 12 hours of audiovisual data, including video, speech, motion capture of face, text transcriptions. It consists of dyadic sessions where actors perform improvisations or scripted scenarios, specifically selected to elicit emotional expressions. IEMOCAP database is annotated by multiple annotators into categorical labels, such as anger, happiness, sadness, neutrality, as well as dimensional labels such as valence, activation and dominance. The detailed motion capture information, the interactive setting to elicit authentic emotions, and the size of the database make this corpus a valuable addition to the existing databases in the community for the study and modeling of multimodal and expressive human communication.

TIAS 04-819.1 cover memo

TIAS 11-505 cover memo. Visit https://dataone.org/datasets/sha256%3Ad289e90fc9d36e2afae0c31f5be6f1daa24a890f4cbd27689580aa954c6d7d8c for complete metadata about this dataset.