PROSITE consists of documentation entries describing protein domains, families and functional sites as well as associated patterns and profiles to identify them [More... / References / Commercial users ]. PROSITE is complemented by ProRule , a collection of rules based on profiles and patterns, which increases the discriminatory power of profiles and patterns by providing additional information about functionally and/or structurally critical amino acids [More...].

Net change in housing units arising from new buildings, demolitions, or alterations for NYC Census Blocks since 2010. The NYC Department of City Planning's (DCP) Housing Database provide the 2010 census count of housing units, the net change in Class A housing units since the census, and the count of units pending completion for commonly used political and statistical boundaries. These tables are aggregated from the DCP Housing Database, which is derived from Department of Buildings (DOB)-approved housing construction and demolition jobs filed or completed in NYC since January 1, 2010. Net housing unit change is calculated as the sum of all three construction job types that add or remove residential units: new buildings, major alterations, and demolitions, and can be used to determine the change in legal housing units across time and space. All previously released versions of this data are available on the DCP Website: BYTES of the BIG APPLE. Current version: 25q2

Data item of the type family from the database pfam with accession PF00012 and name Hsp70 protein

CATH Domain Classification List (latest release) - protein structural domains classified into CATH hierarchy.

This database has the positional information in the amino acid motif structure. The information is identified by experiments or sequence analysis (homology search and multiple alignment). We use PROSITE and BLOCKS as amino acid motif databases. PROSITE has motives from experiments, and BLOCKS has motives from both experiments and sequence analysis. We use PDB as the protein structure database and decide the motif position from SEQRES and ATOM sequences. The reason we need SEQRES and ATOM sequences is because those are not matched in some PDB entries.

Census Blocks from the 2020 US Census for New York City clipped to the shoreline. These boundary files are derived from the US Census Bureau's TIGER project and have been geographically modified to fit the New York City base map. Because some census blocks are under water not all census blocks are contained in this file, only census blocks that are partially or totally located on land have been mapped in this file. All previously released versions of this data are available on the DCP Website: BYTES of the BIG APPLE. Current version: 25c

Data item of the type domain from the database pfam with accession PF00019 and name Transforming growth factor beta like domain

Data item of the type domain from the database pfam with accession PF00020 and name TNFR/NGFR cysteine-rich region

This is an Allegheny County extract of the 2016 US Census Blocks downloaded from the following website: https://www.census.gov/geo/maps-data/data/tiger-cart-boundary.html

A. SUMMARY Census Block groups are the next level above census blocks in the geographic hierarchy. Block groups are a combination of census blocks that is a subdivision of a census tract.A block group consists of all census blocks whose numbers begin with the same digit in a given census tract; for example, block group 3 includes all census blocks numbered in the 300s. More information on the census tracts can be found here.

B. HOW THE DATASET IS CREATED The boundaries are uploaded from TIGER/Line shapefiles provided by the U.S. Census Bureau.

C. UPDATE PROCESS This dataset is static. Changes to the census blocks are tracked in multiple datasets. See here for 2000 census tract boundaries.

D. HOW TO USE THIS DATASET This boundary file can be joined to other census datasets on GEOID. Column descriptions can be found on in the technical documentation included on the census.gov website

E. RELATED DATASETS Census 2020: Census Tracts for San Francisco Analysis Neighborhoods - 2020 census tracts assigned to neighborhoods Census 2020: Blocks for San Francisco Census 2020: Blocks for San Francisco Clipped to SF Shoreline Census 2020: Blocks Groups for San Francisco Clipped to SF Shoreline

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 15, 2013. Non-coding DNA segments that are conserved across multiple homologous genomic sequences are good indicators of putative regulatory elements. We use a systematic approach to delineate such conserved non-coding blocks from a collection of vertebrate species. Upstream regions of homologous gene pairs from man, rhesus monkey, mouse, rat, dog, cow, chicken, tetraodon, zebrafish and xenopus are considered for this purpose. Pairwise as well as Multiple alignments based on the pairwise ones are available. Sequence conservation in non-coding, upstream regions of orthologous genes from man and mouse is likely to reflect common regulatory DNA sites. Motivated by this assumption we have delineated a catalogue of conserved non-coding sequence blocks and provide the CORG-''COmparative Regulatory Genomics''-database. The data were computed based on statistically significant local suboptimal alignments of 15 kb regions upstream of the translation start sites of, currently, 10 793 pairs of orthologous genes. The resulting conserved non-coding blocks were annotated with EST matches for easier detection of non-coding mRNA and with hits to known transcription factor binding sites. CORG data are accessible from the ENSEMBL web site via a DAS service as well as a specially developed web service for query and interactive visualization of the conserved blocks and their annotation.

Block outlines are one dataset among many that provide information about property. Other related datasets can be found by

filtering on the Property keyword. Data currencyThis data in City systems is updated frequently in the normal course of business, however priorities and resources determine how fast a change in reality is reflected in the database. The extract on this website is updated weekly. Data accuracyMuch of the City's land base is created using survey accuracy however some features are not as precise.

Blocks are typically bounded by streets, roads or creeks. In cities, a census block may correspond to a city block, but in rural areas where there are fewer roads, blocks may be limited by other features. The Census Bureau established blocks covering the entire nation for the first time in 1990.There are less number of Census Blocks within Los Angeles County in 2020 Census TIGER/Line Shapefiles, compared in 2010.Updated:1. June 2023: This update includes 2022 November Santa Clarita City annexation and the addition of "Kinneloa Mesa" community (was a part of unincorporated East Pasadena). Added new data fields FIP_CURRENT to CITYCOMM_CURRENT to reflect new/updated city and communities. Updated city/community names and FIP codes of census blocks that are in 2022 November Santa Clarita City annexation and new Kinneloa Mesa community (look for FIP_Current, City_Current, Comm_Current field values)2. February 2023: Updated few Census Block CSA values based on Demographic Consultant inquiry/suggestions3. April 2022: Updated Census Block data attribute values based on Supervisorial District 2021, Service Planning Area 2022, Health District 2022 and ZIP Code Tabulation Area 2020Created: March 2021How This Data is Created? This census geographic file was downloaded from Census Bureau website: https://www2.census.gov/geo/tiger/TIGER2020PL/STATE/06_CALIFORNIA/06037/ on February 2021 and customized for LA County. New data fields are added in the census blocks 2020 data and populated with city/community names, LA County FIPS, 2021 Supervisorial Districts, 2020 Census Zip Code Tabulation Area (ZCTA) and some administrative boundary information such as 2022 Health Districts and 2022 Service Planning Areas (SPS) are also added. "Housing20" field value and "Pop20" field value is populated with PL 94-171 Redistricting Data Summary File: Decennial Census P.L. 94-171 Redistricting Data Summary Files. Similarly, "Feat_Type" field is added and populated with water, ocean and land values. Five new data fields (FIP_CURRENT to CITYCOMM_CURRENT) are added in June 2023 updates to accommodate 2022 Santa Clarita city annexation. City/community names and FIP codes of census blocks affected by 2022 November Santa Clarita City annexation are assigned based on the location of block centroids. In June 2023 update, total of 36 blocks assigned to the City of Santa Clarita that were in Unincorporated Valencia and Castaic. Note: This data includes 3 NM ocean (FEAT_TYPE field). However, user can use a definition query to remove those. Data Fields: 1. STATE (STATEFP20): State FIP, "06" for California, 2. COUNTY (COUNTYFP20): County FIP "037" for Los Angeles County, 3. CT20: (TRACTCE20): 6-digit census tract number, 4. BG20: 7-digit block group number, 5. CB20 (BLOCKCE20): 4-digit census block number, 6. CTCB20: Combination of CT20 and CB20, 7. FEAT_TYPE: Land use types such as water bodies, ocean (3 NM ocean) or land, 8. FIP20: Los Angeles County FIP code, 9. BGFIP20: Combination of BG20 and FIP20, 10. CITY: Incorporated city name, 11. COMM: Unincorporated area community name and LA City neighborhood, also known as "CSA", 12. CITYCOMM: City/Community name label, 13. ZCTA20: Parcel specific zip codes, 14. HD12: 2012 Health District number, 15. HD_NAME: Health District name, 16. SPA22: 2022 Service Planning Area number, 17. SPA_NAME: Service Planning Area name, 18. SUP21: 2021 Supervisorial District number, 19. SUP_LABEL: Supervisorial District label, 20. POP20: 2020 Population (PL 94-171 Redistricting Data Summary File - Total Population), 21. HOUSING20: 2020 housing (PL 94-171 Redistricting Data Summary File - Total Housing),22. FIP_CURRENT: Los Angeles County 2023 FIP code, as of June 2023,23. BG20FIP_CURRENT: Combination of BG20 and 2023 FIP, as of June 2023,24. CITY_CURRENT: 2023 Incorporated city name, as of June 2023,25. COMM_CURRENT: 2023 Unincorporated area community name and LA City neighborhood, also known as "CSA", as of June 2023,26. CITYCOMM_CURRENT: 2023 City/Community name label, as of June 2023.

Conserved syntenic regions among publicly available cotton genomes were analyzed by CottonGen and made available using the Tripal Synteny Viewer developed by the Fei Bioinformatics Lab from the Boyce Thomson Institute at Cornell University. Analysis was done using MCScanX (Wang et al. 2012) with default settings and blast files were made using blastp with an expectation value cutoff < 1e-10, maximum alignment of 5, and maximum scores of 5. The synteny viewer displays all the conserved syntenic blocks between a selected chromosome of a genome and another genome in a circular and tabular layout. Once a block is chosen in the circular or tabular layout, all the genes in the block are shown in a graphic and tabular format. The gene names have hyperlinks to gene pages where detailed information of the gene can be accessed. The ‘synteny’ section of the gene page displays all the orthologs and the paralogs with link to the corresponding syntenic blocks or gene pages. Resources in this dataset:Resource Title: Website Pointer for Cottongen Synteny Viewer. File Name: Web Page, url: https://www.cottongen.org/synview/search Synteny among Cotton genomes can be viewed using the new Tripal Synteny Viewer. Conserved syntenic regions among publicly available cotton genomes were analyzed by CottonGen and made available using the Tripal Synteny Viewer developed by the Fei Bioinformatics Lab from the Boyce Thomson Institute at Cornell University. Analysis was done using MCScanX (Wang et al. 2012) with default settings and blast files were made using blastp with an expectation value cutoff < 1e-10, maximum alignment of 5, and maximum scores of 5. The synteny viewer dynamically displays all the conserved syntenic blocks between a selected chromosome of a genome and another genome in a circular and tabular layout. Once a block is chosen in the circular or tabular layout, all the genes in the block are shown in a graphic and tabular format. The gene names have hyperlinks to gene pages where detailed information of the gene can be accessed. The ‘synteny’ section of the gene page displays all the orthologs and the paralogs with link to the corresponding syntenic blocks or gene pages.

See where community members and organizations have adopted blocks. Search by address, group name or by using the map controls. Click the block lines to see how has adopted the block.The program helps beautify neighborhoods and allows citizens and families to take an active role in cleaning and greening the District. Adopt-A-Block offers a hands-on project for people and organizations to participate in making a noticeable contribution to their communities.For more information, please visit the Adopt a Block website.

Accessibility Observatory data reflects the number of jobs that are reachable by various modes within different travel times from different Census-defined geographies in Massachusetts (block, block group, tract). The data comes from the Accessibility Observatory at the University of Minnesota, and the underlying jobs data is sourced from the U.S. Census Bureau’s Local Employer Household Dynamics (LEHD) dataset. More information about data methodology is available here: http://access.umn.edu/publications/·The data posted on GeoDOT is initially organized by mode: Auto, Transit, Pedestrian, and Bike. With respect to Auto, Transit, and Pedestrian data, data is then organized by geography (group and block group), and then travel time threshold: 30, 45, and 60 minutes. Please note that MassDOT has access to data that reflects travel time thresholds in five minute increments, email Derek Krevat at derek.krevat@dot.state.ma.us for more information.Data reflecting access to jobs via Auto is available for each hour of the day at the different travel time thresholds (30, 45 and 60 minute thresholds are posted; five minute thresholds are available by contacting Derek Krevat at derek.krevat@dot.state.ma.us). For convenience, MassDOT has also created stand-alone summary files that reflect the total number of jobs available throughout the day within 30, 45, and 60 minutes of travel time. See the Data Dictionary, Auto All Jobs for more information.· Pedestrian and Transit data is only available for the morning peak travel period, 7:00 to 9:00 am.· Bicycle data is only available for the noontime hour.· Each of the data files contains data reflecting access to all jobs as well as discrete job opportunities as categorized by the U.S. Census bureau, such as jobs in specific industries, with specific types of workers, with specific wages, or in businesses of certain sizes or ages. See the Data Dictionary for more information.

A. SUMMARY Census blocks, the smallest geographic area for which the Bureau of the Census collects and tabulates decennial census data, are formed by streets, roads, railroads, streams and other bodies of water, other visible physical and cultural features, and the legal boundaries shown on Census Bureau maps. More information on the census tracts can be found here.

B. HOW THE DATASET IS CREATED The boundaries are uploaded from TIGER/Line shapefiles provided by the U.S. Census Bureau.

C. UPDATE PROCESS This dataset is static. Changes to the census blocks are tracked in multiple datasets. See here for 2000 and 2010 census tract boundaries.

D. HOW TO USE THIS DATASET This boundary file can be joined to other census datasets on GEOID. Column descriptions can be found on in the technical documentation included on the census.gov website

E. RELATED DATASETS Census 2020: Census Tracts for San Francisco Analysis Neighborhoods - 2020 census tracts assigned to neighborhoods Census 2020: Blocks for San Francisco Clipped to SF Shoreline Census 2020: Blocks Groups for San Francisco Census 2020: Blocks Groups for San Francisco Clipped to SF Shoreline

This dataset contains mobile wireless download speed test results and areas where the PSD (Vermont Public Service Department) challenged mobile wireless service asserted by wireless carriers.DOWNLOAD SPEED TEST RESULTSResults from download speed tests that were conducted in September-December 2018 are contained by 6 point feature-classes, each with results for a particular carrier.PSD staff employed the android smartphone application G-NetTrack to conduct download speed tests at approximately 300 meter intervals along all federal-aid highways.The point feature-classes are very detailed and more suitable when zoomed into the neighborhood scale. All point feature-classes have the same field schema, which includes these fields: timestamp: Date and time at which the data point was collected. signal_str: Signal strength (RSRP in dBm). download_s: Download speed (in Mbps). latency: The round-trip time for a request to a website, in milliseconds.DRIVE-TEST BLOCKSDrive-test blocks (Utility_DriveTest_poly_Blocks) is a polygon feature-class that is composed of 1-kilometer blocks; it has a field for each of the 6 carriers; the fields show the average download speed recorded in each block for each carrier.The fields also include a composite field (All_) that contains averages of all carriers, masking variation in coverage between individual carriers. "999" indicates no test was conducted for the carrier in that block.Drive-test blocks are generalized information and are suitable when zoomed at various scales. A BLOCK DOES NOT INDICATE SERVICE THROUGHOUT A BLOCK; use the point feature-classes for detailed data and judge accordingly.WIRELESS CHALLENGE BLOCKSWireless Challenge Blocks (Utility_DriveTest_poly_VTMFCIIChallengeBlocks) depicts the status of each block in the submission of the PSD in the FCC Mobility Fund Phase II Challenge process. It shows challenges to mobile wireless service asserted by wireless carriersA value of 0 in the Area_1 field indicates that the challenge was rejected, either because a) the block is already largely eligible, or b) because no tests below 5 Mbps were submitted.DISCLAIMERVCGI and the State of VT make no representations of any kind, including but not limited to the warranties of merchantability or fitness for a particular use, nor are any such warranties to be implied with respect to the data.