The Census Bureau does not recognize or release data for Boston neighborhoods. However, Census block groups can be aggregated to approximate Boston neighborhood boundaries to allow for reporting and visualization of Census data at the neighborhood level. Census block groups are created by the U.S. Census Bureau as statistical geographic subdivisions of a census tract defined for the tabulation and presentation of data from the decennial census and the American Community Survey. The 2020 Census block group boundary files for Boston can be found here. These block group-approximated neighborhood boundaries are used for work with Census data. Work that does not rely on Census data generally uses the Boston neighborhood boundaries found here.

This layer presents the census block groups of the United States in the 50 states, the District of Columbia, and Puerto Rico. It provides detailed boundaries that are consistent with the tract, county, and state data sets and are effective at regional and state levels. This layer can be used for visualization and spatial analysis. This layer will be updated on an annual basis with the latest available data from TomTom.Source Data: access the source data for this layer to use or publish and share.USA Block Groups Layer Package

Block Groups:Block groups are statistical subdivisions of census tracts and are the smallest geographic units for which the Census Bureau tabulates sample data. They are designed to cover contiguous areas and are uniquely numbered within each census tract. Block groups do not cross state, county, or census tract boundaries but may cross other geographic entity boundaries.This feature class is used for various purposes, including visualization and analysis of demographic data, urban planning, and resource allocation. It is available for public use and can be accessed through platforms like ArcGIS.Population Range: Each block group generally contains between 600 to 3,000 people.Data Fields:BG20 (BLKGRPCE20): 7-digit census tract and block group number.CT20 (TRACTCE20): 6-digit census tract number.Label (NAMELSAD20): Block group number label.ACS Data:The 2022 American Community Survey (ACS) Block Group Data tables offer detailed estimates on various social, economic, housing, and demographic characteristics at the block group level, which are small statistical divisions of census tracts.The table provides the most comprehensive estimates on all topics for City of Salinas, including block groups. They include detailed information on population, housing, economic, and social characteristics.Selected ACS Fields:Median Age (b01002e1)Population (b01003e1)Households (b11001e1)Households with 200% Federal Poverty Level (c17002e8)Median Household Income (b19301e1)Per Capita Income (b19301e1)Housing Units (b25001e1)Average Household Size (b25010e1)Bachelor's Degree or Higher (b99152e2)High School Degree or Higher (b15003e17)Limited English Households (c16002e1)

These data map life expectancy from birth at the census tract level. This data is repeated to block groups from each parent tract value. Provided by the CDC National Center for Health Statistics (NCHS). For more information, you can visit https://www.cdc.gov/nchs/data-visualization/life-expectancy/.

This hosted feature layer contains Census block group population retrieved from the U.S. Census Bureau's American FactFinder website. More information on the data source can be found on the U.S. Census Bureau website. The data contains Denton County population at the block group level from 2011, and 2013-2017. The 2011 population estimates are from the decennial census. The 2013-2017 population estimates are from the 5-year American Community Survey. The data is in long form for time series visualization purposes.

The stimulator of interferon genes (STING) is a vital protein to the immune surveillance of the tumor microenvironment. In this study, we develop novel inhibitor-based radioligands and evaluate their feasibility for noninvasive visualization of STING expression in tumor-bearing mice. Analogous compounds to STING inhibitors C170 and C176 were synthesized and labeled with 131I and 18F to attain [131I]I-NFIP and [18F]F-NFEP, respectively. The radiosynthesis was achieved with high radiochemical purity (>95%) and molar activity (28.56–48.89 GBq/μmol). The affinity and specificity of tracers were assessed through cell uptake and docking experiments, demonstrating that [131I]I-NFIP exhibited high specificity for STING, with a cell-based IC50 value of 7.56 nM. Small-animal PET/SPECT imaging and biodistribution studies in tumor-bearing mice models were performed to verify the tracers’ pharmacokinetics and tumor-targeting capabilities (n = 3/group). SPECT imaging demonstrated that [131I]I-NFIP rapidly accumulated in the Panc02 tumor quickly at 30 min post-injection, with a tumor-to-muscle (T/M) ratio of 2.03 ± 0.30. This ratio significantly decreased in the blocking group (1.10 ± 0.14, **P < 0.01, n = 3). Furthermore, tumor uptake and the T/M ratio of [131I]I-NFIP were positively associated with STING expression. In summary, [131I]I-NFIP is the first STING-specific inhibitor-based radioligand offering the potential for visualizing STING status in tumors.

Source: Snapshot visualization of the percentage of travel by census block that is non-automotive, disaggregated from ACS block group level estimates.

Purpose: Tile layer utilized for visualization.

Contact Information: Charles Rudder (crudder@citiesthatwork.com)/ Alex Bell (abell@citiesthatwork.com)

We present an economical imaging system with integrated hardware and software to capture multispectral images of Lepidoptera with high efficiency. This method facilitates the comparison of colors and shapes among species at fine and broad taxonomic scales and may be adapted for other insect orders with greater three-dimensionality. Our system can image both the dorsal and ventral sides of pinned specimens. Together with our processing pipeline, the descriptive data can be used to systematically investigate multispectral colors and shapes based on full-wing reconstruction and a universally applicable ground plan that objectively quantifies wing patterns for species with different wing shapes (including tails) and venation systems. Basic morphological measurements, such as body length, thorax width, and antenna size are automatically generated. This system can increase exponentially the amount and quality of trait data extracted from museum specimens. Methods Processed data These data include but are not limited to all parameters generated during image processing, gridded multispectral reflectance, wing shapes, and the measurements of body size and antennae. The detailed data structure can be found on the GitHub repository. Map of archived materials, protocols, and tutorials To prevent potential conflicts, scripts for different purposes on the cluster and on the local machine are provided in different protocols on Protocols.io and repositories on GitHub. Here, the summary of online protocols and source codes are organized as follows. Inclusion in [Protocol] indicates the corresponding step-by-step instruction on Protocols.io; inclusion in [Cluster] indicates the script will run better on the cluster; inclusion in [Local] indicates the script is designed for local machines with relatively low CPU and memory demands. ● Raw data: files described in the following format [[Folder/File name]]: descriptions ● [[Methodology_imaging_records.csv]]: A file recording image names and the barcode of imaged specimens ● [[Drawer_img_nef]]: Drawer images in RAW (.NEF) format (total 35 images) o Five set of images: Method_1-1_dorsal, Method_1-1_ventral, Method_1-2_dorsal, Method_1-2_ventral, Method_1-r_ventral (with a scale bar placed upside-down) ● [[Drawer_img_tiff]]: Drawer images in linearized 16-bit (.tiff) format (total 35 images) ● [[manual_bounding_box_par]]: Manually corrected bounding boxes ● [[spp_img_inspection]]: Specimen images for visualization (.jpg) o [[Problematic]]: Those problematic ones that need to be manually corrected ● [[spp_img_reMask_tiff_done]]: Specimen images (.tiff) after the mask correction ● [[spp_first_level_product]]: The initial descriptive data or ‘first-level products’ (AllBandsMask.mat). Find Methods for the detailed data structure ● [[spp_RGB_Imgs]]: Images used for manual fore-and hindwing segmentation o [[Seg_done]]: Done images (.jpg) o [[Segmented]]: The fore-and hindwing segmentation parameters (.json) ● [[spp_segmentation_analysis]]: Segmented images after inspection and manual correction o [[wing_segmentation_img]]: The visualizations of image segmentation (.jpg) o [[wing_shape_morph-seg]]: The results of image segmentation (morph-seg.mat) o [[morphology_analysis_spp_preference_table_template.csv]]: A table generated according to the images in the ‘wing_segmentation_img’ folder, which is later used for inspection o [[morphology_analysis_spp_preference_table.csv]]: The result after manual inspection, which records the condition of different body parts of a specimen o [[reflectance_table]]: The reflectance data for all body parts of all specimens ● [[spp_wing_grids_generation]]: Generate wing grids and processed data o [[inspect_imgs]]: The visualization (.jpg) of wing grids (no correction was needed in these results) o [[spp_wing_parameters]]: Processed wing data. The original folder name is kept here. o [[wing_matrix_visualization]]: The summarized multispectral reflectance (NIR [740], fNIR [940], F, FinRGB, PolDiff, UV, UVF, white, whitePol1, whitePol2) according to wing grids. ● [[spp_second-level_product]]: The processed “second-level products”. (_d-v_gridsPars.mat). Find Methods for the detailed data structure ● [[group_summary]]: The summary statistics for specified groups o [[specimen_groups.csv]]: A table specifying groups o [[specimen_groups_group_barcode_list.json]]: The group table in JSON format o [[summary_matrices]]: The summary results according to the group table (._summary.mat) o [[summary_visualization]]: The summary visualization for each group (.png) o [[shp_tail_adv_vis]]: Replot wing shape and tails by scripts for advanced visualization (.png) o [[tail_summary_visualization]]: Replot tails by scripts for advanced visualization (.png)

● Blueprints and materials (Fig. 6) [Protocol] https://www.protocols.io/private/2E2FB268F7AF11EBB05F0A58A9FEAC02

● Bash scripts and shell scripts running on the cluster [Cluster] https://github.com/weipingchan/Bash_scripts_methodology_paper

● Image preprocessing to derive initial descriptive data for museum archiving [Protocol] https://www.protocols.io/private/DEF29A74E44E11EB96DA0A58A9FEAC02 [Cluster] https://github.com/weipingchan/single_img_processing ● Inspection and manual correction of specimen bounding box (Fig. 3d) [Local] https://github.com/weipingchan/Drawer_img_manual_define_bounding_boxes ● Inspection and manual correction of mask for background removal (Fig. 9a) [Local] commercial painting software, such as Adobe Photoshop

● Data preparation and processing for color and shape quantification [Protocol] https://www.protocols.io/private/DEF29A74E44E11EB96DA0A58A9FEAC02 ● Body-part segmentation (Fig. 9c panels at right) manually defined fore-hindwing segmentation data [Local] https://github.com/weipingchan/body-seg_distribute Segmentation [Cluster] https://github.com/weipingchan/basic_segmentation Inspection and manual correction of primary landmarks (Fig. 9b) [Local] https://github.com/weipingchan/manual_landmark_correction ● Multispectral reflectance at wing-size level (as table format; Fig. 9d) [Protocol] https://www.protocols.io/private/F3292DF1FE0211EB878B0A58A9FEAC02 [Cluster] https://github.com/weipingchan/multispectral_reflectance_wing-size_level ● Dorsal-ventral side analyses (Fig. 4) [Protocol] https://www.protocols.io/private/F3292DF1FE0211EB878B0A58A9FEAC02 [Cluster] https://github.com/weipingchan/dorsal_ventral_analysis ● Inspection and manual correction of secondary landmarks (Fig. 1d) [Local] https://github.com/weipingchan/manual_wing_grid_correction

● Visualization (Fig. 1g-h & Fig.5) [Protocol] https://www.protocols.io/private/F3292DF1FE0211EB878B0A58A9FEAC02 ● Multispectral reflectance at wing-pattern level with wing shape summary [Local] https://github.com/weipingchan/dorsal_ventral_summary ● Advanced visualization for wing shapes and tails (Methods) [Local] https://github.com/weipingchan/replot_tail_and_avg_shapes

Context

Counter-Strike Global Offensive is a game released in 2012, as a sequel to Counter-Strike Source (released in 2004), which is itself a sequel to the original Counter-Strike (released in 2000). The game's longevity is primarily caused by its competitive approach and vibrant professional scene. This longevity has shown in numbers recently, as CS:GO reached in March its all-time high concurrent players (1.1M players), making it the most played game on Steam, 7 years after it was launched. So, I thought it would be interesting to celebrate this milestone by grouping relevant data about the game and seeing what insights people can get from it!

Content

The data is split into 4 tables that store data related to: - Results.csv: map scores and team rankings - Picks.csv: order of map picks and vetoes in the map selection process. - Economy.csv: round start equipment value for all rounds played - Players.csv: individual performances of players on each map.

Values stored in 'event_id' and 'match_id' columns are unique for each match and event and shared between tables, so these columns can be used as keys to merge data between tables.

It is necessary to note that the rows in the 'results' and 'economy' tables store data for each map played in a match, while the rows in 'picks' and 'players' table store data for the entire match.

The dates of the matches range from 11/2015 to 03/2020. If you think the data is useful, I might update the dataset with data from 2014 and 2015.

Acknowledgements

This data was scraped off https://www.hltv.org/

The Game

Counter-strike is a FPS (First-Person Shooter) game in which two teams of 5 players face each other in a matchup. The game retains the same gameplay concepts since its first version, which include a Terrorist side (T) that is tasked to plant a bomb and have it detonate, and a Counter-Terrorist side (CT) that is tasked to defuse the bomb or prevent it from being planted. Both teams can also win a round by eliminating all players on the opposing team before the bomb is planted.

A standard game of Counter-Strike is a best of 30 rounds, the winning team being the first to win 16 rounds. The 30 rounds are played in two halves of 15 on each side of the map, with a round time limit of 1 minute 55 seconds, plus 40 seconds after the bomb is planted.

In case both teams draw at the 30th round on 15x15, 6 more rounds are added-on, which constitutes overtime. The overtime ends if a team wins 4 out of 6 rounds. If both teams win 3 rounds in overtime, another overtime of 6 rounds is played, and the process might repeat indefinitely until one team wins it.

There are 7 maps in the map pool that are available to be played competitively at any given time. Maps are removed and added frequently for updates and revamps, as to not make the game stale. Matches are normally played as a 'bo3' (Best of 3) maps, with less important matches played in a 'bo1' fashion and finals often played as 'bo5's.

Counter-strike has an economic system that governs the acquisitions of armor, weapons and grenades by the players. Winning a round award the players with $3250 while losing a round after a winning streak gives them $1400. Losing many times in a short period increases the losing bonus by $500 for every additional loss, as to not penalize the losing team too much. Players can also win money by getting kills and planting or defusing the bomb.

The match in the link https://www.youtube.com/watch?v=EkJu4laFGTs elucidates all of these concepts. It is also one of my all-time favorite matches (even though I was not rooting for any of the teams), so I decided to include it here.

Inspiration

What insights can you create from this data? Can you predict map picks? Map winners? Combine those predictions and predict match winners? Give it a try!

Database of three-dimensional structures of macromolecules that allows the user to retrieve structures for specific molecule types as well as structures for genes and proteins of interest. Three main databases comprise Structure-The Molecular Modeling Database; Conserved Domains and Protein Classification; and the BioSystems Database. Structure also links to the PubChem databases to connect biological activity data to the macromolecular structures. Users can locate structural templates for proteins and interactively view structures and sequence data to closely examine sequence-structure relationships. * Macromolecular structures: The three-dimensional structures of biomolecules provide a wealth of information on their biological function and evolutionary relationships. The Molecular Modeling Database (MMDB), as part of the Entrez system, facilitates access to structure data by connecting them with associated literature, protein and nucleic acid sequences, chemicals, biomolecular interactions, and more. It is possible, for example, to find 3D structures for homologs of a protein of interest by following the Related Structure link in an Entrez Protein sequence record. * Conserved domains and protein classification: Conserved domains are functional units within a protein that act as building blocks in molecular evolution and recombine in various arrangements to make proteins with different functions. The Conserved Domain Database (CDD) brings together several collections of multiple sequence alignments representing conserved domains, in addition to NCBI-curated domains that use 3D-structure information explicitly to define domain boundaries and provide insights into sequence/structure/function relationships. * Small molecules and their biological activity: The PubChem project provides information on the biological activities of small molecules and is a component of NIH''''s Molecular Libraries Roadmap Initiative. PubChem includes three databases: PCSubstance, PCBioAssay, and PCCompound. The PubChem data are linked to other data types (illustrated example) in the Entrez system, making it possible, for example, to retrieve information about a compound and then Link to its biological activity data, retrieve 3D protein structures bound to the compound and interactively view their active sites, and find biosystems that include the compound as a component. * Biological Systems: A biosystem, or biological system, is a group of molecules that interact directly or indirectly, where the grouping is relevant to the characterization of living matter. The NCBI BioSystems Database provides centralized access to biological pathways from several source databases and connects the biosystem records with associated literature, molecular, and chemical data throughout the Entrez system. BioSystem records list and categorize components (illustrated example), such as the genes, proteins, and small molecules involved in a biological system. The companion FLink icon FLink tool, in turn, allows you to input a list of proteins, genes, or small molecules and retrieve a ranked list of biosystems.

Context

For those who are actively looking for data scientist jobs in the U.S., the best news this month is the LinkedIn Workforce Report August 2018. According to the report, there is a shortage of 151,717 people with data science skills, with particularly acute shortages in New York City, San Francisco Bay Area and Los Angeles.

To help job hunters (including me) to better understand the job market, I scraped Indeed website and collected information of 7,000 data scientist jobs around the U.S. on August 3rd. The information that I collected are: Company Name, Position Name, Location, Job Description, and Number of Reviews of the Company.

Content

• alldata.csv If you want to explore the job market around the U.S., download this one because it aggregates all information and cleans the job description by removing the tags.
• all other files. If you want to explore specific city or region, you can download any of them.

Acknowledgements

Special thanks to Indeed for not blocking me : )

Inspiration

• If you have no clue of where to start, check my blog for inspiration.
• Link to my PowerPoint Slides for Presentation.
• Link to my GitHub Code.
• Reach me at sl6149@nyu.edu

Possible Questions:

1. Who gets hired? What kind of talent do employers want when they are hiring a data scientist?
2. Which location has the most opportunities?
3. What skills, tools, degrees or majors do employers want the most for data scientists?
4. What's the difference between data scientist, data engineer and data analyst?
5. Can you develop an efficient classification algorithm to differentiate the three job types above?

This layer is being made accessible on this platform as part of a larger collaborative project under development by Arizona Water Company, University of Arizona Water Resources Research Center, Babbitt Center for Land and Water Policy, and Center for Geospatial Solutions. This visualization expresses 2021 U.S. Census block data within the Active Management Areas in Arizona. These shapefiles were altered for visualization purposes.The main sources of data present in this feature layer were taken from the following locations:U.S. Census Tiger/Line Shapefiles (2021)2021 TIGER/Line® Shapefiles (census.gov)The University of Arizona (2008)https://repository.arizona.edu/handle/10150/188734

This layer is being made accessible on this platform as part of a larger collaborative project under development by Arizona Water Company, University of Arizona Water Resources Research Center, Babbitt Center for Land and Water Policy, and Center for Geospatial Solutions. This visualization expresses 2021 U.S. Census block data within the Active Management Areas in Arizona. These shapefiles were altered for visualization purposes.The main sources of data present in this feature layer were taken from the following locations:U.S. Census Tiger/Line Shapefiles (2021)2021 TIGER/Line® Shapefiles (census.gov)The University of Arizona (2008)https://repository.arizona.edu/handle/10150/188734

This layer is being made accessible on this platform as part of a larger collaborative project under development by Arizona Water Company, University of Arizona Water Resources Research Center, Babbitt Center for Land and Water Policy, and Center for Geospatial Solutions. This visualization for Pinal County expresses 2021 U.S. Census block, tract, and places data and the boundaries of the Active Management Areas and Pinal County within Arizona. All of these shapefiles have been altered for visualization purposes.The main sources of data present in this feature layer were taken from the following locations:U.S. Census Tiger/Line Shapefiles (2021)2021 TIGER/Line® Shapefiles (census.gov)The University of Arizona (2008)https://repository.arizona.edu/handle/10150/188734Arizona Department of Water Resources GIS Data (2021)https://gisdata2016-11-18t150447874z-azwater.opendata.arcgis.com/datasets/azwater::ama-and-ina-1/explore?location=34.158174%2C-111.970823%2C7.24These shapefiles were altered.