Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
63
datasets available to search
ShareScore release 0.9.0
Dataset results
63 results for “New York City”
Air Quality and Exposure Disparity Results for the Bronx, New York City
<p>This is the dataset accompanying the publication "Big Mobility Data Reveals Hyperlocal Air Pollution Exposure Disparities in the Bronx, New York". It contains mainly three parts: 1. day-to-day air quality prediction maps for exposure estimation; 2. street-level PM2.5 exposure and its disparity modeling results for all populations and for socio-demographic groups; 3. residence- and mobility-based exposure calculation for a sample of Bronx residents.</p>
Block-group level mode choice parameters for New York City and New York State
<p>We provide two datasets of census block group-level mode choice parameters for New York City and New York State. The parameters are estimated by GLAM logit model using Replica's synthetic population datasets (For details of the GLAM logit model, please refer to <a href="https://github.com/BUILTNYU/GLAM-Logit">BUILTNYU/GLAM-Logit (github.com)</a>). Each row contains a set of mode choice parameters for each block-group OD pair and one of the four population segments (low-income, not low-income, students, and senior population). Six trip modes are considered: private auto, public transit (such as buses, light rail, and subways), on demand auto (taxi or TNC services such as Uber or Lyft), biking (including e-bike), walking, and carpool. Parameters of twelve mode attributes are estimated, including, auto travel time, transit in-vehicle time, transit access time, transit egress time, number of transit transfers, non-vehicle travel time, trip cost, and five alternative specific constants (setting carpool as the reference level).</p> <p>In New York City, the average value of time (VOT) of low-income population is 21.67$/hour, the average VOT of not low-income population is 28.05$/hour, the average VOT of student population is 10.96$/hour, and the average VOT of senior population is 10.93$/hour. In New York State, the average value of time (VOT) of low-income population is 9.63$/hour, the average VOT of not low-income population is 13.95$/hour, the average VOT of student population is 7.40$/hour, and the average VOT of senior population is 6.26$/hour. </p> <p>The empirical distribution of agent-level parameters is neither Gumbel nor Gaussian, which reveals a regional divergence of the value of time and mode preference, indicating potential inequity issues in the transportation system. This is infeasible for conventional discrete choice models (DCMs) to capture. </p>
Block-group level predicted mode share for New York City and New York State
<p>We provide two datasets of predicted mode share, one for New York City and another for New York State. Each row contains the mode proportion of trips along a census block group-level OD pair made by one of the four population segments: low-income, not low-income, students, and senior population. Six trip modes are considered: private auto, public transit (such as buses, light rail, and subways), on demand auto (taxi or TNC services such as Uber or Lyft), biking (including e-bike), walking, and carpool.</p> <p>The prediction is based on GLAM logit model calibrated with Replica's statewide synthetic population dataset. The in-sample prediction accuracy is quite competitive, with an overall accuracy of 90.28% in New York State and 88.63% in New York City. For more details of the model, please refer to our Github repository: <a href="https://github.com/BUILTNYU/GLAM-Logit">BUILTNYU/GLAM-Logit (github.com)</a></p>
2021 New York City Daily Residential Parcel Volume and Stops
<p>These datasets give <strong>2021 NYC residential parcel volume and stops </strong>information. The four datasets represent the estimated volume and stops served by Amazon, FedEx, UPS, and USPS respectively.</p> <p>The dataset is generated by using the Pluto 2021, 2020 census, 2020 USPS postal diary, and the market share among the four companies in 2021. In total,1.92 million daily residential parcels are estimated in the whole NYC area. Each row represents a unique stop. It contains the census tract ID the stop belongs to, its coordinates, and the delivery (FTA) and pickup (FTP) volume. We assume that no pickup volume is assigned to Amazon and USPS due to their service characteristics. In the case of USPS, no direct pickup service will be provided. Instead, the parcel will be directly handed to post offices for pickup. The pickup volume proportional to Amazon's market share is evenly distributed to FedEx and UPS pickup services.</p> <p>"myGraph.pickle": pickle file storing the NYC OSM network graph.</p> <p>"Shapfile.zip": Shapefiles containing different geographic features of NYC.</p> <p>"Centroid_OSM.zip": OSM maps storing the centroid of NYC census tracts.</p> <p>"NTA_dist.zip": VKT result based on NTA. </p> <p>"Service Zone": facility locations represented by OSM node ID and the service areas defined by census tracts</p>
Socioeconomic disparities in subway use and COVID-19 outcomes in New York City
<p>Using data from New York City, we found that there was an estimated 28-day lag between the onset of reduced subway use and the end of the exponential growth period of SARS-CoV-2 within New York City boroughs. We also conducted a cross-sectional analysis of the associations between human mobility (i.e., subway ridership), sociodemographic factors, and COVID-19 incidence as of April 26, 2020. Areas with lower median income, a greater percentage of individuals who identify as non-white and/or Hispanic/Latino, a greater percentage of essential workers, and a greater percentage of healthcare essential workers had greater mobility during the pandemic. When adjusted for the percent of essential workers, these associations do not remain, suggesting essential work drives human movement in these areas. Increased mobility and all sociodemographic variables (except percent older than 75 years old and percent of healthcare essential workers) was associated with a higher rate of COVID-19 cases per 100k, when adjusted for testing effort. Our study demonstrates that the most socially disadvantaged are not only at an increased risk for COVID-19 infection, but lack the privilege to fully engage in social distancing interventions.</p>
Urban Redevelopment by Census Tract in New York City (2000 -2020)
<p>The annual urban redevelopment map for NYC was produced using the classification method proposed in this experiment to highlight the spatial and temporal distribution of urban reconstructions. The time-series gentrification risk maps illustrate areas that have faced gentrification risk since 2000. The data was aggregated to the Census tract level for displaying a visually friendly result. The raw building-level data is also provided.</p>
Green Roofs Footprints for New York City, Assembled from Available Data and Remote Sensing
<p><strong><em>Summary:</em></strong></p> <p>The files contained herein represent green roof footprints in NYC visible in 2016 high-resolution orthoimagery of NYC (described at <a href="https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_AerialImagery.md">https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_AerialImagery.md</a>). Previously documented green roofs were aggregated in 2016 from multiple data sources including from NYC Department of Parks and Recreation and the NYC Department of Environmental Protection, greenroofs.com, and greenhomenyc.org. Footprints of the green roof surfaces were manually digitized based on the 2016 imagery, and a sample of other roof types were digitized to create a set of training data for classification of the imagery. A Mahalanobis distance classifier was employed in Google Earth Engine, and results were manually corrected, removing non-green roofs that were classified and adjusting shape/outlines of the classified green roofs to remove significant errors based on visual inspection with imagery across multiple time points. Ultimately, these initial data represent an estimate of where green roofs existed as of the imagery used, in 2016.</p> <p>These data are associated with an existing GitHub Repository, <a href="https://github.com/tnc-ny-science/NYC_GreenRoofMapping">https://github.com/tnc-ny-science/NYC_GreenRoofMapping</a>, and as needed and appropriate pending future work, versioned updates will be released here.</p> <p><strong><em>Terms of Use:</em></strong></p> <p>The Nature Conservancy and co-authors of this work shall not be held liable for improper or incorrect use of the data described and/or contained herein. Any sale, distribution, loan, or offering for use of these digital data, in whole or in part, is prohibited without the approval of The Nature Conservancy and co-authors. The use of these data to produce other GIS products and services with the intent to sell for a profit is prohibited without the written consent of The Nature Conservancy and co-authors. All parties receiving these data must be informed of these restrictions. Authors of this work shall be acknowledged as data contributors to any reports or other products derived from these data.</p> <p><strong><em>Associated Files:</em></strong></p> <p>As of this release, the specific files included here are:</p> <ul> <li><em>GreenRoofData2016_20180917.geojson</em> is in the human-readable, GeoJSON format, in geographic coordinates (Lat/Long, WGS84; EPSG 4263).</li> <li><em>GreenRoofData2016_20180917.gpkg</em> is in the GeoPackage format, which is an Open Standard readable by most GIS software including Esri products (tested on ArcMap 10.3.1 and multiple versions of QGIS). This dataset is in the New York State Plan Coordinate System (units in feet) for the Long Island Zone, North American Datum 1983, EPSG 2263.</li> <li><em>GreenRoofData2016_20180917_Shapefile.zip</em> is a zipped folder containing a Shapefile and associated files. Please note that some field names were truncated due to limitations of Shapefiles, but columns are in the same order as for other files and in the same order as listed below. This dataset is in the New York State Plan Coordinate System (units in feet) for the Long Island Zone, North American Datum 1983, EPSG 2263.</li> <li><em>GreenRoofData2016_20180917.csv</em> is a comma-separated values file (CSV) with coordinates for centroids for the green roofs stored in the table itself. This allows for easily opening the data in a tool like spreadsheet software (e.g., Microsoft Excel) or a text editor.</li> </ul> <p><strong><em>Column Information for the datasets:</em></strong></p> <p>Some, but not all fields were joined to the green roof footprint data based on building footprint and tax lot data; those datasets are embedded as hyperlinks below.</p> <ul> <li><em>fid</em> - Unique identifier</li> <li><em>bin</em> - NYC Building ID Number based on overlap between green roof areas and a building footprint dataset for NYC from August, 2017. (Newer building footprint datasets do not have linkages to the tax lot identifier (bbl), thus this older dataset was used). The most current building footprint dataset should be available at: <a href="https://data.cityofnewyork.us/Housing-Development/Building-Footprints/nqwf-w8eh">https://data.cityofnewyork.us/Housing-Development/Building-Footprints/nqwf-w8eh</a>. Associated metadata for fields from that dataset are available at <a href="https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_BuildingFootprints.md">https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_BuildingFootprints.md</a>.</li> <li><em>bbl</em> - Boro Block and Lot number as a single string. This field is a tax lot identifier for NYC, which can be tied to the Digital Tax Map (<a href="http://gis.nyc.gov/taxmap/map.htm">http://gis.nyc.gov/taxmap/map.htm</a>) and PLUTO/MapPLUTO (<a href="https://www1.nyc.gov/site/planning/data-maps/open-data/dwn-pluto-mappluto.page">https://www1.nyc.gov/site/planning/data-maps/open-data/dwn-pluto-mappluto.page</a>). Metadata for fields pulled from PLUTO/MapPLUTO can be found in the PLUTO Data Dictionary found on the aforementioned page. All joins to this bbl were based on MapPLUTO version 18v1.</li> <li><em>gr_area</em> - Total area of the footprint of the green roof as per this data layer, in square feet, calculated using the projected coordinate system (EPSG 2263).</li> <li><em>bldg_area</em> - Total area of the footprint of the associated building, in square feet, calculated using the projected coordinate system (EPSG 2263).</li> <li><em>prop_gr</em> - Proportion of the building covered by green roof according to this layer (<em>gr_area</em>/<em>bldg_area</em>).</li> <li><em>cnstrct_yr</em> - Year the building was constructed, pulled from the Building Footprint data.</li> <li><em>doitt_id</em> - An identifier for the building assigned by the NYC Dept. of Information Technology and Telecommunications, pulled from the Building Footprint Data.</li> <li><em>heightroof</em> - Height of the roof of the associated building, pulled from the Building Footprint Data.</li> <li><em>feat_code</em> - Code describing the type of building, pulled from the Building Footprint Data.</li> <li><em>groundelev</em> - Lowest elevation at the building level, pulled from the Building Footprint Data.</li> <li><em>qa</em> - Flag indicating a positive QA/QC check (using multiple types of imagery); all data in this dataset should have 'Good'</li> <li><em>notes</em> - Any notes about the green roof taken during visual inspection of imagery; for example, it was noted if the green roof appeared to be missing in newer imagery, or if there were parts of the roof for which it was unclear whether there was green roof area or potted plants.</li> <li><em>classified</em> - Flag indicating whether the green roof was detected image classification. (1 for yes, 0 for no)</li> <li><em>digitized</em> - Flag indicating whether the green roof was digitized prior to image classification and used as training data. (1 for yes, 0 for no)</li> <li><em>newlyadded</em> - Flag indicating whether the green roof was detected solely by visual inspection after the image classification and added. (1 for yes, 0 for no)</li> <li><em>original_source</em> - Indication of what the original data source was, whether a specific website, agency such as NYC Dept. of Parks and Recreation (DPR), or NYC Dept. of Environmental Protection (DEP). Multiple sources are separated by a slash.</li> <li><em>address</em> - Address based on MapPLUTO, joined to the dataset based on <em>bbl</em>.</li> <li><em>borough</em> - Borough abbreviation pulled from MapPLUTO.</li> <li><em>ownertype</em> - Owner type field pulled from MapPLUTO.</li> <li><em>zonedist1</em> - Zoning District 1 type pulled from MapPLUTO.</li> <li><em>spdist1</em> - Special District 1 pulled from MapPLUTO.</li> <li><em>bbl_fixed</em> - Flag to indicate whether <em>bbl</em> was manually fixed. Since tax lot data may have changed slightly since the release of the building footprint data used in this work, a small percentage of bbl codes had to be manually updated based on overlay between the green roof footprint and the MapPLUTO data, when no join was feasible based on the bbl code from the building footprint data. (1 for yes, 0 for no)</li> </ul> <p>For <em>GreenRoofData2016_20180917.csv</em> there are two additional columns, representing the coordinates of centroids in geographic coordinates (Lat/Long, WGS84; EPSG 4263):</p> <ul> <li><em>xcoord</em> - Longitude in decimal degrees.</li> <li><em>ycoord</em> - Latitude in decimal degrees.</li> </ul> <p><strong><em>Acknowledgements: </em></strong></p> <p>This work was primarily supported through funding from the J.M. Kaplan Fund, awarded to the New York City Program of The Nature Conservancy, with additional support from the New York Community Trust, through New York City Audubon and the Green Roof Researchers Alliance.</p>
Figure 2 in Status of the tiger beetle Cicindela hirticollis Say (Coleoptera: Cicindelidae) in New York City and on Long Island, New York, USA
Figure 2. Adult specimens of Cicindela hirticollis Say collected at Rockaway Beach on Long Island between 1909 and 1932, showing variability in elytral color pattern ranging from the form described as C. h. hirticollis Say (right) to the form described as C. h. rhodensis Calder (left). Specimens in top row are males; specimens in bottom row are females.
Figure 1 in Status of the tiger beetle Cicindela hirticollis Say (Coleoptera: Cicindelidae) in New York City and on Long Island, New York, USA
Figure 1. Historical (circles) and de novo (triangles) survey sites for the tiger beetle Cicindela hirticollis Say in New York City and on Long Island, New York. Open symbols indicate no detection, light gray circles indicate sites not surveyed, and dark gray symbols indicate that at least one adult was detected. Symbols in black indicate large populations (> 40 individuals detected).
Socioeconomic disparities in subway use and COVID-19 outcomes in New York City
Open the record for dataset details and reuse information.
Burke Library New York City Religions web archive collection derivatives
<p>Web archive derivatives of the <a href="https://archive-it.org/collections/1945">Burke Library New York City Religions</a> collection from <a href="https://archive-it.org/home/Columbia">Columbia University Libraries</a>. The derivatives were created with the <a href="https://github.com/archivesunleashed/aut/">Archives Unleashed Toolkit</a> and <a href="https://cloud.archivesunleashed.org/">Archives Unleashed Cloud</a>.</p> <p>The <strong>cul-1945-parquet.tar.gz</strong> derivatives are in the <a href="https://parquet.apache.org/">Apache Parquet format</a>, which is a <a href="http://en.wikipedia.org/wiki/Column-oriented_DBMS">columnar storage</a> format. These derivatives are generally small enough to work with on your local machine, and can be easily converted to Pandas DataFrames. See <a href="https://github.com/archivesunleashed/notebooks/blob/master/datathon-nyc/parquet_pandas_stonewall.ipynb">this</a> notebook for examples.</p> <p><strong>Domains</strong></p> <pre><code class="language-java">.webpages().groupBy(ExtractDomainDF($"url").alias("url")).count().sort($"count".desc)</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>domain</li> <li>count</li> </ul> <p><strong>Web Pages</strong></p> <pre><code class="language-java">.webpages().select($"crawl_date", $"url", $"mime_type_web_server", $"mime_type_tika", RemoveHTMLDF(RemoveHTTPHeaderDF(($"content"))).alias("content"))</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>crawl_date</li> <li>url</li> <li>mime_type_web_server</li> <li>mime_type_tika</li> <li>content</li> </ul> <p><strong>Web Graph</strong></p> <pre><code class="language-java">.webgraph()</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>crawl_date</li> <li>src</li> <li>dest</li> <li>anchor</li> </ul> <p><strong>Image Links</strong></p> <pre><code class="language-java">.imageLinks()</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>src</li> <li>image_url</li> </ul> <p><a href="https://github.com/archivesunleashed/aut-docs/blob/master/current/binary-analysis.md#binary-analysis"><strong>Binary Analysis</strong></a></p> <ul> <li>Images</li> <li>PDFs</li> <li>Presentation program files</li> <li>Spreadsheets</li> <li>Text files</li> <li>Word processor files<br> </li> </ul> <p>The <strong>cul-1945-auk.tar.gz </strong>derivatives<strong> </strong>are the <a href="https://cloud.archivesunleashed.org/derivatives">standard set of web archive derivatives</a> produced by the Archives Unleashed Cloud.</p> <ul> <li><strong>Gephi </strong>file, which can be loaded into <a href="https://gephi.org/">Gephi</a>. It will have basic characteristics already computed and a basic layout.</li> <li><strong>Raw Network</strong> file, which can also be loaded into <a href="https://gephi.org/">Gephi</a>. You will have to use that network program to lay it out yourself.</li> <li><strong>Full text</strong> file. In it, each website within the web archive collection will have its full text presented on one line, along with information around when it was crawled, the name of the domain, and the full URL of the content.</li> <li><strong>Domains count</strong> file. A text file containing the frequency count of domains captured within your web archive.</li> </ul>
Searching for anthrax in the New York City subway metagenome.
<p>You can view the write up at the following link: http://read-lab-confederation.github.io/nyc-subway-anthrax-study/</p> <p>This data set includes the scripts and write up of the following GitHub repository: https://github.com/Read-Lab-Confederation/nyc-subway-anthrax-study</p> <p> </p> <p>In January 2015 Chris Mason and his team published<sup>1</sup> an in-depth analysis of metagenomic<sup>2</sup> data(environmental shotgun DNA sequence) from samples isolated from public surfaces in the New York City (NYC) subway system. Along with a ton of really interesting findings, the authors claimed to have detected DNA from the bacterial biothreat pathogens <em>Bacillus anthracis</em> (which causes anthrax) and <em>Yersinia pestis</em>(causes plague) in some of the samples. This predictably led to a huge interest from the press and scientists on social media. The authors followed up with an re-analysis of the data on microbe.net<sup>3</sup>, where they showed some results that suggested the tools that they were using for species identification overcalled anthrax and plague.</p> <p><em>B. anthracis</em> is a Gram-positive bacterium that forms tough spores as part of its lifecycle. The 5.2 M basepair (Mb) main chromosome is very similar to those of other bacteria in species informally called the ‘<em>Bacillus cereus</em> group’<sup>4</sup> (including <em>B. cereus</em>, <em>B. thuringiensis</em> and <em>B. mycoides</em>). <em>Bacillus cereus</em> group strains in general are commonly found in soil but <em>B. anthracis</em> itself is very rare and generally associated with livestock grazing sites with a past history of anthrax.</p> <p>What sets <em>B. anthracis</em> apart from close relatives is the presence of two plasmids: pXO1 (181kb), which carries the lethal toxin genes and pXO2 (94kb), which includes genes for a protective capsule. Without one of these plasmids, <em>B. anthracis</em> is considered attenuated in virulence and unable to cause classic anthrax. Other <em>B. cereus</em> group bacteria can have plasmids very similar to pXO1 and pXO2 but missing the important virulence genes. Rarely, other <em>B. cereus</em> group carry pXO1 and appear to cause anthrax-like disease. Its a confusing situation, not helped by the current overly-narrow species definitions. This recent review<sup>5</sup> gives more information.</p> <p>The NYC subway metagenome study raised very timely questions about using unbiased DNA sequencing for pathogen detection. We were interested in this dataset as soon as the publication appeared and started looking deeper into why the analysis software gave false positive results and indeed what exactly was found in the subway samples. We decided to wrap up the results of our preliminary analysis and put it on this site. This report focuses on the results for <em>B. anthracis</em> but we also did some preliminary work on <em>Y.pestis</em> and may follow up on this later.</p> <ol> <li>http://www.sciencedirect.com/science/article/pii/S2405471215000022</li> <li>http://en.wikipedia.org/wiki/Metagenomics</li> <li>http://microbe.net/2015/02/17/the-long-road-from-data-to-wisdom-and-from-dna-to-pathogen/</li> <li>http://genome.cshlp.org/content/22/8/1512</li> <li>http://www.annualreviews.org/doi/abs/10.1146/annurev.micro.091208.073255</li> </ol> <p> </p> <p> </p>
Map of New York City
New York City encompasses five county-level administrative divisions called boroughs: Manhattan, Brooklyn, Queens, The Bronx, and Staten Island. All boroughs are part of New York City, and each of the boroughs is coextensive with a respective county, the primary administrative subdivision within New York State. Queens and The Bronx are concurrent with the counties of the same name, while Manhattan, Brooklyn, and Staten Island correspond to New York, Kings, and Richmond Counties respectively. Source: Objaverse 1.0 / Sketchfab
New York City Multi-scalar Street Segment Data
<p>This dataset compiles a comprehensive database containing 90,327 street segments in New York City, covering their street design features, streetscape design, Vision Zero treatments, and neighborhood land use. It has two scales-street and street segment group (aggregation of same type of street at neighborhood). This dataset is derived based on all publicly available data, most from NYC Open Data. The detailed methods can be found in the published paper, <a href="https://journals.sagepub.com/doi/10.1177/03611981241263570" target="_blank" rel="noopener">Pedestrian and Car Occupant Crash Casualties Over a 9-Year Span of Vision Zero in New York City</a>. To use it, please refer to the metadata file for more information and cite our work. A full list of raw data source can be found below:</p> <ul> <li>Motor Vehicle Collisions – NYC Open Data: <a href="https://data.cityofnewyork.us/Public-Safety/Motor-Vehicle-Collisions-Crashes/h9gi-nx95">https://data.cityofnewyork.us/Public-Safety/Motor-Vehicle-Collisions-Crashes/h9gi-nx95</a></li> <li>Citywide Street Centerline (CSCL) – NYC Open Data: <a href="https://data.cityofnewyork.us/City-Government/NYC-Street-Centerline-CSCL-/exjm-f27b">https://data.cityofnewyork.us/City-Government/NYC-Street-Centerline-CSCL-/exjm-f27b</a></li> <li>NYC Building Footprints – NYC Open Data: <a href="https://data.cityofnewyork.us/Housing-Development/Building-Footprints/nqwf-w8eh">https://data.cityofnewyork.us/Housing-Development/Building-Footprints/nqwf-w8eh</a></li> <li>Practical Canopy for New York City: https://zenodo.org/record/6547492</li> <li>New York City Bike Routes – NYC Open Data: <a href="https://data.cityofnewyork.us/Transportation/New-York-City-Bike-Routes/7vsa-caz7">https://data.cityofnewyork.us/Transportation/New-York-City-Bike-Routes/7vsa-caz7</a></li> <li>Sidewalk Widths NYC (originally from Sidewalk – NYC Open Data): <a href="https://www.sidewalkwidths.nyc/">https://www.sidewalkwidths.nyc/</a></li> <li>LION Single Line Street Base Map - The NYC Department of City Planning (DCP): <a href="https://www.nyc.gov/site/planning/data-maps/open-data/dwn-lion.page">https://www.nyc.gov/site/planning/data-maps/open-data/dwn-lion.page</a></li> <li>NYC Planimetric Database Median – NYC Open Data: <a href="https://data.cityofnewyork.us/Transportation/NYC-Planimetrics/wt4d-p43d">https://data.cityofnewyork.us/Transportation/NYC-Planimetrics/wt4d-p43d</a></li> <li>NYC Vision Zero Open Data (including multiple datasets including all the implementations): <a href="https://www.nyc.gov/content/visionzero/pages/open-data">https://www.nyc.gov/content/visionzero/pages/open-data</a></li> <li> NYS Traffic Data - New York State Department of Transportation Open Data: <a href="https://data.ny.gov/Transportation/NYS-Traffic-Data-Viewer/7wmy-q6mb">https://data.ny.gov/Transportation/NYS-Traffic-Data-Viewer/7wmy-q6mb</a></li> <li> Smart Location Database - US Environmental Protection Agency: <a href="https://www.epa.gov/smartgrowth/smart-location-mapping">https://www.epa.gov/smartgrowth/smart-location-mapping</a></li> <li> Race and ethnicity in area - American Community Survey (ACS): https://www.census.gov/programs-surveys/acs</li> </ul> <p> </p>
Worker Cooperatives' Potential for Migrant Women's Self-Empowerment. Insights from a Case Study in New York City
<p>Many migrant women in New York City face structural discrimination and administrative hurdles that complicate their access to safe and well-paid labor. Worker cooperatives have been shown to reduce the precarity and economic exclusion of marginalized groups. However, although much is known about worker cooperatives’ economic potential for improving workers’ lives, other social effects remain far less well explored. The present research contributes to exploring this gap by examining how joining a worker cooperative empowers migrant women in their everyday lives. We apply the concept of self-empowerment to several spheres of the everyday lives of migrant women. At an empirical level, the study focuses on migrant women who are members of nine cleaning- or care-worker cooperatives in New York City. The data were gathered using a participatory research approach and consist of interviews, participant observations, and a quantitative survey. The findings are that worker cooperatives have empowering effects on migrant women beyond the sphere of paid work. Although the additional unpaid workload as co-owners of cooperatives represents an extra burden for many migrant women, they now have better wages, more flexibility, and safer workplaces. Furthermore, they acquire a range of leadership skills, enlarge their social network beyond their ethnic communities, and earn increased esteem as co-owners of businesses. Through worker-ownership, migrant women thus increase their economic, cultural, social, and symbolic capital, which enables them to exercise more agency not only in their paid work but also in their families and leisure time.</p>
The State of the Urban Forest in New York City - Supplemental Datasets
<p><strong>Summary:</strong></p> <p>The files available here contain summary data for the urban forest of New York City, associated with the report, <em>The State of the Urban Forest in New York City</em>, developed by The Nature Conservancy and released in 2021. Methods and data used in development of these files are described in Appendix 1 of the report. and additional details and supplemental code are available at <a href="https://github.com/tnc-ny-science/NYC_StateOfUrbanForest_Docs">https://github.com/tnc-ny-science/NYC_StateOfUrbanForest_Docs</a>. If you do not find what you are looking for here, you may contact Michael Treglia at michael.treglia@tnc.org.</p> <p> </p> <p><strong>Terms of Use</strong></p> <p>© The Nature Conservancy. This material is provided as-is, without warranty under a Creative Commons Attribution-NonCommercial-ShareAlike License as set forth in our Conservation Gateway Terms of Use (available at: <a href="http://conservationgateway.org/Pages/Terms-of-Use.aspx">http://conservationgateway.org/Pages/Terms-of-Use.aspx</a>)</p> <p>If using these data, please cite the both the report and the data, based on the following recommended citations.</p> <p>Recommended citation for the report:</p> <p>Treglia, M.L., Acosta-Morel, M., Crabtree, D., Galbo, K., Lin-Moges, T., Van Slooten, A., Maxwell, E.N. 2021. <em>The State of the Urban Forest in New York City</em>. The Nature Conservancy. doi: 10.5281/zenodo.5532876</p> <p>Recommended citation for the data is:</p> <p>Treglia, M.L., Acosta-Morel, M., Crabtree, D., Galbo, K., Lin-Moges, T., Van Slooten, A., Maxwell, E.N. 2021. <em>The State of the Urban Forest in New York City - Supplemental Datasets</em>. The Nature Conservancy. doi: 10.5281/zenodo.5210261</p> <p> </p> <p><strong>Contents</strong></p> <p><strong><em>canopy_jurisdiction_landuse_borough.zip</em></strong> - Zipped folder with comma separated values (.csv) file of land and canopy area summaries by approximated general ownership type, land use categories, and natural/developed breakdown, with Data Dictionary files in .docx and .html formats.</p> <p><strong><em>canopy_streettree_summaries.zip</em></strong> - Zipped folder containing GeoPackage, Esri File Geodatabase, and comma separated values (.csv) files with canopy and street tree summary data at the scales of Neighborhood Tabulation Area, Community District, City Council District, and Borough, with Data Dictionary files in .docx and .html formats.</p> <p><strong><em>equity_data.zip</em></strong> - Zipped folder containing GeoPackage, Esri File Geodatabase, and comma separated values (.csv) files with data used for equity analyses at the scale of Neighborhood Tabulation Area, with Data Dictionary files in .docx and .html formats.</p> <p><strong><em>naturalareas_canopy_jurisdiction_borough.zip</em></strong> - Zipped folder with comma separated values (.csv) file of summaries of natural area canopy data by approximated general ownership type and by borough, with Data Dictionary files in .docx and .html formats.</p> <p> </p> <p>*Note for the contents of <em>equity_data.zip</em>: For data in this .zip folder, the column named "relativecanopychange_percent" represents relative canopy change from 2010 to 2017 as proportions, not percentages. To convert these numbers to percentages, values can be multiplied by 100. The data are accurately depicted in the report, and column names are otherwise accurate in this repository.</p> <p> </p>
New York City Equitable Zoning
<p>This dataset gives <strong>NYC Equitable Zoning (NYCEZ)</strong>, which is a zoning system of NYC derived from census tracts and ACS data with <strong>574 zones</strong>.</p> <p>The zoning system considers data reliability of 3 minority population groups: <strong>population below poverty level</strong>, <strong>seniors above 67</strong>, and <strong>long commuters (>1 hour)</strong>. Underserved groups of interest include the population above 67 years old (seniors), the population under the poverty level, the population with a commute time above one hour, and the population with one or more disabilities. Only the former three groups are considered in zoning, since populations disabilities are already highly correlated with the others.</p> <p>The 2168 census tracts in NYC are aggregated to improve the data reliability of the 3 minority groups. Average margin of error (MOE) percentages at census tract level of population above 67, population below poverty level, and population with a commute time above 1 hour are <strong>15.22%</strong>, <strong>50.07%</strong>, and <strong>18.23%</strong>, respectively. After aggregation to the NYC Equitable Zones, MOE percentages become <strong>8.02%</strong>, <strong>12.33%</strong>, and <strong>9.88%</strong>, respectively. Equitable Zones shown in Figure 5 simultaneously reduces the average MOE percentage of demographic data by <strong>48% for seniors</strong>, <strong>75% for low-income population</strong>, and <strong>46% for long commuters</strong>.</p> <p>Files include:</p> <ul> <li><strong>NYC census tracts shapefile with mapping to NYCEZ </strong>(“zoning” column) <ul> <li>equitable_zoning_new_dissol.cpg</li> <li>equitable_zoning_new_dissol.dbf</li> <li>equitable_zoning_new_dissol.prj</li> <li>equitable_zoning_new_dissol.sbn</li> <li>equitable_zoning_new_dissol.sbx</li> <li>equitable_zoning_new_dissol.shp</li> <li>equitable_zoning_new_dissol.shx</li> </ul> </li> <li><strong>NYCEZ shapefile</strong> <ul> <li>equitable_zoning_new.cpg</li> <li>equitable_zoning_new.dbf</li> <li>equitable_zoning_new.prj</li> <li>equitable_zoning_new.shp</li> <li>equitable_zoning_new.shx</li> </ul> </li> <li><strong>Data used for NYCEZ generation </strong>(from American Community Survey (ACS)) <ul> <li>Number of seniors in each census tract (with 80 variance replicate estimates) <ul> <li>data_elderly.csv</li> </ul> </li> <li>Number of disabled in each census tract (with 80 variance replicate estimates) <ul> <li>data_disabled.csv</li> </ul> </li> <li>Number of long commuters in each census tract (with 80 variance replicate estimates) <ul> <li>data_commute>1h.csv</li> </ul> </li> <li>Number of low incomers in each census tract (with 80 variance replicate estimates) <ul> <li>data_below_poverty.csv</li> </ul> </li> </ul> </li> </ul> <p>Variance replicate estimates from ACS are used to MOE aggregation. Information can be found here: <a href="https://www.census.gov/programs-surveys/acs/data/variance-tables.html">https://www.census.gov/programs-surveys/acs/data/variance-tables.html</a></p>
Resource selection by New York City deer reveals the effective interface between wildlife, zoonotic hazards, and humans
<p class="MsoNormal"><span>Although the role of host movement in shaping infectious disease dynamics is widely acknowledged, methodological separation between animal movement and disease ecology has prevented researchers from leveraging empirical insights from movement data to advance landscape scale understanding of infectious disease risk. To address this knowledge gap, we examine how movement behavior and resource utilization by white-tailed deer (<em>Odocoileus virginianus</em>) determines blacklegged tick (<em>Ixodes scapularis</em>) distribution, which depend on deer for dispersal in a highly fragmented New York City borough. Multi-scale hierarchical resource selection analysis and movement modeling provide insight into how deer's movements contribute to the risk landscape for human exposure to the Lyme disease vector–<em>I. scapularis</em>. We find deer select highly vegetated and accessible residential properties which support blacklegged tick survival. We conclude the distribution of tick-borne disease risk results from individual resource selection by deer across spatial scales in response to habitat fragmentation and anthropogenic disturbances.</span></p>
Information Visualizations to Facilitate HIV-related Patient-provider Communication in New York City (Info Viz: HIV-NYC)
ClinicalTrials.gov study NCT04102540. IPD Sharing: YES. Countries: 1. Publications: 50.
Information About Alzheimer's Disease for Latinos in New York City
ClinicalTrials.gov study NCT04471779. IPD Sharing: YES. Countries: 1. Publications: 17.
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.