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299 results for “New York”
Figure 2 in A new peacock spider from the Cape York Peninsula (Araneae: Salticidae: Euophryini: Maratus Karsch 1878)
Figure 2. Automontage images of left pedipalp of preserved holotype Maratus sagittus sp. nov. 1, Retrolateral view showing finger-like retrolateral tibial apophysis (RTA) and retrolateral sperm duct loop (RSDL). 2, Ventral view showing embolic disc (ED) and tegular lobe (TL). 3, Prolateral view.
Figure 1 in A new peacock spider from the Cape York Peninsula (Araneae: Salticidae: Euophryini: Maratus Karsch 1878)
Figure 1. Habitus of living holotype male Maratus sagittus sp. nov. (Photos 1-6: Robert Whyte). 1, Note paler integument beneath white setae on carapace, glabrous surface beneath the bands of white setae on flanks of carapace. 2, Distinctive yellow arrow-shaped patch of setae on dorsal opisthosoma. 3, Note orange-brown tarsi and metatarsi. Longer leg III ornamented with white banded femur and tibia. 4, Iridescence of the short, squamous setae on opisthosomal plate visible here. 5, Note lack of ornamentation on left leg III due to regeneration. 6, Deep, plum coloured integument of the carapace shown here.
New York Militia Button
This New York Militia uniform button was recovered during an archaeological survey of Brook Farm in the 1990's. Artifacts from the survey illuminate the vast history of the property, ranging from Native American settlements, an experimental utopian community, and Civil War era Camp Andrew. This button dates to the latter and has been intentionally flattened, perhaps for use as a game piece. While no battles were fought in the Boston area during the Civil War, Union troops trained and mustered at places like Camp Andrew before heading south to fight, and many never returned. Artifact lot #102938.Scanned by Brian Schools. Source: Objaverse 1.0 / Sketchfab
New York State Fair Sand Sculpture 2022
Scanned using Polycam on a Pixel 6 Pro, cleanup in blender, no remesh. Source: Objaverse 1.0 / Sketchfab
370 Jay St., Brooklyn, New York
My workplace in downtown Brooklyn Data source: [2014 USGS Post Sandy LiDAR scan of New York City](https://coast.noaa.gov/htdata/lidar1_z/geoid12b/data/4920/) A note on the data extraction: http://av-vo.com/2018/12/25/usgs-lidar-nyc.html Source: Objaverse 1.0 / Sketchfab
New York State Synthetic Population
<p><span>The synthetic population includes nearly 20 million individuals and 7.5 million households in the whole New York State using the PUMS from 2021 5-year ACS. The marginals obtained from the synthetic population well matches the census marginals. When coming to attribute combinations, the synthetic population can still generally follow what the input sample depict. In addition, the synthetic population reconstructs the associations among household members that the input sample shows.</span></p> <p><span>We propose a population synthesis framework that involves both the deterministic model and ciDATGAN to generate households and corresponding personal synthetic populations. The framework is illustrated in the figure below.</span></p> <p><span></span></p> <p> </p> <p><span>A wide range of socio-demographic variables are included, and the variables selected for this study can be found in Table 1. We aggregate categories of some attributes deemed too granular, such as age and working industry (NAICS). To capture potential spatial heterogeneity of the population between New York City (NYC) and non-NYC regions, we separate PUMS by filtering regions within and outside of NYC using the Public Use Microdata Areas (PUMAs). </span></p> <p><span>Because NYC is the most densely populated region in the US with high population diversity, we want higher population resolutions. Therefore, we further assign the NYC specific PUMS from PUMA level to Census Tract (CT) levels by using Popgen.</span></p> <p><span>Table 1. Selected attributes of input samples</span></p> <div> <table> <tbody> <tr> <td> </td> <td> <p><em><span>Non-NYC region attribute (label name)</span></em></p> </td> <td> <p><em><span>No. of values (range if continuous)</span></em></p> </td> <td> <p><em><span>NYC region attribute (label name)</span></em></p> </td> <td> <p><em><span>No. of values</span></em></p> </td> </tr> <tr> <td> <p><em><span>Household attribute</span></em></p> </td> <td> <p><span>Residence area (PUMA)</span></p> </td> <td> <p><span>90</span></p> </td> <td> <p><span>Residence area (CT)</span></p> </td> <td> <p><span>2313</span></p> </td> </tr> <tr> <td> <p><span>Income level (HINCP)</span></p> </td> <td> <p><span>9</span></p> </td> <td> <p><span>Income level (HINCP)</span></p> </td> <td> <p><span>9</span></p> </td> </tr> <tr> <td> <p><span>Vehicle ownership (VEH)</span></p> </td> <td> <p><span>4</span></p> </td> <td> <p><span>Vehicle ownership (VEH)</span></p> </td> <td> <p><span>4</span></p> </td> </tr> <tr> <td> <p><em><span>Personal attribute</span></em></p> </td> <td> <p><span>Age (AGEP)</span></p> </td> <td> <p><span>7</span></p> </td> <td> <p><span>Age (AGEP)</span></p> </td> <td> <p><span>7</span></p> </td> </tr> <tr> <td> <p><span>English proficiency (ENG)</span></p> </td> <td> <p><span>5</span></p> </td> <td> <p><span>English proficiency (ENG)</span></p> </td> <td> <p><span>5</span></p> </td> </tr> <tr> <td> <p><span>Commute trip length (JWMNP)</span></p> </td> <td> <p><span>0-140 min</span></p> </td> <td> <p><span>Gender (SEX)</span></p> </td> <td> <p><span>2</span></p> </td> </tr> <tr> <td> <p><span>Commute mode (JWTRNS)</span></p> </td> <td> <p><span>13</span></p> </td> <td> <p><span>Disability (DIS)</span></p> </td> <td> <p><span>2</span></p> </td> </tr> <tr> <td> <p><span>School status (SCH)</span></p> </td> <td> <p><span>3</span></p> </td> <td> <p><span>Working industry (NAICSP)</span></p> </td> <td> <p><span>2</span></p> </td> </tr> <tr> <td> <p><span>Gender (SEX)</span></p> </td> <td> <p><span>2</span></p> </td> <td> <p><span>Race white/non-white (RACWHT)</span></p> </td> <td> <p><span>2</span></p> </td> </tr> <tr> <td> <p><span>Disability (DIS)</span></p> </td> <td> <p><span>2</span></p> </td> <td> </td> <td> </td> </tr> <tr> <td> <p><span>Working industry (NAICSP)</span></p> </td> <td> <p><span>20</span></p> </td> <td> </td> <td> </td> </tr> <tr> <td> <p><span>Race white/non-white (RACWHT)</span></p> </td> <td> <p><span>2</span></p> </td> <td> </td> <td> </td> </tr> </tbody> </table> <p> </p> </div>
Data from ONSET OF EARLY SPRING ACTIVITY BY AMPHIBIANS IN ITHACA, NEW YORK, USA
<p>This dataset was generated by conducting a search of all verifiable, research grade observations with photographs of amphibians (Category = Amphibia) in Ithaca, New York for the period 1 February to 30 April for the years 2016–2024. We constrained the search to an area within a 5 km radius of Cornell University, Ithaca, New York, USA (42.45° N, 76.48° W). The data output was modified to remove observer names as well as datafields that were extraneous to the analysis.</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>
Building Envelopes in New York's CBD: Normalized Signed Distance Function Representation
<p>This dataset, developed to substantiate the findings of the study by Zhuang et al. [1], comprises 1,521 building envelopes from the Central Business District (CBD) of New York City (NYC), represented in three formats: the wavefront (obj) format, the Signed Distance Function (SDF) format and 3D binary-volume format. </p> <p>The original mesh data is procured from the NYC Open Data Portal [2]. The process of data manipulation is thoroughly delineated in the referenced research paper [1].</p> <p>[1] Zhuang, X., Ju, Y., Yang, A., & Caldas, L. (2023). Synthesis and Generation for 3D Architecture Volume with Generative Modeling. International Journal of Architecture Computing, AI, Architecture, Accessibility, & Data Justice. DOI: 10.1177/14780771231168233.</p> <p>[2] New York city department of city planning. NYC 3D model by community district, manhattan district, MN05. 2018. Available at: https://www.nyc.gov/site/planning/data-maps/open-data/dwn-nyc-3d-model-download.page. Accessed 15 July 2023.</p> <p> </p>
Corn field management for wintering waterfowl on eastern Long Island, New York
<p>The study took place in corn fields in Suffolk County, Long Island, New York, 7 February – 4 April 2018 and 7 February – 10 April 2019 (Fig.1). Fields were planted for typical production corn with 15.2 cm (6 inch) spacing among plants in rows 30.5 cm (12 inch) apart. Suffolk County contains coastal wetlands, freshwater ponds, and rural landscapes where corn fields are available to wintering waterfowl. Seasonal corn yield and wildlife abundance were determined at two corn fields in 2018 and 2019 (Cutchogue [41.023 ° N, -72.511° W] and Orient Point [41.141° N, -72.278° W]) and included another corn field in 2019 (Brookhaven [40.798° N, -72.891° W]).</p> <p>Corn fields were divided into three sections and marked them with flagging to identify them from a distance. The mean (± SE) corn field size was 4.08 ± 0.20 ha (Cutchogue = 3.99 ha, [0.87 ha, 1.33 ha, and 1.79 ha sections]; Orient = 4.47 ha, [1.46 ha, 1.46 ha, and 1.55 ha sections]; Brookhaven = 3.78 ha, [1.26 ha, 1.26 ha, and 1.26 ha sections]).</p> <p>Corn field samples were taken to obtain an index of corn availability and corn depletion rates following Barney [8]. One section in each field was chopped with a brush-hog every 2 weeks until all three sections in a field were chopped. Section of standing corn were sampled once the day before chopping and after chopping once every two weeks in 2018 and weekly in 2019. Sampling was adjusted to weekly in 2019 because some sections were depleted to zero or near zero kg/ha in < 2 weeks during 2018. A random sampling design was used to distribute samples throughout the field. Main transects (<em>n</em> = 3) were established perpendicular to the field edge in each section of a field (evenly spaced 20 − 26 m apart). Each sampling period, a random number generator was used to select sampling points along each main transect. The same number of samples were taken along each main transect (<em>n</em> = 4; <em>n </em>= 12 per section). A random number generator was used to determine the left or right direction of samples to be taken off of the main transect along a perpendicular transect. A random number generator was used to determine the distance of the sampling point along the perpendicular transect (between 1 – 10 m). Corn was sampled using a 1 m <strong>× </strong>1 m quadrat at each sampling point and all corn within each quadrat was collected and placed in marked plastic bags. All individual kernels, cobs full of kernels, and cobs partial covered in kernels were included in the sample and frozen within 4 hrs of sampling. In the lab, corn was thawed, kernels were removed from cobs, and samples were dried at 60℃ until a constant mass at 48 hrs and weighed to ± 0.1 g, and reported as kg/ha.</p> <p>Wildlife surveys were conducted at each field 8 February – 3 April 2018 and 8 February – 9 April 2019. Morning and evening surveys were conducted, switching the time of survey at each field weekly. Morning surveys occurred 30 min before to 2 h after sunrise and evening surveys were 2 h before to 30 mins after sunset. To survey two fields on the same day, one field was surveyed in the morning and another field in the evening following weekly protocol for switching survey times. Each field was surveyed 3 times per week. Observation points were adjusted accordingly to maximize clear line of site when each section was chopped. Waterfowl flew into and landed in fields during sunrise and sunset surveys. Canada geese that were in fields at the start counts were included. This scenario reduced error in counting and identifying waterfowl to species, so 100% detection was assumed. For each field, total number of waterfowl, species composition, and other wildlife were recorded. Other wildlife included blackbirds (<em>Icteridae</em>), white-tailed deer (<em>Odocoileus virginianus</em>), and wild turkeys (<em>Meleagris gallopavo</em>).</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>
Harlem Branch New York Public Library
Harlem Branch New York Public Library as seen in the Virtual Harlem Project Source: Objaverse 1.0 / Sketchfab
A Comparison Of Outcomes In Patients In New York Heart Association (NYHA) Class II Heart Failure When Treated With Eplerenone Or Placebo In Addition To Standard Heart Failure Medicines
ClinicalTrials.gov study NCT00232180. IPD Sharing: Not stated. Countries: 29. Publications: 15.
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.
New York City Observational Study of Mpox Immunity
ClinicalTrials.gov study NCT05654883. IPD Sharing: YES. Countries: 1. Publications: 0.
Impact of Medicaid Health Home on Patients With Diabetes in New York City
ClinicalTrials.gov study NCT02713321. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: Perspectives of New York State residents to deer management, hunting, and predator reintroductions
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.