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299 results for “New York”
FIGURE 3 in A trilobite cluster from the Silurian Rochester Shale of New York: predation patterns and possible defensive behavior
FIGURE 3. Specimens of Arctinurus boltoni with injuries to the pygidium, under plain and UV light (with brighter areas indicating parts of reconstructed exoskeleton). Arrows point to injuries described in the text. Scale bar = 1 mm. A–B, AMNH-FI-101521. C–D, AMNH-FI-101527.
FIGURE 2 in A trilobite cluster from the Silurian Rochester Shale of New York: predation patterns and possible defensive behavior
FIGURE 2. Diagram of 12 landmarks selected to describe the overall shape of the exoskeleton of Arctinurus boltoni.
FIGURE 7 in A trilobite cluster from the Silurian Rochester Shale of New York: predation patterns and possible defensive behavior
FIGURE 7. Principal components analysis of landmark data, with 49.5% variance in the data explained by the first two PCs (PC1=29.7%, PC2=19.8%). PC1 describes the variation in the intersection of the occipital furrow and anterior-posterior axis and junction points between posterior margin of the 11th tergite. PC2 mostly describes variation in cephalic width.
FIGURE 6 in A trilobite cluster from the Silurian Rochester Shale of New York: predation patterns and possible defensive behavior
FIGURE 6. Arctinurus boltoni specimen AMNH-FI-101520 with injuries to the thorax and pygidium, under A, plain and B, UV light. Arrows point to injuries described in the text. Scale bar = 1 mm.
Microscopic images of Dictyosphaeria ocellata (Howe 5090 and Howe 5585), New York Botanical Garden (NY)
<p>Microscopic images of</p> <p>Valonia ocellata M.Howe - M. A. Howe 5090, 1907-11-25, Bahamas, Watling Island, in the lagoon, 24.063924 -74.532563, New York Botanical Garden (NY) 00937651 (Holotype)</p> <p>Valonia ocellata M.Howe – M.A. Howe 5585, 1907-12-16, Turks and Caicos Islands, Cockburn Harbor, South Caicos, B.W.I., 21.500434 -71.532698, New York Botanical Garden (NY) 02140870</p>
Socioeconomic disparities in subway use and COVID-19 outcomes in New York City
Open the record for dataset details and reuse information.
Summer Water Quality Data from four ponds at Grafton Lakes State Park, New York, United States: 2022
Lake water quality data was collected on four ponds in Grafton Lakes State Park, NY, USA to establish long-term monitoring sites. Samples were collected once a month from May to July of 2022. Temperature and dissolved oxygen profiles were collected using a YSI Sonde. Secchi disk depths were recorded during the July sampling event.
Small mammals surveys, 1981 - 1996, Adirondack Long-Term Ecological Monitoring Program Project No. 10 by Adirondack Ecological Center of the State University of New York College of Environmental Science and Forestry, Newcomb, New York. Environmental Data Initiative
Small mammals are important in forested ecosystems: they are key predators on seeds and invertebrates, provide food for larger predators and act as disease vectors. The objective of this study was to document small mammal abundance and population changes in managed and unmanaged forests of Huntington Wildlife Forest (HWF). Seven sites were sampled from 1981-1996. Fifty traps per site (250 total) were deployed for 4 nights and checked in the mornings. All captured small mammals were identified, sexed, weighed, and measured for reproductive condition, tagged, and brought into the lab for processing. Females with embryos or placental scars were noted in the lab. Over a five-year period, 671 deer mice; 261 woodland jumping mice, 594 southern redbacked voles, 248 short-tailed shrews, 373 masked shrews, 75 smoky shrews and small numbers of other species were captured and sexed/aged. According to Prachar and Sage (1988), weights of deer mice, redbacked voles, woodland jumping mice, short-tailed shrews, masked shrews and smoky shrews differed among years and age classes for 1983-1987. Weights differed between sexes for mice and voles but not shrews. Placental scar/embryo counts of mice and voles did not differ among years, habitats, mammal age classes or sexes. Reproductive rates of shrews exhibited patterns of fluctuation from 1983-1987.
Songbird surveys , 1952 - 1964, 1983 - 2008 Adirondack Long-Term Ecological Monitoring Program Project No. 2 Breeding Birds by Adirondack Ecological Center of the State University of New York College of Environmental Science and Forestry, Newcomb, New York. Environmental Data Initiative.
Study objectives were to (1) Document long-term trends in relative abundance and diversity of breeding forest birds (songbirds) in forest stands with different harvest histories and (2) Identify bird species that can be used as indicators of habitat change or degradation. Declines in neotropical migrants have been linked to changes in habitat quantity and quality across species' range. Songbirds that nest and forage in different habitat types or at different heights in the forest canopy may not be affected equally by forest change or management. We detected breeding songbirds using point-counts at Huntington Wildlife Forest (HWF) in the central Adirondack Mountains of New York during 1983-2000 and modeled on an original songbird point count dataset from Webb et al. (1977). Relative abundance (RA, the number of individual birds/count) was measured in sites with differing management histories, from an unmanaged >300-year-old stand to a stand cut with the shelterwood/overstory removal method just prior to sampling in 1983). Over eighty bird species were detected during the study duration. Songbird ecology and habitat characteristics can be used to understand long-term changes in relative abundance as related to forest change.
Cascading effects of insecticides and road salt on wetland communities, outdoor mesocosm experiment, New York, USA, 2015
Novel stressors introduced by human activities increasingly threaten freshwater ecosystems. The annual application of more than 2.3 billion kg of pesticide active ingredient and 22 billion kg of road salt has led to the contamination of temperate waterways. While pesticides and road salt are known to cause direct and indirect effects in aquatic communities, their possible interactive effects remain widely unknown. Using outdoor mesocosms, we created wetland communities consisting of zooplankton, phytoplankton, periphyton, and leopard frog (Rana pipiens) tadpoles. We evaluated the toxic effects of six broad- spectrum insecticides from three families (neonicotinoids: thiamethoxam, imidacloprid; organophosphates: chlorpyrifos, malathion; pyrethroids: cypermethrin, permethrin), as well as the potentially interactive effects of four of these insecticides with three concentrations of road salt (NaCl; 44, 160, 1600 Cl- mg/L). Organophosphate exposure decreased zooplankton abundance, elevated phytoplankton biomass, and reduced tadpole mass whereas exposure to neonicotinoids and pyrethroids decreased zooplankton abundance but had no significant effect on phytoplankton abundance or tadpole mass. While organophosphates decreased zooplankton abundance at all salt concentrations, effects on phytoplankton abundance and tadpole mass were dependent upon salt concentration. In contrast, while pyrethroids had no effects in the absence of salt, they decreased zooplankton and phytoplankton density under increased salt concentrations. Our results highlight the importance of multiple-stressor research under natural conditions. As human activities continue to imperil freshwater systems, it is vital to move beyond single-stressor experiments that exclude potentially interactive effects of chemical contaminants.
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>
New York Taxi Usage 2014
<p>Data sourced from https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page by https://arxiv.org/pdf/1510.03336.pdf</p> <p>Data for Zenodo backup taken from https://github.com/stanford-futuredata/ASAP/blob/master/Taxi.csv .</p> <p>Stored on Zenodo as backup for Stumpy bAsics tutorial on https://stumpy.readthedocs.io/en/latest/Tutorial_STUMPY_Basics.html .</p>
Niagara Falls, New York, USA & Canada
Niagara Falls, New York, USA & Canada Niagara Falls is a group of three waterfalls at the southern end of Niagara Gorge, spanning the border between the province of Ontario in Canada and the state of New York in the United States. The largest of the three is Horseshoe Falls, also known as Canadian Falls, which straddles the international border of the two countries.The smaller American Falls and Bridal Veil Falls lie within the United States. Bridal Veil Falls is separated from Horseshoe Falls by Goat Island and from American Falls by Luna Island, with both islands situated in New York. https://en.wikipedia.org/wiki/Niagara_Falls G Source: Objaverse 1.0 / Sketchfab
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>
Evaluating and Improving Snow in the National Water Model, using Observations from the New York State Mesonet
This dataset repository contains the dataset that supports the study of Minder et al. (2024): Evaluating and Improving Snow in the National Water Model, using Observations from the New York State Mesonet. The datasets in this archive include output from distributed simulations of the WRF Hydro model, output from point simulations with the Noah-MP land surface model, and manual and automated snow water equivalent (SWE) observations at New York State Mesonet (NYSM) station locations. The formatting and file naming conventions for each dataset are described in detail in the minder_etal_NWM_snow_dataset_ReadMe.pdf file.
Guggenheim interior point cloud New York
Guggenheim interior point cloud. Such a great building very rough needs a lot of cleaning Source: Objaverse 1.0 / Sketchfab
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>
Model-based analysis of tuberculosis genotype clusters in the United States reveals high degree of heterogeneity in transmission, and state-level differences across California, Florida, New York, and Texas.
<p>Data and codes for the publication</p>
Figure 4 in A new peacock spider from the Cape York Peninsula (Araneae: Salticidae: Euophryini: Maratus Karsch 1878)
Figure 4. Automontage images of the habitus of holotype. 1, Dorsal view. 2, Anterior view. 3, Ventral view. 4, Lateral view.
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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)
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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.