The Oregon Alzheimer Disease Center is the core program of the Layton Aging & Alzheimer's Disease Center (LAADC), supported by the National Institute on Aging (NIA, NIH). We promote interactive, multidisciplinary research among the scientific community. Our primary emphasis is on studies of preclinical dementia, as well as early dementia. Well-characterized patients, clinical, MRI and genetic data, as well as biological specimens are made available to investigators and research groups worldwide.

The sample SAS and Stata code provided here is intended for use with certain datasets in the National Neighborhood Data Archive (NaNDA). NaNDA (https://www.openicpsr.org/openicpsr/nanda) contains some datasets that measure neighborhood context at the ZIP Code Tabulation Area (ZCTA) level. They are intended for use with survey or other individual-level data containing ZIP codes. Because ZIP codes do not exactly match ZIP code tabulation areas, a crosswalk is required to use ZIP-code-level geocoded datasets with ZCTA-level datasets from NaNDA. A ZIP-code-to-ZCTA crosswalk was previously available on the UDS Mapper website, which is no longer active. An archived copy of the ZIP-code-to-ZCTA crosswalk file has been included here. Sample SAS and Stata code are provided for merging the UDS mapper crosswalk with NaNDA datasets.

Opioid use pattern for an example participant; “…” represents a series of UDS results negative for opioids.

NACC’s Uniform Data Set (UDS), collected since 2005, is widely regarded as the gold standard by the field. This longitudinal, multi-domain neurocognitive and phenotypic dataset includes robust, criteria-based diagnoses, providing a valuable foundation for grounding other studies. UDS data collection instruments are trusted benchmarks in Alzheimer’s disease and related dementias (AD/ADRD) clinical phenotypic assessments globally.

Functions in the package CTNote, their categories, and their descriptions.

This is a parallel and comparable corpus of speeches held in the European Parliament; the corpus follows the European Parliament Interpreting Corpora tradition of the EPIC and EPICG corpora. It contains original speeches from 2008 to 2013 by English, German, and Spanish native speakers and their interpretation (English to and from German; Spanish to English). All transcripts in the corpus are based on videos of the European Parliament Proceedings published by the European Parliament. Annotation includes typical characteristics of spoken language such as false starts, hesitations and truncated words. To obtain better results for source-target alignment as well as sentence parsing the transcripts were segmented using a main clause approach: compound sentences were segmented separately. For the second version of the corpus, the transcripts were processed clause by clause with the spaCy NLP tools; the data is encoded in CoNLL-U and provides universal PoS tags, fine-grained language-specific PoS tags as well as Universal Dependency syntactic relations. All data was enriched with relevant metadata such as source language, name of original speaker, speech timing, mode of delivery and delivery rate.

The corpus is available for download from CLARIN-D (Saarland University B-centre).

This project is just getting off the ground, but the intent is to start by mapping historic corner store buildings in our community. Identifying where historic corner stores are located, their current state of use and condition, and other useful information will begin to get a bigger picture of the history of our neighborhoods and the potential for these structures to catalyze hyper-local regeneration. What can the locations of these structures tell us about the neighborhoods they occupy?In some cases these storefronts have managed to hold on and provide the community glue, weathering the detrimental effects of big box stores and other factors that have gutted neighborhood stores. Is there an opportunity to celebrate and support the corner stores that still exist and can we find ways to prioritize rehabilitating the structures that could contribute to neighborhood revitalization? Could the historic corner store become the new cornerstones of neighborhoods?

Homelessness has been a consistent problem for the city of Louisville for decades now. Despite efforts from the city government and local nonprofits, homelessness increased 139% last year alone. The Covid-19 pandemic significantly worsened the crisis, but the risk factors that contribute to homelessness are still endemic across the city: lack of affordable housing, lack of access to physical and mental healthcare, stagnant wages, etc. Homelessness has negative effects on mortality, personal health of the homeless, and public health in general (also see here, no paywall). When I recently attended a strategy meeting for the Louisville Downtown Partnership, one of the top issues voted by attendees was the rise of homelessness downtown. This could come from genuine care or that many Americans associate homeless people with crime. Everyone benefits when the issues that cause homelessness are addressed effectively, and a vital part of that is knowing what areas are most at-risk.The app above was made to map certain risk factors across Jefferson County. The risk factors include percent of households with 50%+ income going to rent, persons without health insurance coverage, percent of households at or below the poverty line, percent of households using public assistance, percent of persons reporting extensive physical and mental distress, unemployment, along with other economic and health-based factors. This doesn’t include every possible factor that could cause homelessness, but many that have strong effects. A dummy census tract was made with all the worst possible outcomes for risk factors, which was then used to rank the similarity of every census tract in Jefferson County; the lower the rank, the more at-risk the tract is. The app allows you to click through every tract in the county and see the ten most at-risk ones.The most at-risk places tend to line up with the west end and areas of the city that were historically redlined. These areas also saw mass amounts of “urban renewal” in the 60s and 70s. They also tend to line up with areas of the city that face the highest eviction rates (thanks to Ryan Massey for pointing this out).

Nœuds

## Overview

Nœuds is a dataset for object detection tasks - it contains Tache Trame Arret Fil Noeuds annotations for 468 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

A. UDS data files ------------------- Eight files are provided that conform to the UDS conventions regarding the naming of files and the format of the data. The eight files are divided into 4 pairs of files with each pair consisting of a file containing data averaged over a 10 minute period and a file containing the maximum data value during the same 10 minute period. The 4 pairs of file contain data for the RAR, the PFR, WFA - magnetic field, and WFA - magnetic field.

FENIKS UDS Catalogs: Version 1.1 (v1.1)

Catalogs produced by the FENIKS collaboration in the UDS field, including the multi-wavelength PSF-matched photometric catalog, the catalogs of photometric redshifts and stellar population properties, as well as other high-level products.

The catalog construction is described in detail in the accompanying paper (Zaidi et al. 2024). Briefly, the catalogs were constructed by combining data in 24 photometric bands covering optical to mid-IR wavelengths ranging from 0.38 microns (MegaCam-uS band) to 8 microns (Spitzer-IRAC ch4 band). The catalog covers a footprint of ~ 0.9 square degrees with full coverage in most of the bands, and close to full coverage in the rest (see the accompanying paper for footprint details).

When using these products, please cite Zaidi et al. 2024 and use the following acknowledgment:

"This work is based on data and catalog products from the FENIKS survey, funded by the National Science Foundation under grants AST-2009442 and AST-2009632"

Please email mailto:kumail.zaidi@tufts.edu?subject=Notifcation of UDS FENIKS catalogs" href="mailto:kumail.zaidi@tufts.edu?subject=Notifcation%20of%20UDS%20FENIKS%20catalogs">kumail.zaidi at tufts.edu or mailto:danilo.marchesini@tufts.edu?subject=Notifcation of UDS FENIKS catalogs" href="mailto:danilo.marchesini@tufts.edu?subject=Notifcation%20of%20UDS%20FENIKS%20catalogs">danilo.marchesini at tufts.edu to communicate any published papers that used the data from this release

v1.1 release update:

The multiplicative factors to apply to color aperture fluxes to get total fluxes, the 'aper_to_tot_corr' column in the photometry catalogs, feniks_uds_v1.1.cat and feniks_uds_v1.1_zpcor.cat have been fixed.

A. UDS data files ---------------------- Eight files are provided that conform to the UDS conventions regarding the naming of files and the format of the data. The eight files are divided into 4 pairs of files with each pair consisting of a file containing data averaged over a 10 minute period and a file containing the maximum data value during the same 10 minute period. The 4 pairs of file contain data for the RAR, the PFR, WFA - magnetic field, and WFA - magnetic field.

This layer presents the 2020 U.S. Census Tract boundaries of the United States in the 50 states and the District of Columbia. This layer is updated annually. The geography is sourced from U.S. Census Bureau 2020 TIGER FGDB (National Sub-State) and edited using TIGER Hydrography to add a detailed coastline for cartographic purposes. Attribute fields include 2020 total population from the U.S. Census Public Law 94 data.This ready-to-use layer can be used in ArcGIS Pro and in ArcGIS Online and its configurable apps, dashboards, StoryMaps, custom apps, and mobile apps. The data can also be exported for offline workflows. Cite the 'U.S. Census Bureau' when using this data.

Land use divided into legend classes (Corine Landcover), roads and hydrography. It represents a first level of knowledge, with characteristics and properties of a territorial database. It was built, with some adjustments to the regional specificity, according to the standard classification methodology of the territorial entities of the CORINE Land Cover (CLC) legend, an EU project whose objective is to set up the homogeneous database at European level on the coverage and land use and its changes over time.

NaNDA contains some datasets that measure neighborhood context at the ZIP Code Tabulation Area (ZCTA) level. They are intended for use with survey or other individual-level data containing ZIP codes. Because ZIP codes do not exactly match ZIP code tabulation areas, a crosswalk is required to use ZIP-code-level geocoded datasets with ZCTA-level datasets from NaNDA. A ZIP-code-to-ZCTA crosswalk is available on the UDS Mapper website at https://www.udsmapper.org/zcta-crosswalk.cfm.The sample SAS and Stata code provided here merges the UDS Mapper crosswalk with NaNDA datasets.

A. UDS data files -------------------- Eight files are provided that conform to the UDS conventions regarding the naming of files and the format of the data. The eight files are divided into 4 pairs of files with each pair consisting of a file containing data averaged over a 10 minute period and a file containing the maximum data value during the same 10 minute period. The 4 pairs of file contain data for the RAR, the PFR, WFA - magnetic field, and WFA - magnetic field.

Pp Uds Company Export Import Records. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

A variety of methods are commonly used to quantify animal home ranges using location data acquired with telemetry. High-volume location data from global positioning system (GPS) technology provide researchers the opportunity to identify various intensities of use within home ranges, typically quantified through utilization distributions (UDs). However, the wide range of variability evident within UDs constructed with modern home range estimators is often overlooked or ignored during home range comparisons, and challenges may arise when summarizing distributional shifts among multiple UDs. We describe an approach to gain additional insight into home range changes by comparing UDs across isopleths and summarizing comparisons into meaningful results. To demonstrate the efficacy of this approach, we used GPS location data from 16 bighorn sheep (Ovis canadensis) to identify distributional changes before and after habitat alterations, and we discuss advantages in its application when comparing home range size, overlap, and joint-space use. We found a consistent increase in bighorn sheep home range size when measured across home range levels, but that home range overlap and similarity values decreased when examined at increasing core levels. Our results highlight the benefit of conducting multiscale assessments when comparing distributions, and we encourage researchers to expand comparative home range analyses to gain a more comprehensive evaluation of distributional changes and to evaluate comparisons across home range levels.

FQHC data mapped from the 2018 UDS data.