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.
164
datasets available to search
ShareScore release 0.9.0
Dataset results
164 results for “neighborhood”
Tree seedling abundance mediated by local neighborhood effects and microsite conditions
Tree recruitment across elevational gradients is shaped by myriad factors, including abundance of nearby conspecific adults and abiotic establishment conditions. We measured seedling abundance and overstory basal area for six tree species that dominate northern temperate and montane boreal forests on mountains in the northeastern United States. Data were collected on elevational transects spaced at 100m-intervals of elevation on ten mountains. Associated environmental variables, including vegetation cover, substrate, microclimate, and light environment, were also collected.
Evidence of alliesthesia during a neighborhood thermal walk in a hot and dry city (Phoenix, Arizona)
Thermal comfort should be an integral part of urban design in the context of global warming and urbanization. The influence of built infrastructure on thermal perceptions of walking pedestrians is not well explored, but thermal walks that combine sensing technologies with simultaneous collection of user experiences is a promising research direction to shorten the gap. We examined the relationships between the built environment, heat perception, and behavioral coping mechanisms in one of the most heat vulnerable Phoenix neighborhoods. Using Phoenix as an example, where extremely hot summer temperatures are becoming a norm, can help to address heat challenges of other cities that are facing rising temperatures. This study is an experimental citizen science project in which participants were surveyed during a 1-hour walk around the neighborhood and recorded their experience in a field guide. Walkers wore GPS devices and microclimate measurements were taken to gain deeper insights on subjective heat perception and physical body heat accumulation during the walk. Results revealed the differences in heat perception across a variety of urban landscapes. Participants identified preferred and most challenging locations. Combined GPS and microclimate data mapped in GIS visualized dependencies between the streetscape, microclimate, and thermal perceptions. Moreover, we presented the evidence of thermal alliesthesia, a feeling of pleasure from relieving of thermal discomfort. This project is one of the first to examine the impact of urban environment on dynamic psychological and physiological responses to heat. Using sensing technologies and collecting subjective perceptions, this research will inform the design changes in the neighborhood that will undergo redevelopment. It can serve as an example for other cities striving to adapt urban microclimates to new extremes.
Fine-scale meteorological observations from walking traverses in two Phoenix Area Social Survey (PASS) 2017 neighborhoods (2019)
This dataset includes human-biometeorological observations from 2.5 km walking traverses with a mobile weather station. The traverses occurred in two 2017 Phoenix Area Social Survey neighborhoods (U18: South Phoenix/Salt River (Audubon) and W15: Camelback Mountain) on one day in June and October, at 12pm and 4pm on each day. Specifically, air temperature, humidity, wind speed, and radiant energy (infrared and solar radiation) in 3-dimensions were measured at 2-second intervals. Additionally, mean radiant temperature was calculated from the radiation measurements. The meteorological observations are spatially referenced with latitude and longitude coordinates. The paths through the neighborhoods were chosen to maximize proximity to PASS 2017 participants’ homes.
Wildlife in urban neighborhoods of the greater Phoenix, Arizona metropolitan area: patterns that span a social-ecological gradient in 2021
Wildlife communities are structured by numerous ecological filters in cities that influence their populations, and some species even manage to thrive in urban landscapes. CAP researchers were the first to observe “the luxury effect”, the hypothesis that biodiversity is positively related to income of residents. The luxury effect is still being tested worldwide twenty years later and has led to important new research on other socio-demographic factors that shape biodiversity but are vastly understudied, such as race and ethnicity, as well as the interaction of these factors with urban structural inequalities that may be hidden by income. This research aims to unpack the luxury effect by considering other landscape and socio-demographic factors that may influence wildlife communities across neighborhoods of metro Phoenix. Specifically, we are investigating if neighborhood income and ethnicity independently influence mammal occupancy in neighborhoods across the CAP ecosystem. To answer this question, we leveraged a wildlife camera array across CAP within community parks, in which cameras are placed across a gradient of average median household income and percent Latinx of residents. Incorporating socioeconomic data into urban mammal research will allow for the advancement in the understanding of socio-ecological patterns.
Data for the article "Typhon: a polar stream from the outer halo raining through the Solar neighborhood"
<p>This data contains the stellar parameters of Typhon stream stars in the context of the "Typhon: a polar stream from the outer halo raining through the Solar neighborhood" (Tenachi et al. 2022) paper, in two formats:.csv and .fits (which also contains a short description of each column).</p> <p>This data includes:</p> <ul> <li>Stellar coordinates and parameters from Gaia DR3 (Gaia Collaboration 2022) with extinction-corrected magnitudes using the (Schlafly and Finkbeiner 2011) corrections to the (Schlegel et al. 1998) extinction maps, assuming the extinction ratios A<sub>G</sub>/A<sub>V</sub> = 0.86117, A<sub>GBP</sub>/A<sub>V</sub> = 1.06126 and A<sub>GRP</sub> /A<sub>V</sub> = 0.64753, as listed on the web interface to the PARSEC isochrones (Bressan et al. 2012) and assuming a solar position (x,y,z) = (−8.2240, 0, 0.0028) kpc (Bovy 2020, Widmark et al. 2021) and a solar velocity (vx,vy,vz) = (11.10, 7.20, 7.25) km/s with a circular velocity = 243 km/s (Schonrich et al. 2010, Bovy 2020).</li> <li>Added dynamical parameters (actions, energy, apocenters and pericenters values) derived in a (McMillan et al 2017) potential.</li> <li>Metallicity parameters from LAMOST DR8 PASTEL column (Wang et al 2022).</li> <li>Independent measurements from the "Chemical Abundances of the Typhon Stellar Stream" follow-up paper (Ji et al 2022).</li> </ul>
Neighborhood Socioeconomic and demographic changes in Baltimore's (BES) Neighborhoods: 1930 to 2010
This dataset was created primarily to map and track socioeconomic and demographic variables from the US Census Bureau from year 1940 to year 2010, by decade, within the City of Baltimore's Mayor's Office of Information Technology (MOIT) year 2010 neighborhood boundaries. The socioeconomic and demographic variables include the percent White, percent African American, percent owner occupied homes, percent vacant homes, the percentage of age 25 and older people with a high school education or greater, and the percentage of age 25 and older people with a college education or greater. Percent White and percent African American are also provided for year 1930. Each of the the year 2010 neighborhood boundaries were also attributed with the 1937 Home Owners' Loan Corporation (HOLC) definition of neighborhoods via spatial overlay. HOLC rated neighborhoods as A, B, C, D or Undefined. HOLC categorized the perceived safety and risk of mortgage refinance lending in metropolitan areas using a hierarchical grading scale of A, B, C, and D. A and B areas were considered the safest areas for federal investment due to their newer housing as well as higher earning and racially homogenous households. In contrast, C and D graded areas were viewed to be in a state of inevitable decline, depreciation, and decay, and thus risky for federal investment, due to their older housing stock and racial and ethnic composition. This policy was inherently a racist practice. Places were graded based on who lived there; poor areas with people of color were labeled as lower and less-than. HOLC's 1937 neighborhoods do not cover the entire extent of the year 2010 neighborhood boundaries. The neighborhood boundaries were also augmented to include which of the year 2017 Housing Market Typology (HMT) the 2010 neighborhoods fall within. Finally, the neighborhood boundaries were also augmented to include tree canopy and tree canopy change year 2007 to year 2015.
Micrometeorological conditions at the CAP LTER flux tower located in the west Phoenix, AZ, USA neighborhood of Maryvale (2010-2019)
Preface ============ USERS ARE CAUTIONED THAT THE DATA PRESENTED IN THIS DATASET ARE RAW DATA DOWNLOADED DIRECTLY FROM DATA LOGGERS; THE DATA HAVE NOT BEEN PROCESSED OR CHECKED FOR ACCURACY, COMPLETENESS OR QUALITY IN ANY CAPACITY; ANY CALCULATED VALUES DERIVE DIRECTLY FROM A DATA LOGGER. Overview ============ The CAP LTER maintains a 23-m urban eddy flux tower in the west Phoenix, AZ neighborhood of Maryvale (-112.1426 W, +33.4838 N). The tower is instrumented for long-term measurement of the covariance of turbulent eddy and radiative fluxes for the purposes of urban micrometeorological research. Additional micrometerological parameters, such as precipitation, are also measured. Select soil parameters were measured from 2012 through 2016. Eddy covariance data are collected and stored at two temporal resolutions: 10 Hz and 30-minute averages. Data presented here include only 30-minute averages. Owing to large volumes, 10-Hz data are avialable by request to the CAP LTER Information Manager (caplter.data@asu.edu).
Numerical summaries of vegetation indices and land surface temperature derived from remotely sensed imagery in Phoenix Area Social Survey (PASS) neighborhoods of central Arizona
This project calculates two vegetation indices: Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI), and land surface temperature (LST) from remotely sensed imagery. NDVI and SAVI are calculated from the 2010, 2013, 2015, and 2017 NAIP imagery (1m resolution). LST is calculated from Landsat 5 and 8 imagery (30m resolution) from summer months in 1985, 1990, 1995, 2000, 2005, 2010, and 2015. Summary values are calculated for each of the aforementioned data resources for 2011 and 2017 Phoenix Area Social Survey (PASS) study area boundaries. Tabular summaries of the mean, median, minimum, maximum, and standard deviation of the NDVI, SAVI, and LST values for the 2011 and 2017 Phoenix Area Social Survey boundaries (45 and 12 neighborhoods, respectively) are provided. Javascript code used to process NDVI, SAVI, and LST imagery, and R code used to calculate numerical summaries of NDVI, SAVI, and LST in PASS neighborhoods are included with this dataset. Locations and areas of PASS study neighborhood boundaries and source imagery used to calculate these summaries are available through the Environmental Data Initiative - see resouce listing in the methods of this data set.
A multi-instrument thermal profile of Edison Eastlake, a Phoenix, Arizona, USA neighborhood, on a summer day in 2019
We have measured the thermal environment conditions of the Edison Eastlake neighborhood in Phoenix, AZ on a clear sky, hot, sunny day of June 19, 2019 to look at the neighborhood microclimate before construction began on the renovation of the public housing units in the community. Measurements were taken with a mobile bio-meteorological weather station, known as MaRTy (Middel & Krayenhoff, 2019), car traverses, four pole-mounted temperature/relative humidity sensors, and two rooftop mounted weather stations. Since the measurement campaign began, construction has been undertaken to redevelop the public housing stock in Edison Eastlake. ### references - Middel, A., & Krayenhoff, E. S. (2019). Micrometeorological determinants of pedestrian thermal exposure during record-breaking heat in Tempe, Arizona: Introducing the MaRTy observational platform. Science of The Total Environment, 687, 137–151. https://doi.org/10.1016/j.scitotenv.2019.06.085
Galactic chemical evolution of the solar neighborhood, solar twins and exoplanet indicators
<p>Galactic chemical evolution (GCE), solar analogues or twins, and peculiarities of<br> the solar composition with respect to the twins are inextricably related.<br> We examine GCE parameters from the literature and present newly derived values<br> using a quadratic fit that gives zero for a Solar age (i.e. 4.6 Gyr). <br> We show how the GCE parameters may be used not only to ``correct'' abundances<br> to the solar age, but to predict {\bf average} relative elemental <br> abundances as a function of age. <br> We address the question of whether the solar abundances<br> are depleted in refractories and enhanced in volatiles and<br> find that the answer is sensitive to the selection of a<br> representative standard. Our best quality data sets do<br> not support the notion that the Sun is depleted in refractories. <br> A simple model allows us to estimate<br> the amount of refractory-rich material missing from the sun<br> or alternately added to the average solar twin. The model<br> gives between zero and 1.4 earth masses. <br> </p>
Digital Inventory of the Wooden Neighborhood of Raksila in Oulu, FInland
<p>The material concerns the investigation made on the case study of Raksila, wooden historic neighborhood in Oulu, Finland.</p> <p>For each building it has been elaborated a census card by fixing the information on a sketch book, the analysis has included several specific aspects: dates of construction, function, materials used for the facades, structure, colours of the facades, typologies of frames and analysis of the open areas. For the analysis of Raksila two different types of card have been designed: one type for the residential buildings and another one (more simplified) for the description of the services located around the neighbourhood.</p> <p>The recognition on field represent a fundamental part in the elaboration of the census card, the operator needs to make an appreciation of the main aspects by producing lists of different typologies connected to a same category, i.e. elaborate different lists of values. Categories and list of values represent the main elements in the formulation of the census card. When the analysis is finished the entire categories analysed with the related lists need to be transfer to a digital card by using FileMaker software. This programme gives the possibility to create detailed digital databases by including specific data sets. After the recognition phase and the elaboration of the card in digital form, the operator can start the acquisition of the information on field. Each building is identified with a specific code that can be elaborated by the operator or, as in this case, by using the existing codification system (for the analysis of the buildings in Raksila it has been used the same codification elaborated by the Municipality of Oulu in order to deliver to them an updated material easily consulted).</p> <p>When the acquisition of the information is finished the result is a digital archive which can be navigated and used for different types of purposes. Moreover, the software gives the possibility to recover all those cards, which have the same information or group of information. In the final part, they have been then put in mutual dialogue through the support of a 3D model realized on the base of the laser scanner.</p> <p>This step produced thematic maps and interesting consideration for the understanding of the place with its intrinsic dynamics.</p>
Supplementary Data for "Identification of Neighborhood Hotspots via the Cumulative Hazard Index: Results from a Community-Partnered Low-cost Sensor Deployment"
<p>These are the underlying data sets needed to build the kriging maps and calculate dissemination block cumulative hazard indices described in the paper. There are three data sets:</p> <ol> <li><strong>"Sampling location names and coordinates.csv"</strong>: locations and IDs of the low-cost sensors and the regulatory monitoring stations used in this work.<strong> [NOTE: </strong>latitudes and longitudes for the sensor deployments have been intentionally rounded to protect the location of volunteer sensor hosts.]</li> <li><strong>"Dissemination Block Populations.csv"</strong>: These are the relevant dissemination blocks in the study domain and their associated populations. This information was originally extracted from: https://censusmapper.ca/#13/49.2430/-123.1252</li> <li><strong>"Daily average concentrations by site and pollutant.csv"</strong>: This contains the PM2.5, NO2 and O3 daily averages for the entire study period across all low-cost sensor sites and regulatory monitoring stations. Refer to "Sampling location names and coordinates.csv" to parse the labels in this data set.</li> </ol> <p>There is also a sample code in Python to construct the kriging maps provided in 2 formats. <strong>[NOTE: </strong>we have intentionally excluded uploading the exact data sets imported by this code; our original data contains exact locations of sensor host volunteers and thus cannot be shared.]</p> <ol> <li><strong>"Jain et al - GeoHealth - Kriging Script.ipynb"</strong>: A Jupyter notebook script to import the data, build kriging maps, calculate CHIs, and export the data.</li> <li><strong>" Jain et al - GeoHealth - Kriging Script.pdf"</strong>: A PDF export of the Jupyter notebook so that you can read the Python scripts even if you are not a Jupyter notebooks user.</li> </ol>
Point-count bird censusing: bird abundance and diversity in CAP LTER Phoenix Area Social Survey neighborhoods throughout the greater Phoenix metropolitan area, 2006-2016
The Phoenix Area Social Survey (PASS) parallels the Ecological Survey of Central Arizona (formerly, Survey 200) as a long-term monitoring program of the CAP LTER. Every five years, the PASS research team surveys households in selected neighborhoods in the metropolitan Phoenix area to better understand perceptions, values, and behaviors of several key environmental issues, including water conservation, urban growth, air pollution, land conservation, biodiversity and urban climate change, as well as perceptions about their neighborhoods. The survey was piloted in 2001-2002 in eight neighborhoods in Phoenix with 302 respondents, and grew to over 40 neighborhoods and 800 households in 2005. Bird survey locations were established in each of the PASS neighborhoods, colocated as much as possible with the corresponding ESCA survey location in the neighborhood. Bird surveys were conducted biannualy (spring, winter) approximately the year of and the year after each PASS. In a given season, each bird survey location is visited independently by three birders who count all birds seen or heard within a 15-minute window.
Individually experienced temperatures: a heat exposure study in five greater Phoenix, AZ area neighborhoods (2014)
Urban environmental health hazards, including exposure to extreme heat, have become increasingly important to understand in light of ongoing climate change and urbanization. Most current knowledge about heat-health risks is based on measurements of outdoor air temperatures. Further, neighborhoods are often considered a homogenous and appropriate unit with which to assess risk and implement intervention strategies. Little is known about temperatures individuals actually experience within neighborhoods and cities, given differential access to cooling resources, complex activity patterns, and heterogeneous thermal and social environments. This dataset contains information collected during a study about individually experienced temperatures (IETs) within and between neighborhoods in Phoenix, Arizona. In September 2014, 80 research participants were recruited from 5 Phoenix-area neighborhoods and equipped with air temperature sensors that recorded IETs as they went about their daily lives. Surveys, activity log phone calls, and exit interviews were used to collect additional information from participants about demographics (age, race, gender), housing status, activities during the week, lifestyle, occupation, orientation toward the neighborhood, uses of indoor and outdoor spaces as well as public and private cooling resources. 86% of participants (69 out of 80) filled out background surveys, 89% of participants (71 out of 80) filled out daily surveys, 31% of participants (25 out of 80) engaged in activity log calls, and 48% of participants (39 out of 80) participated in exit interviews. The research team found that 1) variance in mean IET was relatively equal within each neighborhood and 2) significant differences existed in average mean IETs between neighborhoods. Data collected in this study help explain how intra-city differences in outdoor temperatures manifest themselves into IETs of urban residents. Individual differences are an overlooked determinant of heat expos
A quantitative analysis of the interplay of environment, neighborhood and cell state in 3D spheroids - Dataset
<p>This is the umbrella archive that contains all the datasets and code associated with:</p> <p><strong>A quantitative analysis of the interplay of environment, neighborhood, and cell state in 3D spheroids</strong></p> <p> Vito RT Zanotelli<br> Matthias Leutenegger<br> Xiao‐Kang Lun<br> Fanny Georgi<br> Natalie de Souza<br> Bernd Bodenmiller</p> <p><em>Mol Syst Biol. (2020) 16: e9798</em><br> <a href="https://doi.org/10.15252/msb.20209798">https://doi.org/10.15252/msb.20209798</a></p> <p><em>Please cite this article if you re-use any of the data or code.</em></p> <p>Datasets:</p> <ul> <li>Brightfield plate images: Contains all plate acquisitions for the individual sphere plates before pooling <ul> <li>4 cellline experiment: <ul> <li>p173: <a href="http://doi.org/10.5281/zenodo.3991929">10.5281/zenodo.3991929</a></li> <li>p176: <a href="http://doi.org/10.5281/zenodo.3991931">10.5281/zenodo.3991931</a></li> </ul> </li> <li>Overexpression experiment: <ul> <li>p161: <a href="http://doi.org/10.5281/zenodo.3991925">10.5281/zenodo.3991925</a></li> <li>p165: <a href="http://doi.org/10.5281/zenodo.3991927">10.5281/zenodo.3991927</a><br> </li> </ul> </li> </ul> </li> <li>Slidescan images: Contains all fluorescent slidescan images of the cuts derived from the pooled spheroid sample blocks <ul> <li>4 cellline experiment: <ul> <li>p173: <a href="http://doi.org/10.5281/zenodo.3991921">10.5281/zenodo.3991921</a></li> <li>p176: <a href="http://doi.org/10.5281/zenodo.3991923">10.5281/zenodo.3991923</a></li> </ul> </li> <li>Overexpression experiment: <ul> <li>p161: <a href="http://doi.org/10.5281/zenodo.4066430">10.5281/zenodo.4066430</a></li> <li>p165: <a href="http://doi.org/10.5281/zenodo.3991919">10.5281/zenodo.3991919</a><br> </li> </ul> </li> </ul> </li> <li>Spillover acquisitions: Contains all spillover acquisitions associated with the two experiments: <ul> <li>4 cellline experiment: <ul> <li>p173/p176: <a href="http://doi.org/10.5281/zenodo.3991945">10.5281/zenodo.3991945</a></li> </ul> </li> <li>Overexpression experiment: <ul> <li>p161/p165: <a href="http://doi.org/10.5281/zenodo.3991947">10.5281/zenodo.3991947</a><br> </li> </ul> </li> </ul> </li> <li>Imaging mass cytometry acquisitions: <ul> <li>4 cellline experiment: <ul> <li>p173: <a href="http://doi.org/10.5281/zenodo.3991937">10.5281/zenodo.3991937</a></li> <li>p176: <a href="http://doi.org/10.5281/zenodo.3991939">10.5281/zenodo.3991939</a></li> </ul> </li> <li>Overexpression experiment: <ul> <li>p161: <a href="http://doi.org/10.5281/zenodo.3991933">10.5281/zenodo.3991933</a></li> <li>p165: <a href="http://doi.org/10.5281/zenodo.3991935">10.5281/zenodo.3991935</a><br> </li> </ul> </li> </ul> </li> <li>Processed data set: <ul> <li>4 cellline experiment: <a href="https://doi.org/10.5281/zenodo.3991942">10.5281/zenodo.3991942</a></li> <li>Overexpression experiment: <a href="http://doi.org/10.5281/zenodo.4271917">10.5281/zenodo.4271917</a></li> </ul> </li> </ul> <p>Code:</p> <p>A snakemake pipeline to reproduce the analyses from this raw data can be found at: <a href="http://github.com/BodenmillerGroup/SpheroidPublication">http://github.com/BodenmillerGroup/SpheroidPublication</a></p> <p>A version already containing all the containers can be found at: <a href="http://doi.org/10.5281/zenodo.4071861">10.5281/zenodo.4071861</a></p> <p> </p>
Zero-Shot Information Extraction to Enhance a Knowledge Graph Describing Silk Textiles - English and Spanish neighborhood sub-graphs
<p>Two language-specific sub-graphs (English and Spanish) based on the ConceptNet Knowledge Graph. These two files are required to run the code for reproducing the results reported in the paper <a href="https://aclanthology.org/2021.latechclfl-1.16/">"Zero-Shot Information Extraction to Enhancea Knowledge Graph Describing Silk Textiles"</a> at the <a href="https://sighum.wordpress.com/events/latech-clfl-2021/">LaTeCH-CLfL 2021</a> workshop co-located with <a href="https://2021.emnlp.org/">EMNLP 2021</a>.</p>
Polio vaccine hesitancy in the networks and neighborhoods of Malegaon, India
<p>This dataset was created as part of our study on polio vaccine hesitancy in Malegaon, India. If using this dataset, please cite this data repository and the publication: Onnela JP, Landon BE, Kahn AL, Ahmed D, Verma H, O'Malley AJ, Bahl S, Sutter RW, Christakis NA. Polio vaccine hesitancy in the networks and neighborhoods of Malegaon, India. Soc Sci Med. 2016 Mar;153:99-106. doi: 10.1016/j.socscimed.2016.01.024. Epub 2016 Feb 4. PMID: 26889952.</p> <p> </p> <p><strong>A) Main data file data.csv</strong></p> <p>The files below can be constructed from this main file.</p> <p>Column: Variable (Value)<br> Col 0: Respondent ID (Integer)<br> Col 1: Data collection team ID (Integer)<br> Col 3: Vaccine status of household (1:Accepting, 2:Reluctant, 3:Refusing, 4:P0 House – no vaccine eligibles)<br> Col 12: Primary ID for Alter 1 – general questions (Integer)<br> Col 13: Duplicate ID for Alter 1 – general questions (Integer)<br> Col 14: Primary ID for Alter 2 – general questions (Integer)<br> Col 15: Duplicate ID for Alter 2 – general questions (Integer)<br> Col 16: Primary ID for Alter 3 – general questions (Integer)<br> Col 17: Duplicate ID for Alter 3 – general questions (Integer)<br> Col 18: Primary ID for Alter 4 – general questions (Integer)<br> Col 19: Duplicate ID for Alter 4 – general questions (Integer)<br> Col 48: Primary ID for Alter 1 if not household head – general questions (Integer)<br> Col 49: Duplicate ID for Alter 1 if not household head – general questions (Integer)<br> Col 50: Primary ID for Alter 2 if not household head – general questions (Integer)<br> Col 51: Duplicate ID for Alter 2 if not household head – general questions (Integer)<br> Col 52: Primary ID for Alter 3 if not household head – general questions (Integer)<br> Col 53: Duplicate ID for Alter 3 if not household head – general questions (Integer)<br> Col 54: Primary ID for Alter 4 if not household head – general questions (Integer)<br> Col 55: Duplicate ID for Alter 4 if not household head – general questions (Integer)<br> Col 56: Primary ID for Alter 1 – health questions (Integer)<br> Col 57: Duplicate ID for Alter 1 – health questions (Integer)<br> Col 58: Primary ID for Alter 2 – health questions (Integer)<br> Col 59: Duplicate ID for Alter 2 – health questions (Integer)<br> Col 60: Primary ID for Alter 3 – health questions (Integer)<br> Col 61: Duplicate ID for Alter 3 – health questions (Integer)<br> Col 62: Primary ID for Alter 4 – health questions (Integer)<br> Col 63: Duplicate ID for Alter 4 – health questions (Integer)<br> Col 92: Primary ID for Alter 1 if not household head – health questions (Integer)<br> Col 93: Duplicate ID for Alter 1 if not household head – health questions (Integer)<br> Col 94: Primary ID for Alter 2 if not household head – health questions (Integer)<br> Col 95: Duplicate ID for Alter 2 if not household head – health questions (Integer)<br> Col 96: Primary ID for Alter 3 if not household head – health questions (Integer)<br> Col 97: Duplicate ID for Alter 3 if not household head – health questions (Integer)<br> Col 98: Primary ID for Alter 4 if not household head – health questions (Integer)<br> Col 99: Duplicate ID for Alter 4 if not household head – health questions (Integer)<br> Col 101: Education (1:No school, 2:Primary school, 3:Middle school, 4:High school, 5:Intermediate diploma, 6:Graduate or post graduate, 7:Professional, 8:Islamic education)<br> Col 102: TV (1:Yes, 2:No)<br> Col 103: Phone (1:Yes, 2:No)<br> Col 104: Cooking cylinder (1:Yes, 2:No)<br> Col 105: Number of rooms (Integer)<br> Col 106: Toilet (1:Yes, 2:No)<br> Col 107: Number of people (Integer)</p> <p> </p> <p><strong>B) Derived network files (edge lists)</strong></p> <p>These directed network edge lists were derived from the main data file. Here N stands for number of nodes and L stands for number of edges.</p> <p>1) Nomination network (directed, general only): N=8161, L=7357; edgelist_dg.txt</p> <p>2) Nomination network (directed, health only): N=7223, L=5744; edgelist_dh.txt</p> <p>3) Nomination network (combined, directed): N=11828, L=11655; edgelist_d.txt</p> <p>4) Nomination network (combined, directed) LCC: N=6113, L=6647; edgelist_d_lcc.txt</p> <p>5) Vaccine network (directed): N=2428, L=1355; edgelist_g_vstatusok.txt</p> <p>6) Vaccine network (directed) LCC: N=710, L=813; edgelist_g_vstatusok_lcc.txt</p> <p> </p> <p><strong>C) Derived nodal attribute file final_node_data.csv</strong></p> <p>This nodal attribute file was derived from the main data file.</p> <p>Column: Variable (Value)<br> Col 0: Respondent ID (Integer)<br> Col 1: Data collection team ID (Integer)<br> Col 2: Vaccine status of household (1:Accepting, 2:Reluctant, 3:Refusing, 4:P0 House – no vaccine eligibles)<br> Col 3: In-degree – general questions (Integer)<br> Col 4: In-degree – health questions (Integer)<br> Col 5: In-degree – combined (Integer)<br> Col 6: Out-degree – general questions (Integer)<br> Col 7: Out-degree – health questions (Integer)<br> Col 8: Out-degree – combined (Integer)<br> Col 9: Education (1:No school, 2:Primary school, 3:Middle school, 4:High school, 5:Intermediate diploma, 6:Graduate or post graduate, 7:Professional, 8:Islamic education)<br> Col 10: TV (1:Yes, 2:No)<br> Col 11: Phone (1:Yes, 2:No)<br> Col 12: Cooking cylinder (1:Yes, 2:No)<br> Col 13: Number of rooms (Integer)<br> Col 14: Toilet (1:Yes, 2:No)<br> Col 15: Number of people (Integer)</p>
Temperature and Humidity in vulnerable neighborhoods in Madrid summer 2022 - summer 2023
<p>Data collection (temperature and humidity) in neighborhoods identified with vulnerability indicators in the context of the <strong>MateMAD project</strong>.</p> <p>The data has been collected by <strong>MonitorMAD team </strong>(UPM) through the installation of fixed sensors on streetlight poles in the city of Madrid. The monitored neighborhoods are:</p> <p>- Centro<br>- Delicias - Palos de la Frontera - Legazpi<br>- Orcasitas<br>- San Cristóbal<br>- San Diego<br>- Orcasur</p> <p>The monitoring campaign took place from August 2022 to October 2023 and the equipment installation follows the recommendations of World Meteorological Organization WMO. </p> <p>The equipments used for monitoring are from TandD Corporation, model RTR503BL. Due to exposure to outdoor conditions, the sensor has been protected by an insulated cade and self-ventilated through a photovoltaic-powered extractor. Weather data logging is programmed every 10 minutes, and the data is collected monthly. Furthermore, the data has been grouped by sensor reference. The temperature has been collected in degrees Celsius (accuracy ±0.3°C), and the relative humidity in percentage (accuracy ±5%RH at 25°C, 50%RH).</p> <p>Source of cartographic base: </p> <ul> <li>Ayuntamiento de Madrid. (s. f.). <em>Cartografía municipal por distritos a escala 1:1000, formato SHP, ETRS89</em> [Map]. Urbanismo e infraestructuras. Downloaded 30 de enero de 2023, de <a href="https://datos.madrid.es/portal/site/egob/menuitem.c05c1f754a33a9fbe4b2e4b284f1a5a0/?vgnextoid=a4f36d34fa86c410VgnVCM2000000c205a0aRCRD&vgnextchannel=374512b9ace9f310VgnVCM100000171f5a0aRCRD">https://datos.madrid.es/portal/site/egob/menuitem.c05c1f754a33a9fbe4b2e4b284f1a5a0/?vgnextoid=a4f36d34fa86c410VgnVCM2000000c205a0aRCRD&vgnextchannel=374512b9ace9f310VgnVCM100000171f5a0aRCRD</a></li> <li>Sede Electrónica del Catastro. (s. f.). <em>Difusión de datos catastrales</em> [Map]. Sede Electrónica del Catastro. Downloaded 30 de abril de 2023, de <a href="https://www.sedecatastro.gob.es/Accesos/SECAccDescargaDatos.aspx">https://www.sedecatastro.gob.es/Accesos/SECAccDescargaDatos.aspx</a></li> </ul>
Data for Seropositivity to Dengue virus DENV in three neighborhoods in the periphery of a city with a recent history of outbreaks in Argentina: what can we learn from unreported cases?
<p>This release include the datasets, R scripts and interactive maps generated for the manuscript: Seropositivity to Dengue virus DENV in three neighborhoods in the periphery of a city with a recent history of outbreaks in Argentina: what can we learn from unreported cases? Authors: DA Mendicino, T Ricardo, MA Cristaldi, M Maglianesi, G Guzmán S Claussen, RG Chiaraviglio, CA Ávalos, MA Previtali. (2024).</p>
3D neighborhood motion maps (From 6 to 26-neighborhoods)
<p>The file is a sqlite3 database which contains 3D neighborhood motions maps of together with sample points. It was computed while using a variation of the algorithm described in: Pluta K., Moroz G., Kenmochi Y., Romon P. (2016) Quadric Arrangement in Classifying Rigid Motions of a 3D Digital Image. In: Gerdt V., Koepf W., Seiler W., Vorozhtsov E. (eds) Computer Algebra in Scientific Computing. CASC 2016. Lecture Notes in Computer Science, vol 9890. Springer, doi:10.1007/978-3-319-45641-6_27</p> <p>To refer to a version of the algorithm used to compute the file use DOI:10.5281/zenodo.573013 (https://zenodo.org/badge/latestdoi/53963129).</p> <p>To view the content of the databases we refer to: https://github.com/copyme/NeighborhoodMotionMapsTools/tree/master/3DNMMViewerDB</p> <p>Note that in the files' names the prefix 'Nx' refers to different neighborhoods, i.e., N1 – 6-neighborhood, N2 – 18-neighborhood and N3 – 26-neighborhood.</p> <p>This is a consolidating submission for a purpose of a new publication.</p>
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.