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Using Trellis software to enhance high-quality large-scale network data collection in the field
Trellis is a mobile platform created by the Human Nature Lab at the Yale Institute for Network Science to collect high-quality, location-aware, off-line/online, multi-lingual, multi-relational social network and behavior data in hard-to-reach communities. Respondents use Trellis to identify their so...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8117970/ https://www.ncbi.nlm.nih.gov/pubmed/34219904 http://dx.doi.org/10.1016/j.socnet.2021.02.007 |
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author | Lungeanu, Alina McKnight, Mark Negron, Rennie Munar, Wolfgang Christakis, Nicholas A. Contractor, Noshir S. |
author_facet | Lungeanu, Alina McKnight, Mark Negron, Rennie Munar, Wolfgang Christakis, Nicholas A. Contractor, Noshir S. |
author_sort | Lungeanu, Alina |
collection | PubMed |
description | Trellis is a mobile platform created by the Human Nature Lab at the Yale Institute for Network Science to collect high-quality, location-aware, off-line/online, multi-lingual, multi-relational social network and behavior data in hard-to-reach communities. Respondents use Trellis to identify their social contacts by name and photograph, a procedure especially useful in low-literacy populations or in contexts where names may be similar or confusing. We use social network data collected from 1,969 adult respondents in two villages in Kenya to demonstrate Trellis’ ability to provide unprecedented metadata to monitor and report on the data collection process including artifactual variability based on surveyors, time of day, or location. |
format | Online Article Text |
id | pubmed-8117970 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-81179702021-07-01 Using Trellis software to enhance high-quality large-scale network data collection in the field Lungeanu, Alina McKnight, Mark Negron, Rennie Munar, Wolfgang Christakis, Nicholas A. Contractor, Noshir S. Soc Networks Article Trellis is a mobile platform created by the Human Nature Lab at the Yale Institute for Network Science to collect high-quality, location-aware, off-line/online, multi-lingual, multi-relational social network and behavior data in hard-to-reach communities. Respondents use Trellis to identify their social contacts by name and photograph, a procedure especially useful in low-literacy populations or in contexts where names may be similar or confusing. We use social network data collected from 1,969 adult respondents in two villages in Kenya to demonstrate Trellis’ ability to provide unprecedented metadata to monitor and report on the data collection process including artifactual variability based on surveyors, time of day, or location. Elsevier 2021-07 /pmc/articles/PMC8117970/ /pubmed/34219904 http://dx.doi.org/10.1016/j.socnet.2021.02.007 Text en © 2021 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Lungeanu, Alina McKnight, Mark Negron, Rennie Munar, Wolfgang Christakis, Nicholas A. Contractor, Noshir S. Using Trellis software to enhance high-quality large-scale network data collection in the field |
title | Using Trellis software to enhance high-quality large-scale network data collection in the field |
title_full | Using Trellis software to enhance high-quality large-scale network data collection in the field |
title_fullStr | Using Trellis software to enhance high-quality large-scale network data collection in the field |
title_full_unstemmed | Using Trellis software to enhance high-quality large-scale network data collection in the field |
title_short | Using Trellis software to enhance high-quality large-scale network data collection in the field |
title_sort | using trellis software to enhance high-quality large-scale network data collection in the field |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8117970/ https://www.ncbi.nlm.nih.gov/pubmed/34219904 http://dx.doi.org/10.1016/j.socnet.2021.02.007 |
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