3 datasets found
  1. Data from: Analysis of the Quantitative Impact of Social Networks General...

    • figshare.com
    • produccioncientifica.ucm.es
    doc
    Updated Oct 14, 2022
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    David Parra; Santiago Martínez Arias; Sergio Mena Muñoz (2022). Analysis of the Quantitative Impact of Social Networks General Data.doc [Dataset]. http://doi.org/10.6084/m9.figshare.21329421.v1
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    docAvailable download formats
    Dataset updated
    Oct 14, 2022
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    David Parra; Santiago Martínez Arias; Sergio Mena Muñoz
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    General data recollected for the studio " Analysis of the Quantitative Impact of Social Networks on Web Traffic of Cybermedia in the 27 Countries of the European Union". Four research questions are posed: what percentage of the total web traffic generated by cybermedia in the European Union comes from social networks? Is said percentage higher or lower than that provided through direct traffic and through the use of search engines via SEO positioning? Which social networks have a greater impact? And is there any degree of relationship between the specific weight of social networks in the web traffic of a cybermedia and circumstances such as the average duration of the user's visit, the number of page views or the bounce rate understood in its formal aspect of not performing any kind of interaction on the visited page beyond reading its content? To answer these questions, we have first proceeded to a selection of the cybermedia with the highest web traffic of the 27 countries that are currently part of the European Union after the United Kingdom left on December 31, 2020. In each nation we have selected five media using a combination of the global web traffic metrics provided by the tools Alexa (https://www.alexa.com/), which ceased to be operational on May 1, 2022, and SimilarWeb (https:// www.similarweb.com/). We have not used local metrics by country since the results obtained with these first two tools were sufficiently significant and our objective is not to establish a ranking of cybermedia by nation but to examine the relevance of social networks in their web traffic. In all cases, cybermedia whose property corresponds to a journalistic company have been selected, ruling out those belonging to telecommunications portals or service providers; in some cases they correspond to classic information companies (both newspapers and televisions) while in others they refer to digital natives, without this circumstance affecting the nature of the research proposed.
    Below we have proceeded to examine the web traffic data of said cybermedia. The period corresponding to the months of October, November and December 2021 and January, February and March 2022 has been selected. We believe that this six-month stretch allows possible one-time variations to be overcome for a month, reinforcing the precision of the data obtained. To secure this data, we have used the SimilarWeb tool, currently the most precise tool that exists when examining the web traffic of a portal, although it is limited to that coming from desktops and laptops, without taking into account those that come from mobile devices, currently impossible to determine with existing measurement tools on the market. It includes:

    Web traffic general data: average visit duration, pages per visit and bounce rate Web traffic origin by country Percentage of traffic generated from social media over total web traffic Distribution of web traffic generated from social networks Comparison of web traffic generated from social netwoks with direct and search procedures

  2. f

    Enhanced Missing Proteins Detection in NCI60 Cell Lines Using an Integrative...

    • acs.figshare.com
    • figshare.com
    xls
    Updated Jun 5, 2023
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    Elizabeth Guruceaga; Alba Garin-Muga; Gorka Prieto; Bartolomé Bejarano; Miguel Marcilla; Consuelo Marín-Vicente; Yasset Perez-Riverol; J. Ignacio Casal; Juan Antonio Vizcaíno; Fernando J. Corrales; Victor Segura (2023). Enhanced Missing Proteins Detection in NCI60 Cell Lines Using an Integrative Search Engine Approach [Dataset]. http://doi.org/10.1021/acs.jproteome.7b00388.s010
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    xlsAvailable download formats
    Dataset updated
    Jun 5, 2023
    Dataset provided by
    ACS Publications
    Authors
    Elizabeth Guruceaga; Alba Garin-Muga; Gorka Prieto; Bartolomé Bejarano; Miguel Marcilla; Consuelo Marín-Vicente; Yasset Perez-Riverol; J. Ignacio Casal; Juan Antonio Vizcaíno; Fernando J. Corrales; Victor Segura
    License

    Attribution-NonCommercial 4.0 (CC BY-NC 4.0)https://creativecommons.org/licenses/by-nc/4.0/
    License information was derived automatically

    Description

    The Human Proteome Project (HPP) aims deciphering the complete map of the human proteome. In the past few years, significant efforts of the HPP teams have been dedicated to the experimental detection of the missing proteins, which lack reliable mass spectrometry evidence of their existence. In this endeavor, an in depth analysis of shotgun experiments might represent a valuable resource to select a biological matrix in design validation experiments. In this work, we used all the proteomic experiments from the NCI60 cell lines and applied an integrative approach based on the results obtained from Comet, Mascot, OMSSA, and X!Tandem. This workflow benefits from the complementarity of these search engines to increase the proteome coverage. Five missing proteins C-HPP guidelines compliant were identified, although further validation is needed. Moreover, 165 missing proteins were detected with only one unique peptide, and their functional analysis supported their participation in cellular pathways as was also proposed in other studies. Finally, we performed a combined analysis of the gene expression levels and the proteomic identifications from the common cell lines between the NCI60 and the CCLE project to suggest alternatives for further validation of missing protein observations.

  3. Habitat point records from 1994 DWT Beer Head to Chesil Cove (Lyme Bay)...

    • ckan.publishing.service.gov.uk
    Updated Feb 4, 2016
    + more versions
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    ckan.publishing.service.gov.uk (2016). Habitat point records from 1994 DWT Beer Head to Chesil Cove (Lyme Bay) survey - Dataset - data.gov.uk [Dataset]. https://ckan.publishing.service.gov.uk/dataset/habitat-point-records-from-1994-dwt-beer-head-to-chesil-cove-lyme-bay-survey
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    Dataset updated
    Feb 4, 2016
    Dataset provided by
    CKANhttps://ckan.org/
    License

    Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
    License information was derived automatically

    Area covered
    Lyme Bay, Chiswell
    Description

    This report forms part of a series of studies describing the environment of Lyme Bay on the south of Dorset and Devon, UK. The studies were commissioned as a prerequisite to exploration drilling for oil and gas within the bay by Kerr-McGee Oil (UK) plc and Partners. The study has been managed by Ambios Environmental Consultants Ltd. A nationally agreed protocol for quantitative or semi-quantitative sampling of boulder and cobble shores does not currently exist. Recognised techniques for rocky shore monitoring are not appropriate as many of the invertebrates on boulder and cobble shores hide beneath the boulders. The method adopted form this survey utilised a 10 minute timed search close to the time of low water (Smith, 1982). During the 10 minutes the shore was surveyed from just above the high water mark to the low water mark. If the boulders extended over the whole shore the survey was completed in two parts (upper and lower shore) with a 10 minute search in each part. At sites where boulders were only present on part of the shore, the survey was restricted to that zone. Boulders on a strip approximately 5 metres wide were turned over, invertebrates recorded and the boulders replaced. Any other material (eg strand line and wood) that might act as a refuge for invertebrates was also examined. NUmbers of invertebrates were recorded as accurately as possible, but some groups such as amphipods (sand hoppers) occured in such large numbers that only an estimate was possible. This semi-quantitative technique has been shown to be reproducible and produces valid results (Smith, 1982). Abundance scales based on numbers per unit area are not appropriate for this type of timed survey. The following abundance scale was adopted: Number counted in 10 minutes: 0..........Absent 1-3........Present 4-10.......Frequent 11-30......Common 31-100.....Abundant >100.......Super Abundant The objective of this survey was to provide a baseline against which to monitor and change in the diversity of fauna living on and under cobbles and boulders in the eastern part of Lyme Bay. Five sites were chosen, from Charmouth in the west to Fortuneswell (on the Isle of Portland) in the east. A total of 32 taxa were recorded from these sites. The number of taxa recorded at each site ranged from 3-15. It is suggested that the site with the lowest number of taxa may be affected by disturbance from holidaymakers, and that another site with a low number of taxa may be affected by pollution. Some groups that would have been expected in reasonable numbers were either absent (eg oligocheate worms) or present at very low densities (eg mussels and nemertean worms). None of the species recoreded is scarce or rare. Analysis of the data from these sites suggests that they should all be classified as being of 'Local' importance in conservation terms, but in some cases inclusion of data from other habitats at the site will alter the classification.

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David Parra; Santiago Martínez Arias; Sergio Mena Muñoz (2022). Analysis of the Quantitative Impact of Social Networks General Data.doc [Dataset]. http://doi.org/10.6084/m9.figshare.21329421.v1
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Data from: Analysis of the Quantitative Impact of Social Networks General Data.doc

Related Article
Explore at:
docAvailable download formats
Dataset updated
Oct 14, 2022
Dataset provided by
Figsharehttp://figshare.com/
Authors
David Parra; Santiago Martínez Arias; Sergio Mena Muñoz
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description

General data recollected for the studio " Analysis of the Quantitative Impact of Social Networks on Web Traffic of Cybermedia in the 27 Countries of the European Union". Four research questions are posed: what percentage of the total web traffic generated by cybermedia in the European Union comes from social networks? Is said percentage higher or lower than that provided through direct traffic and through the use of search engines via SEO positioning? Which social networks have a greater impact? And is there any degree of relationship between the specific weight of social networks in the web traffic of a cybermedia and circumstances such as the average duration of the user's visit, the number of page views or the bounce rate understood in its formal aspect of not performing any kind of interaction on the visited page beyond reading its content? To answer these questions, we have first proceeded to a selection of the cybermedia with the highest web traffic of the 27 countries that are currently part of the European Union after the United Kingdom left on December 31, 2020. In each nation we have selected five media using a combination of the global web traffic metrics provided by the tools Alexa (https://www.alexa.com/), which ceased to be operational on May 1, 2022, and SimilarWeb (https:// www.similarweb.com/). We have not used local metrics by country since the results obtained with these first two tools were sufficiently significant and our objective is not to establish a ranking of cybermedia by nation but to examine the relevance of social networks in their web traffic. In all cases, cybermedia whose property corresponds to a journalistic company have been selected, ruling out those belonging to telecommunications portals or service providers; in some cases they correspond to classic information companies (both newspapers and televisions) while in others they refer to digital natives, without this circumstance affecting the nature of the research proposed.
Below we have proceeded to examine the web traffic data of said cybermedia. The period corresponding to the months of October, November and December 2021 and January, February and March 2022 has been selected. We believe that this six-month stretch allows possible one-time variations to be overcome for a month, reinforcing the precision of the data obtained. To secure this data, we have used the SimilarWeb tool, currently the most precise tool that exists when examining the web traffic of a portal, although it is limited to that coming from desktops and laptops, without taking into account those that come from mobile devices, currently impossible to determine with existing measurement tools on the market. It includes:

Web traffic general data: average visit duration, pages per visit and bounce rate Web traffic origin by country Percentage of traffic generated from social media over total web traffic Distribution of web traffic generated from social networks Comparison of web traffic generated from social netwoks with direct and search procedures

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