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188 results for “content analysis”
Content analysis of occupational definitions
<p>The methodological process followed for the competence assignment has considered two main phases. In a first phase, a taxonomic analysis has been carried out, identifying and systematizing the tasks included in around the 1.300 definitions of the two main occupational classification systems SOC-2018 and ISCO-2008. Once systematized, it has been assigned to key competences from the European Reference Framework on Key Competences for Lifelong Learning established by the European Commission (European Council, 2018). In a second phase, and on the basis of the results obtained, an analysis of competences has been developed. To make it possible, it has been established two indexes that measure to what extent a competence is relevant to an occupation, quantifying if such competence is present in all the tasks performed for such occupation or just in some or even if it is not (extension). The second index, specialization, measures to what extent a given competence is the most important of the tasks developed by an occupation, or if on the contrary, it is only one of the required competences.</p>
Campaign content analysis on X for the Castilla y León and Andalusia 2022 regional elections
<p>This dataset refers to the replication data of the article titled: "Could you teach new tricks to old dogs? An analysis of online communication of political leaders in Spanish regional elections", to be published in Frontiers in Political Sciece, after reviewing process.</p>
Fall 2000 soil organic content survey -- ash-free dry weight analysis for soil samples from 10 GCE LTER sampling sites
Soil core samples were collected from the permanent plots at 10 GCE LTER sampling sites in October, 2000, to survey the fractional organic content in marsh sediments. Surveys will be conducted annually to assess changes in soil organic content in response to environmental factors documented by other GCE monitoring efforts.
Mummichog (Fundulus heteroclitus) counts and gut content analysis from lift trap transect collections along Rowley River tidal creeks associated with long term fertilization experiments, Rowley, MA.
The lift traps were used to capture mummichogs accessing the high marsh platform. Mummichogs were collected to study the effect of marsh-edge geomorphology on mummichog distribution and foraging. The TIDE project aims to simulate eutrophication on a large scale by the addition of NO3- aiming to reach 70μM concentrations from May to September every year during the growing season. This fertilization of the marsh has been going on at Sweeney Creek since the 2004 growing season through 2016 and at Clubhead Creek in 2005 and from 2009 till 2016. Years 2017-2020 are enrichment recovery years.
Mummichog (Fundulus heteroclitus) gut content analysis from Breder trap transect collections in tidal creeks associated with long term fertilization experiments, Rowley, MA.
At PIE, mummichog (Fundulus heteroclitus) use the spring-cycle high tides to access the flooded high marsh platform and eat invertebrate prey, coupling the high marsh and aquatic creek food webs by gathering energy produced on the high marsh and making it available to the aquatic food web. Changes in the geomorphology of saltmarsh creek edges greatly influence the survival, biomass, and resource use of mummichog populations. Here we use gut content analysis assess the diet of mummichog on the high marsh platform during a flooding spring-cycle tide in July 2018 across 3 PIE creeks known to present different geomorphologic patterns in their low marsh zones. These data allow us to quantify the amount of terrestrial invertebrate prey mummichog consume on a single flooding tide and determine the impact altered low marsh geomorphology has on the trophic relationships in PIE food webs. These mummichog were captured in Breder traps; information about the consumer communities captured in these traps was recorded separately (LTE-TIDE-BrederTrap-Demographics). These data were included in part of the study “Habitat decoupling via saltmarsh creek geomorphology alters connection between spatially-coupled food webs” (Lesser et al. 2020) and were a portion of an MBL REU project.
Quantitative Content Analysis Data for Hand Labeling Road Surface Conditions in New York State Department of Transportation Camera Images
<p><strong>Foundational Codebook and Data: </strong></p> <p>Traffic camera images from the New York State Department of Transportation (511ny.org) are used to create a hand-labeled dataset of images classified into to one of six road surface conditions: 1) severe snow, 2) snow, 3) wet, 4) dry, 5) poor visibility, or 6) obstructed. Six labelers (authors Sutter, Wirz, Przybylo, Cains, Radford, and Evans) went through a series of four labeling trials where reliability across all six labelers were assessed using the Krippendorff’s alpha (KA) metric (Krippendorff, 2007). The online tool by Dr. Freelon (Freelon, 2013; Freelon, 2010) was used to calculate reliability metrics after each trial, and the group achieved inter-coder reliability with KA of 0.888 on the 4th trial. This process is known as quantitative content analysis, and three pieces of data used in this process are shared, including: 1) a PDF of the codebook which serves as a set of rules for labeling images, 2) images from each of the four labeling trials, including the use of New York State Mesonet weather observation data (Brotzge et al., 2020), and 3) an Excel spreadsheet including the calculated inter-coder reliability (ICR) metrics and other summaries used to asses reliability after each trial. The data are included in NYSDOT_quantitative_content_analysis.zip.</p> <p>The broader purpose of this work is that the six human labelers, after achieving inter-coder reliability, can then label large sets of images independently, each contributing to the creation of larger labeled dataset used for training supervised machine learning models to predict road surface conditions from camera images. The xCITE lab (xCITE, 2023) is used to store camera images from 511ny.org, and the lab provides computing resources for training machine learning models.</p> <p><strong>Obstructed Class Variation: </strong></p> <p>There are many applications for labeling roadside camera images, and as a variation of the foundational codebook, an addendum codebook provides another version of labeling the obstructed class. Specifically, this variation prioritizes labeling an image as “obstructed” only in extreme circumstances where there is a camera- or image- specific problem that prevents the assessment of any road surfaces. For labelers who want to use this version of the obstructed class (in this document) and also the other five weather-related classes (in the foundational codebook), the guidance is to use both documents in tandem, making sure to use the obstructed rules/definitions in this document while disregarding the obstructed rules/definitions in the foundational codebook. Alternatively, this codebook may be used alone in applications where the goal is to solely classify obstructed vs not obstructed. To ensure reliability and quality of this variation, quantitative content analysis was conducted on this addendum codebook, just as it was for the foundational codebook. Two labelers were tested with a sample of 30 images and achieved inter-coder reliability with Krippendorff's Alpha of 0.934 after one trial. The data, including the addendum codebook and labeling trial data (images and results) are included in ObstructedVariation_quantitative_content_analysis.zip.</p> <p>This material is based upon work supported by the U.S. National Science Foundation under Grant No. RISE-2019758.</p>
Assessing Quality Variations in Early Career Researchers' Data Management Plans: Quantitative Data of the Content Analysis
<p>The data includes the numerical results of the ranking of the data management plans created during the Basics of Research Data Management (BRDM) courses worth 3 ECTS credits in the years 2020 - 2022. The ranking was made using the Finnish DMP Evaluation Guidance (https://doi.org/10.5281/zenodo.4729831). Additionally, the data contains the results of the analysis of the best RDM practices included in the DMPs.</p> <p>Note 1: The comma-separated coded CSV version 1 (5.2.2024) may not open correctly on MacOS. You can use the comma-delimited CSV file version 2 or 3 (31.5.2024).</p> <p>Note 2: Versions 1 (Quality_variations_in_ECRs_DMPs_data) and 3 (Quality_variations_in_ECRs_DMPs_data_ver_3) contain evaluations of DMPs, best practices for data management, as well as methods for data sharing, storage, and preservation. In version 2 (Quality_variations_in_ECRs_DMPs_data_ver_2), the methods for data sharing, storage, and preservation are missing.</p> <p>Data is related to the research article https://doi.org/10.2218/ijdc.v18i1.873.</p>
Sources used for content analysis in Crespy, A. and Szabo, I. : Healthcare reforms and fiscal discipline in Europe: Responsibility or responsiveness? forthcoming: European Policy Analysis
<p>Sources used for content analysis in Crespy, A. and Szabo, I. : Healthcare reforms and fiscal discipline in Europe: Responsibility or responsiveness? forthcoming: European Policy Analysis. Data from the ENLIGHTEN project (H2020 #649456).</p>
All-sky information content analysis for novel passive microwave instruments - data
<p>This dataset is the underlying data for the article:</p> <p>Grützun, V., S. A. Buehler, L. Kluft, M. Brath, J. Mendrok, and P. Eriksson (in press, 2018), All-sky Information Content Analysis for Novel Passive Microwave Instruments in the Range from 23.8 GHz up to 874.4 GHz, Atmos. Meas. Tech., doi:10.5194/amt-2017-377. </p> <p>Please refer to that article for a description of the scientific background of the data and to the attached README file for a technical documentation. </p> <p>Contact: Verena Grützun, verena.gruetzun@uni-hamburg.de<br> </p>
Fed4Fire/CDN-X-ALL network metrics dataset for time series analysis in Media content delivery for 4G/5G networks
<p>The following dataset was generated at VICOMTECH (https://www.vicomtech.org) under project/experiment CDN-X-ALL: "CDN edge-cloud computing for efficient cache and reliable streaming aCROSS Aggregated unicast-multicast LinkS".</p> <p>Project funded by Fed4FIRE+ OC5 (<a href="https://www.fed4fire.eu/">https://www.fed4fire.eu</a>) under grant 732638.</p> <p>The dataset provides network metrics captures across several days employing a GStreamer-based MPEG-DASH player running on an UE connected to a LTE network.</p> <p>Nitos LTE/OpenAirInterface (OAI) testbed (<a href="https://nitlab.inf.uth.gr/NITlab/nitos/lte">https://nitlab.inf.uth.gr/NITlab/nitos/lte</a>) was used to deploy the LTE network.</p> <p><strong>CDN-like server/DASH Dataset -> Internet -> EPC/OAI -> eNodeB/OAI -> UE/DASH player</strong></p> <p>The player downloads MPEG-DASH video files provided by Distributed DASH dataset (<a href="https://dash.itec.aau.at/distributed-dash-datset/">https://dash.itec.aau.at/distributed-dash-datset/</a>), a dataset for CDN-like experiments, and captures the following data:</p> <ol> <li>Date: date when the data is collected</li> <li>Player: type of the player (in this case it is always "GStreamer")</li> <li>Num: identifier of the player</li> <li>URLVid: URL of the MPD file</li> <li>Latency: latency experienced by the player</li> <li>BW: bandwidth experienced by the player</li> <li>Quality: chosen DASH video representation</li> </ol> <p>During the experiments, other players run in order to generate realistic media streaming traffic at the CDN-like servers. These players start playing by following Poisson or Pareto distribution.</p> <p>The dataset was used to train Machine Learning Time Series predictor in order to forecast network capabilities and can be used for further experimentation concerning time series analysis.</p>
Analysis of the content of the H2020 project websites related to LEAs and IA.
<p>This is the dataset used in the article entitled "The disconnect between the goals of trustworthy AI for law enforcement and the EU research agenda". You can find more information about the results obtained, as well as the methodology used in the paper.</p>
Content Analysis of Canada's Science Based Departments and Agencies Open Science Action Plans
<p>Dataset and codebook for a content analysis of Science-based departments and agencies open science action plans in response to the Government of Canada's Roadmap for Open Science. </p>
Analysis of the contents of Equipment.data.ac.uk
<p>This data sample was downloaded from https://equipment.data.ac.uk/institutions for for data analysis purposes in my bachelor's thesis with the title "Equipment.data.ac.uk – The Linked Open Catalogue of Scientific Equipment" (in Czech "Otevřený propojený katalog vědeckých přístrojů Equipment.data.ac.uk"), that was submitted in 2023 at the Institute Information Studies and Librarianship, Charles University, Prague, Czech Republic.</p> <p>In the practical part, the bachelor's thesis (in Czech language) deals with the analysis of the data quality of the Equipment.data.ac.uk catalog in terms of respecting the UNIQUIP standard when entering data, for which the attached data and a PHP script are used.</p> <p>The ed_sample_2023-12-12.zip archive contains data sample mentioned above and complete folder stucture needed for PHP script "import.php":</p> <ul> <li>The root folder of the archive contains "import.php" file, which is PHP script used for data import and analysis, and "style.css", the CSS styles file for the HTML output generated by the script.</li> <li>The "data_c" folder contains downloaded data files in csv or json formats, used in analysis.</li> <li>The "defs" folder contains parametrisation for the import.php script: <ul> <li>definition of the UNIQUIP standard ("uniquip.csv") from https://equipment.data.ac.uk/uniquip with the following columns:<br> <ul> <li>column heading (name of the data field) [varchar]</li> <li>code (assigned by author of thesis to simplify the array index names) [varchar]</li> <li>gorf (group of required fields - assigned to number, which is unique for each group) [integer]</li> </ul> </li> <li>the conversion table from KitCat to UNIQUIP standard ("kitcat.csv") with the following columns: <ul> <li>column heading (name of the data field) [varchar]</li> <li>code (assigned by author of thesis to simplify the array index names) [varchar]</li> <li>gorf (group of required fields - assigned to number, which is unique for each group) [integer]</li> <li>kitcat (kitcat field - source field from Kitcat standard to be converted to the UNIQUIP field described by previous three columns) [varchar]</li> </ul> </li> </ul> </li> <li>The "institutions" folder contains "20231212.csv" file, which contains the table of institutions from https://equipment.data.ac.uk/institutions with these columns: <ul> <li>instituce = Name of the institution [varchar]</li> <li>ror = ROR identifier of the institution [varchar]</li> <li>záznamy = count of records stated in table (as a check of correct data import by script) [integer]</li> <li>typ = data standard ("Uniquip" or "Kitcat" - this controls the import method and file extension) [varchar]</li> <li>ignorovat = if this institution should be ignored by the import script by some reason ("t" if true) [char]</li> <li>poznámka = reason for the ignoring (in czech) [varchar]</li> </ul> </li> </ul> <p>When the import.php script is executed, it produces an HTML report with the following four tables, one per every test:</p> <ul> <li>VO1: Summary of non-empty required field groups. Tests, if required group of fields is present in records. Required field groups are defined in "uniquip.csv" table, column "gorf".</li> <li>VO2: Content validity summary. Tests, if content of selected fields is valid in the sense of context (email, url). PHP functions "FILTER_VALIDATE..." are used.</li> <li>VO3: Summary of taxonomy usage. Test, if records uses some taxonomy in the field "Technique".</li> <li>VO4: Summary of types of items. Tests, if field "Type" contains "equipment" or "facility" or other, non-supported value.</li> <li>Then, additional tables follow: <ul> <li>VO2-IVL: Listing of invalid values detected by VO2 test.</li> <li>INS: The Institutions table with counts of records in table and counts of records imported by the script.</li> </ul> </li> </ul> <p>For convenience, the complete html output of the script is present as "output.html" file in the root directory of the archive.</p> <p> </p> <p>Thesis citation:<br>FLOHR, Martin. Otevřený propojený katalog vědeckých přístrojů Equipment.data.ac.uk. Praha, 2023. Bakalářská práce. Univerzita Karlova, Filozofická fakulta, Ústav informačních studií a knihovnictví. Vedoucí bakalářské práce Dr. Jan Dvořák.</p>
F I G U R E 1 3 in Analysis of pigment cell composition, pigment content, tyrosinase content and activity of three kinds of loaches Misgurnus anguillicaudatus from Poyang Lake
F I G U R E 1 3 Tyrosinase content of three kinds of loaches (*means P <0.05, **means P <0.01). () BBL; () SBL; () NBL; () dorsal skin; () abdominal skin
F I G U R E 3 in Analysis of pigment cell composition, pigment content, tyrosinase content and activity of three kinds of loaches Misgurnus anguillicaudatus from Poyang Lake
F I G U R E 3 The distribution of skin pigment cells in abdomen of three kinds of loaches. (a) The abdominal epidermis of big blackspot loaches (BBL), (b) the abdominal epidermis of small blackspot loaches (SBL) and (c) the abdominal epidermis of non-blackspot loaches (NBL). The blue arrow refers to xanthophores. The magnification (a–c) is 80
F I G U R E 9 in Analysis of pigment cell composition, pigment content, tyrosinase content and activity of three kinds of loaches Misgurnus anguillicaudatus from Poyang Lake
F I G U R E 9 Ultrathin sections of skin of three kinds of loaches. (a) The dorsal skin of big blackspot loaches (BBL), (b) the dorsal skin of small blackspot loaches (SBL), (c) the dorsal skin of non-blackspot loaches (NBL), (d) the abdominal skin of BBL, (e) the abdominal skin of SBL and (f) the abdominal skin of NBL; N refers to nucleus in the six figures (a–f); S refers to stratum corneum in the six figures (a–f); (g) the dorsal skin of BBL, (h) the dorsal skin of SBL, (i) the dorsal skin of NBL, (j) the abdominal skin of BBL, (k) the abdominal skin of SBL, (l) the abdominal skin of NBL. The magnification of (a–f) is 1500 and the magnification of (g–l) is 6000. The green arrow indicates stage 1; the yellow arrow indicates stage 2; the red arrow indicates stage 3; the black arrow indicates stage 4
F I G U R E 1 1 in Analysis of pigment cell composition, pigment content, tyrosinase content and activity of three kinds of loaches Misgurnus anguillicaudatus from Poyang Lake
F I G U R E 1 1 Lutein content of three kinds of loaches (*means P <0.05, **means P <0.01). () BBL; () SBL; () NBL; () dorsal skin; () abdominal skin
F I G U R E 2 in Analysis of pigment cell composition, pigment content, tyrosinase content and activity of three kinds of loaches Misgurnus anguillicaudatus from Poyang Lake
F I G U R E 2 Distribution of pigment cells on the dorsal epidermis of three kinds of loaches. (a) The dorsal epidermis of big blackspot loaches (BBL), (b) the dorsal epidermis of small blackspot loaches (SBL), (c) the dorsal epidermis of non-blackspot loaches (NBL), (d) the magnification of the dorsal epidermis melanocytes of BBL, (e) the magnification of the dorsal epidermis melanocytes of SBL and (f) the magnification of the dorsal epidermis melanocytes of NBL. The black arrow refers to the first type of melanocytes, and the red arrow refers to the second type of melanocytes. The blue arrow refers to xanthophores. The magnification of (a–c) is 80. The magnification of d is 200. The magnification of (e–f) is 400
F I G U R E 8 in Analysis of pigment cell composition, pigment content, tyrosinase content and activity of three kinds of loaches Misgurnus anguillicaudatus from Poyang Lake
F I G U R E 8 Iridophores in skin of three kinds of loaches. (a) The dorsal skin of big blackspot loaches (BBL), (b) the dorsal skin of small blackspot loaches (SBL), (c) the dorsal skin of non-blackspot loaches (NBL), (d) the abdominal skin of BBL, (e) the abdominal skin of SBL and (f) the abdominal skin of NBL. The black arrow indicates iridophores
F I G U R E 7 in Analysis of pigment cell composition, pigment content, tyrosinase content and activity of three kinds of loaches Misgurnus anguillicaudatus from Poyang Lake
F I G U R E 7 Melanocyte in skin of three kinds of loaches. (a) The dorsal skin of big blackspot loaches (BBL), (b) the dorsal skin of small blackspot loaches (SBL), (c) the dorsal skin of non-blackspot loaches (NBL), (d) the abdominal skin of BBL, (e) the abdominal skin of SBL and (f) the abdominal skin of NBL. The black arrow indicates melanocytes
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.