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557 results for “data reporting”
Figure 3 in Data to the Heterocera (Insecta, Lepidoptera) fauna of East Kazakhstan: report on a summer expedition in 2018
Figure 3. Landscapes of collecting points in Eastern Kazakhstan: Markakol`, 29.VI.2018, photo by V.V. Ivonin.
Figure 4 in Data to the Heterocera (Insecta, Lepidoptera) fauna of East Kazakhstan: report on a summer expedition in 2018
Figure 4. Landscapes of collecting points in Eastern Kazakhstan: Buran, 30.VI.2018, photo by V.V. Ivonin.
Fig. 1 in Iphimeis dives (Coleoptera: Chrysomelidae): first report on Inga edulis (Fabaceae) in Brazil and data on its biology
Fig. 1. Iphimeis dives (Coleoptera: Chrysomelidae) and its damage on Inga edulis (Fabaceae) (A, B), mating (C), and eggs (D).
Data sets accompanying "Methodological and reporting inconsistencies in land-use requirements misguide future renewable energy planning"
<p>This data sets accompany the publication "Methodological and reporting inconsistencies in land-use requirements misguide future renewable energy planning" in One Earth.</p> <ul> <li>lur-db-output-zenodo.xlsx: contains all land use requirement estimates for renewable energies reviewed in the publication. Meta-data is reported in one sheet, the other sheet contains the original data.</li> <li>authorship-tree-zenodo.xlsx: contains the information necessary to derive the authorship tree shown in the supplementary information. Meta-data is reported in one sheet, the other sheet contains the original data.<br><br><br></li> </ul>
Robberies for cigarettes news reports 2009-2018 data
<p>Dataset used to analyze New Zealand news reports of robberies of stores for tobacco during 2009-2018. </p>
Text-fig. 4. Ratio diagrams of total length of p3–m3. Parasorex depereti from BRS 25 (data from Fanfani 1999), Parasorex depereti (data from Crochet 1986), Parasorex ibericus, type locality Otura-1, Spain (Mein and Martín-Suárez 1993), Parasorex pristinus (Ziegler 2003), Apulogalerix pusillus, Gargano (fissure filling F32), Italy (Masini and Fanfani 2013). On the horizontal axis are reported the element of the series, in ordinates the ratio of the average lengths on the standard Parasorex socialis from La Grive, France (data from Masini and Fanfani 2013). in New Light On Parasorex Depereti (Erinaceomorpha: Erinaceidae: Galericini) From The Late Messinian (Mn 13) Of The Monticino Quarry (Brisighella, Faenza, Italy)
Text-fig. 4. Ratio diagrams of total length of p3–m3. Parasorex depereti from BRS 25 (data from Fanfani 1999), Parasorex depereti (data from Crochet 1986), Parasorex ibericus, type locality Otura-1, Spain (Mein and Martín-Suárez 1993), Parasorex pristinus (Ziegler 2003), Apulogalerix pusillus, Gargano (fissure filling F32), Italy (Masini and Fanfani 2013). On the horizontal axis are reported the element of the series, in ordinates the ratio of the average lengths on the standard Parasorex socialis from La Grive, France (data from Masini and Fanfani 2013).
Use and sharing of raw data in the Journal Citation Reports' Emergency Medicine Category: Metrics and Journals including supplementary material classification sorted by quartile of the JCR emergency medicine category.
<p>Raw data belonged to the study of use and sharing of raw research data in the Journal Citation Reports' Emergency Medicine Category.</p>
Reporting behavior from WHO COVID-19 public data
<p><strong>Objective</strong></p> <p>Daily COVID-19 data reported by the World Health Organization (WHO) may provide the basis for political ad hoc decisions including travel restrictions. Data reported by countries, however, is heterogeneous and metrics to evaluate its quality are scarce. In this work, we analyzed COVID-19 case counts provided by WHO and developed tools to evaluate country-specific reporting behaviors.</p> <p><strong>Methods</strong></p> <p>In this retrospective cross-sectional study, COVID-19 data reported daily to WHO from 3rd January 2020 until 14th June 2021 were analyzed. We proposed the concepts of binary reporting rate and relative reporting behavior and performed descriptive analyses for all countries with these metrics. We developed a score to evaluate the consistency of incidence and binary reporting rates. Further, we performed spectral clustering of the binary reporting rate and relative reporting behavior to identify salient patterns in these metrics.</p> <p><strong>Results</strong></p> <p>Our final analysis included 222 countries and regions. Reporting scores varied between -0.17, indicating discrepancies between incidence and binary reporting rate, and 1.0 suggesting high consistency of these two metrics. Median reporting score for all countries was 0.71 (IQR 0.55 to 0.87). Descriptive analyses of the binary reporting rate and relative reporting behavior showed constant reporting with a slight "weekend effect" for most countries, while spectral clustering demonstrated that some countries had even more complex reporting patterns.</p> <p><strong>Conclusion</strong></p> <p>The majority of countries reported COVID-19 cases when they did have cases to report. The identification of a slight "weekend effect" suggests that COVID-19 case counts reported in the middle of the week may represent the best data basis for political ad hoc decisions. A few countries, however, showed unusual or highly irregular reporting that might require more careful interpretation. Our score system and cluster analyses might be applied by epidemiologists advising policymakers to consider country-specific reporting behaviors in political ad hoc decisions.</p>
Data for Measurement report: Air pollution emission factors of inland river ships under compliance with the 10 parts per million limit for sulfur content in fuel
<p>Since July 1, 2019, China’s domestic diesel fuel has been limited to 10 ppm of sulfur. Hence, to explore the applicability of the “sniffer” method and the distribution and level of inland river ships (IRSs) emission factors (EFs) under this limitation, we installed “sniffer” monitoring equipment, from August 2020 to June 2022, at the Gezhou Dam of the Yangtze River in China and monitored emissions from 8,238 IRSs in total passing through the lock. We partnered with the maritime department to select 100 ships passing through the lock to extract fuel oilsamples for direct fuel sulfur content detection, which determined the true fuel sulfur content of the passing ships. fuel sulfur content.</p> <p>The “sniffer” monitoring equipment included SO<sub>2</sub>, CO<sub>2</sub>, NO, and NO<sub>2</sub> gas sensors, PM<sub>2.5</sub> and PM<sub>10</sub> particulate matter sensors, as well as wind speed, wind direction, temperature, humidity, and pressure sensors.</p>
Code for the publication "DOSE - Global data set of reported sub-national economic output"
<p>The zipped file of this repository contains code and auxiliary data to reproduce the results of the publication:</p> <p>Wenz et al, "DOSE - Global data set of reported sub-national economic output"</p> <p>The respective DOSE database, version2 can be found here: <a href="http://doi.org/10.5281/zenodo.4681305">https://zenodo.org/record/7573249#.Y_RYTnbMI2w</a></p> <p>Please see the README document for descriptions of the required dependencies.</p>
Supporting publication for 'Prevalence sample-based guidance for reporting 2022 data'
<p>The record is aimed at helping the reporting countries to submit their sample-based level data to the EFSA Data Collection Framework. We include here two excel files and one XML file, and we give below specific information on their use.</p> <p>The two Excel documents help in mapping terms from the matrix catalogue ZOO_CAT_MATRIX used in the aggregated prevalence data model to FoodEx2 codes, and offer examples on how prevalence data can be reported using SSD2 and how data are aggregated afterwards. The XML file is the same example as in the Excel file with similar title but in the XML format that allows for it be uploaded in the Data Collection Framework.</p>
Data for "Measuring Back: Bibliodiversity and the Journal Impact Factor brand. A Case study of IF-journals included in the 2021 Journal Citations Report."
<p>This is the open data for the preprint "Measuring Back: Bibliodiversity and the Journal Impact Factor brand. A Case study of IF-journals included in the 2021 Journal Citations Report."</p>
Data Management Training Clearinghouse Metadata and Collection Statistics Report
<p>This collection contains a snapshot of the learning resource metadata from ESIP's <a href="https://dmtclearinghouse.esipfed.org">Data management Training Clearinghouse</a> (DMTC) associated with the closeout (March 30, 2023) of the Institute of Museum and Library Services funded (Award Number: <a href="https://imls.gov/grants/awarded/lg-70-18-0092-18">LG-70-18-0092-18</a>) <em>Development of an Enhanced and Expanded Data Management Training Clearinghouse project.</em> The shared metadata are a snapshot associated with the final reporting date for the project, and the associated data report is also based upon the same data snapshot on the same date.</p> <p>The materials included in the collection consist of the following:</p> <ul> <li><strong>esip-dev-02.edacnm.org.json.zip</strong> - a zip archive containing the metadata for 587 published learning resources as of March 30, 2023. These metadata include all publicly available metadata elements for the published learning resources with the exception of the metadata elements containing individual email addresses (submitter and contact) to reduce the exposure of these data.</li> <li><strong>statistics.pdf</strong> - an automatically generated report summarizing information about the collection of materials in the DMTC Clearinghouse, including both published and unpublished learning resources. This report includes the numbers of published and unpublished resources through time; the number of learning resources within subject categories and detailed subject categories, the dates items assigned to each category were first added to the Clearinghouse, and the most recent data that items were added to that category; the distribution of learning resources across target audiences; and the frequency of keywords within the learning resource collection. This report is based on the metadata for published resourced included in this collection, <strong>and</strong> preliminary metadata for unpublished learning resources that are not included in the shared dataset. </li> </ul> <p>The metadata fields consist of the following:</p> <table> <thead> <tr> <th scope="col">Fieldname</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>abstract_data</td> <td>A brief synopsis or abstract about the learning resource</td> </tr> <tr> <td>abstract_format</td> <td>Declaration for how the abstract description will be represented.</td> </tr> <tr> <td>access_conditions</td> <td>Conditions upon which the resource can be accessed beyond cost, e.g., login required.</td> </tr> <tr> <td>access_cost</td> <td>Yes or No choice stating whether othere is a fee for access to or use of the resource.</td> </tr> <tr> <td>accessibililty_features_name</td> <td>Content features of the resource, such as accessible media, alternatives and supported enhancements for accessibility.</td> </tr> <tr> <td>accessibililty_summary</td> <td>A human-readable summary of specific accessibility features or deficiencies.</td> </tr> <tr> <td>author_names</td> <td>List of authors for a resource derived from the given/first and family/last names of the personal author fields by the system</td> </tr> <tr> <td>author_org<br> - name<br> - name_identifier<br> - name_identifier_type</td> <td> <p><br> - Name of organization authoring the learning resource.<br> - The unique identifier for the organization authoring the resource.<br> - The identifier scheme associated with the unique identifier for the organization authoring the resource.</p> </td> </tr> <tr> <td> <p>authors<br> - givenName<br> - familyName<br> - name_identifier<br> - name_identifier_type</p> </td> <td> <p><br> - Given or first name of person(s) authoring the resource.<br> - Last or family name of person(s) authoring the resource.<br> - The unique identifier for the person(s) authoring the resource.<br> - The identifier scheme associated with the unique identifier for the person(s) authoring the resource, e.g., ORCID.</p> </td> </tr> <tr> <td>citation</td> <td>Preferred Form of Citation.</td> </tr> <tr> <td>completion_time</td> <td>Intended Time to Complete</td> </tr> <tr> <td> <p>contact<br> - name<br> - org<br> - email</p> </td> <td> <p><br> - Name of person(s) who has/have been asserted as the contact(s) for the resource in case of questions or follow-up by resource user.<br> - Name of organization that has/have been asserted as the contact(s) for the resource in case of questions or follow-up by resource user.<br> - (excluded) Contact email address.</p> </td> </tr> <tr> <td>contributor_orgs<br> - name<br> - name_identifier<br> - name_identifier_type<br> - type</td> <td>- Name of organization that is a secondary contributor to the learningresource. A contributor can also be an individual person.<br> - The unique identifier for the organization contributing to the resource.<br> - The identifier scheme associated with the unique identifier for the organization contributing to the resource.<br> - Type of contribution to the resource made by an organization.</td> </tr> <tr> <td>contributors<br> - familyName<br> - givenName<br> - name_identifier<br> - name_identifier_type</td> <td> <p>- Last or family name of person(s) contributing to the resource.<br> - Given or first name of person(s) contributing to the resource.<br> - The unique identifier for the person(s) contributing to the resource.<br> - The identifier scheme associated with the unique identifier for the person(s) contributing to the resource, e.g., ORCID.</p> </td> </tr> <tr> <td> <p>contributors.type</p> </td> <td> <p>Type of contribution to the resource made by a person.</p> </td> </tr> <tr> <td>created</td> <td>The date on which the metadata record was first saved as part of the input workflow.</td> </tr> <tr> <td>creator</td> <td>The name of the person creating the MD record for a resource.</td> </tr> <tr> <td>credential_status</td> <td>Declaration of whether a credential is offered for comopletion of the resource.</td> </tr> <tr> <td> <p>ed_frameworks<br> - name<br> - description<br> - nodes.name</p> </td> <td>- The name of the educational framework to which the resource is aligned, if any. An educational framework is a structured description of educational concepts such as a shared curriculum, syllabus or set of learning objectives, or a vocabulary for describing some other aspect of education such as educational levels or reading ability.<br> - A description of one or more subcategories of an educational framework to which a resource is associated.<br> - The name of a subcategory of an educational framework to which a resource is associated.</td> </tr> <tr> <td>expertise_level</td> <td>The skill level targeted for the topic being taught.</td> </tr> <tr> <td>id</td> <td>Unique identifier for the MD record generated by the system in UUID format.</td> </tr> <tr> <td>keywords</td> <td>Important phrases or words used to describe the resource.</td> </tr> <tr> <td>language_primary</td> <td>Original language in which the learning resource being described is published or made available.</td> </tr> <tr> <td>languages_secondary</td> <td>Additional languages in which the resource is tranlated or made available, if any.</td> </tr> <tr> <td>license</td> <td>A license for use of that applies to the resource, typically indicated by URL.</td> </tr> <tr> <td>locator_data</td> <td>The identifier for the learning resource used as part of a citation, if available.</td> </tr> <tr> <td>locator_type</td> <td>Designation of citation locatorr type, e.g., DOI, ARK, Handle.</td> </tr> <tr> <td>lr_outcomes</td> <td>Descriptions of what knowledge, skills or abilities students should learn from the resource.</td> </tr> <tr> <td>lr_type</td> <td>A characteristic that describes the predominant type or kind of learning resource.</td> </tr> <tr> <td>media_type</td> <td>Media type of resource.</td> </tr> <tr> <td>modification_date</td> <td>System generated date and time when MD record is modified.</td> </tr> <tr> <td>notes</td> <td>MD Record Input Notes</td> </tr> <tr> <td>pub_status</td> <td>Status of metadata record within the system, i.e., in-process, in-review, pre-pub-review, deprecate-request, deprecated or published.</td> </tr> <tr> <td>published</td> <td>Date of first broadcast / publication.</td> </tr> <tr> <td>publisher</td> <td>The organization credited with publishing or broadcasting the resource.</td> </tr> <tr> <td>purpose</td> <td>The purpose of the resource in the context of education; e.g., instruction, professional education, assessment.</td> </tr> <tr> <td>rating</td> <td>The aggregation of input from all user assessments evaluating users' reaction to the learning resource following Kirkpatrick's model of training evaluation.</td> </tr> <tr> <td>ratings</td> <td>Inputs from users assessing each user's reaction to the learning resource following Kirkpatrick's model of training evaluation.</td> </tr> <tr> <td>resource_modification_date</td> <td>Date in which the resource has last been modified from the original published or broadcast version.</td> </tr> <tr> <td>status</td> <td>System generated publication status of the resource w/in the registry as a yes for published or no for not published.</td> </tr> <tr> <td>subject</td> <td>Subject domain(s) toward which the resource is targeted. There may be more than one value for this field.</td> </tr> <tr> <td>submitter_email</td> <td>(excluded) Email address of person who submitted the resource.</td> </tr> <tr> <td>submitter_name</td> <td>Submission Contact Person</td> </tr> <tr> <td>target_audience</td> <td>Audience(s) for which the resource is intended.</td> </tr> <tr> <td>title</td> <td>The name of the resource.</td> </tr> <tr> <td>url</td> <td>URL that resolves to a downloadable version of the learning resource or to a landing page for the resource that contains important contextual information including the direct resolvable link to the resource, if applicable.</td> </tr> <tr> <td>usage_info</td> <td>Descriptive information about using the resource, not addressed by the License information field.</td> </tr> <tr> <td>version</td> <td>The specific version of the resource, if declared.</td> </tr> </tbody> </table> <p> </p>
Input data for the case study reported in "DREAM: an R package for druggability evaluation of human complex diseases".
<p>The data included in this record constituted the input for the case study reported in the manuscript "DREAM: an R package for druggability evaluation of human complex diseases", by Antonio Federico, Michele Fratello, Alisa Pavel, Lena Möbus, Giusy del Giudice, Angela Serra, Dario Greco. The data derive from transcriptomics experiments executed on lesional skin from atopic dermatitis patients and unaffected skin counterparts. The data consists of two files in ".txt" format reporting gene expression data in tabular format, where on the rows are reported genes and on the columns are reported samples. The data is an aggregated and batch-corrected collection of datasets originally downloaded by Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/). The file "GE_Mic_AD_Pamr_MAARS.txt" reports gene expression estimates of lesional skin of atopic dermatitis patients, while the file "GE_Mic_AD_Pamr_nl_MAARS.txt" reports gene expression estimates of non-lesional skin of atopic dermatitis patients.</p>
Data files for manuscript "Re-evaluation and Re-analysis of 152 research exomes five years after the initial report reveals clinically relevant changes in 18%"
<p>#2023-06-16<br> #Summary<br> This ZIP-file contains the data files used for all analyses for the manuscript "Re-evaluation and Re-analysis of 152 research exomes five years after the initial report reveals clinically relevant changes in 18%".</p> <p><br> #File structure<br> README.txt This README file.<br> File S02 ("FileS2_conNDD-cohort.xlsx") All variants identified by Reuter et al. previously with reevaluated variants and addition variants identified in this <br> project togetehr with information about the families, individuals, samplesand the BAM files assessed in this project.<br> File S03 ("FileS3_conNDD-variants.xlsx") All variant data analyzed from the cohort. Including a sheet with thresholdes for in silico predictions tools used to predict effect of variants, <br> a table with exome wide homozygous variants in 4 categories (A45, LGD, Missense, Splice), a table with exome wide variants in 4 categories (A45, LGD, Missense, Splice)<br> filtered for domiant genes associated with neurodevelopmental disorders in SysID (Prime and Candidate list), a table with exome wide variants in 4 categories (A45, LGD, Missense, Splice) filtered for recessive genes associated with neurodevelopmental disorders in SysID (Prime and Candidate list), a table withcopy number (CN) calls for the cohort and a table withcalls for runs of homozygosity (RoH) regions.</p> <p>#Files and checksums<br> 29c4b2f3dd8985d268f50dd3e0265798 ./FileS2_conNDD-cohort.xlsx<br> a054334637b8b22a9bf743db1e348663 ./FileS3_conNDD-variants.xlsx<br> </p>
Training Data for "Creating Quality FAIR assessment reports and draft of Data Papers from EML metadata with MetaShRIMPS"
<p>Training Data for "Training Data for "Creating Quality FAIR assessment reports and draft of Data Papers from EML metadata with MetaShRIMPS""</p>
Data from: Toward spatio-temporal models to support national-scale forest carbon monitoring and reporting
Open the record for dataset details and reuse information.
Data from: Brucite-inspired ocean alkalinity enhancement alters the biogeochemistry and composition of a phytoplankton community: A Santa Barbara channel case report
Open the record for dataset details and reuse information.
Reporting behavior from WHO COVID-19 public data
Open the record for dataset details and reuse information.
Tree Growth data taken at LTTG sites, 1969-Present: Reported Yearly
This data set contains tree growth data from factoral fertilized treatment plots set up as part of the Long Term Tree Growth research study. Selected trees were measured with diameter tapes each fall.
ScienceDex guides
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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.