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79 results for “Data definition”
Figure data and code used in Inconsistent definitions of GDP: Implications for estimates of decoupling
<p>Figure code in R and underlying data to reproduce all figures in the article "Inconsistent definitions of GDP: Implications for estimates of decoupling".</p>
Data for "A Quantum Definition of Molecular Structure"
<p>Supplemental data for our article "A Quantum Definition of Molecular Structure".</p><p>Version 1.1.0 contains data for additional k-medoids runs performed on different subsets of the complete sample.</p>
Definitions of terms extracted from data-related European Union laws, version 3
<p>Collection of definitions of terms in English, French, German, Italian and Spanish extracted from the following data-related European laws:</p> <ol> <li> <p><a href="http://data.europa.eu/eli/dir/2007/2/oj?locale=en">Directive 2007/2/EC of the European Parliament and of the Council of 14 March 2007 establishing an Infrastructure for Spatial Information in the European Community (<strong>INSPIRE</strong>)</a></p> </li> <li> <p><a href="http://data.europa.eu/eli/reg/2016/679/2016-05-04?locale=en">Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (<strong>General Data Protection Regulation</strong>) (Text with EEA relevance)</a></p> </li> <li> <p><a href="https://eur-lex.europa.eu/eli/reco/2018/790/oj?locale=en">Commission Recommendation (EU) 2018/790 of 25 April 2018 on <strong>access to and preservation of scientific information</strong></a></p> </li> <li> <p><a href="https://eur-lex.europa.eu/eli/reg/2018/1807/oj?locale=en">Regulation (EU) 2018/1807 of the European Parliament and of the Council of 14 November 2018 on a framework for the <strong>free flow of non-personal data</strong> in the European Union (Text with EEA relevance)</a></p> </li> <li> <p><a href="https://data.europa.eu/eli/dir/2019/790/oj?locale=en">Directive (EU) 2019/790 of the European Parliament and of the Council of 17 April 2019 on <strong>copyright and related rights in the Digital Single Market</strong> and amending Directives 96/9/EC and 2001/29/EC (Text with EEA relevance)</a></p> </li> <li> <p><a href="https://eur-lex.europa.eu/eli/dir/2019/1024/oj?locale=en">Directive (EU) 2019/1024 of the European Parliament and of the Council of 20 June 2019 on open data and the re-use of public sector information (recast) (<strong>Open Data Directive</strong>)</a></p> </li> <li><a href="https://eur-lex.europa.eu/eli/reg/2021/695/oj?locale=en">Regulation (EU) 2021/695 of the European Parliament and of the Council of 28 April 2021 establishing <strong>Horizon Europe</strong> – the Framework Programme for Research and Innovation, laying down its rules for participation and dissemination, and repealing Regulations (EU) No 1290/2013 and (EU) No 1291/2013 (Text with EEA relevance)</a></li> <li> <p><a href="https://eur-lex.europa.eu/eli/reg/2022/868/oj?locale=en">Regulation (EU) 2022/868 of the European Parliament and of the Council of 30 May 2022 on European data governance and amending Regulation (EU) 2018/1724 (<strong>Data Governance Act</strong>) (Text with EEA relevance)</a></p> </li> <li> <p><a href="https://eur-lex.europa.eu/eli/reg/2022/1925/oj?locale=en">Regulation (EU) 2022/1925 of the European Parliament and of the Council of 14 September 2022 on contestable and fair markets in the digital sector and amending Directives (EU) 2019/1937 and (EU) 2020/1828 (<strong>Digital Markets Act</strong>) (Text with EEA relevance)</a></p> </li> <li> <p><a href="https://eur-lex.europa.eu/eli/reg/2022/2065/oj?locale=en">Regulation (EU) 2022/2065 of the European Parliament and of the Council of 19 October 2022 on a Single Market For Digital Services and amending Directive 2000/31/EC (<strong>Digital Services Act</strong>) (Text with EEA relevance)</a></p> </li> <li> <p><a href="https://eur-lex.europa.eu/eli/reg_impl/2023/138/oj?locale=en">Commission Implementing Regulation (EU) 2023/138 of 21 December 2022 laying down a list of specific <strong>high-value datasets</strong> and the arrangements for their publication and re-use (Text with EEA relevance)</a></p> </li> <li> <p><a href="https://eur-lex.europa.eu/eli/reg/2023/2854/oj?locale=en">Regulation (EU) 2023/2854 of the European Parliament and of the Council of 13 December 2023 on harmonised rules on fair access to and use of data and amending Regulation (EU) 2017/2394 and Directive (EU) 2020/1828 (<strong>Data Act</strong>)</a></p> </li> <li> <p><a href="https://eur-lex.europa.eu/eli/reg/2024/903/oj?locale=en">Regulation (EU) 2024/903 of the European Parliament and of the Council of 13 March 2024 laying down measures for a high level of public sector interoperability across the Union (<strong>Interoperable Europe Act</strong>)</a></p> </li> <li> <p><a href="https://eur-lex.europa.eu/eli/reg/2024/1689/oj?locale=en">Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence and amending Regulations (EC) No 300/2008, (EU) No 167/2013, (EU) No 168/2013, (EU) 2018/858, (EU) 2018/1139 and (EU) 2019/2144 and Directives 2014/90/EU, (EU) 2016/797 and (EU) 2020/1828 (<strong>Artificial Intelligence Act</strong>) Text with EEA relevance.</a></p> </li> <li><a href="https://eur-lex.europa.eu/eli/reg/2024/2847/oj?locale=en">Regulation (EU) 2024/2847 of the European Parliament and of the Council of 23 October 2024 on horizontal cybersecurity requirements for products with digital elements and amending Regulations (EU) No 168/2013 and (EU) 2019/1020 and Directive (EU) 2020/1828 (<strong>Cyber Resilience Act</strong>) (Text with EEA relevance)</a></li> </ol>
Pond data: physical, chemical, and biological characteristics with scientific and United States of America state definitions from literature and legislative surveys
Ponds are often identified by their small size and shallow depths, but the lack of a universal definition hampers science and weakens legal protection. In order to determine a working definition of ‘pond’, we conducted a literature search for scientific definitions, a U.S. state survey for management definitions, and looked at pond ecosystem function using data from the literature search. Our dataset includes physical, chemical, and biological data for 1327 waterbodies ≤ 20 ha in surface area and ≤ 9 m in maximum or mean depth from our literature review. These data have a global distribution, we include a table of latitudes and longitudes, and span many years (1946-2019). We have also included a table of 54 pond definitions from the literature review and a table of U.S. state definitions of ponds, wetlands, and lakes resulting from our survey.
Data and code associated with "Evaluating the definition and distribution of spring ephemeral wildflowers in eastern North America"
<p>Data and code associated with a paper by Yancy et al titled "Evaluating the definition and distribution of spring ephemeral wildflowers in eastern North America". Metadata is included in files when possible.</p>
BRAIN Journal-Auto-generative Learning Objects in Online Assessment of Data Structures Disciplines-Figure 3. Symbols definition for a graph-based test
<p>In this scenario, we intend to generate a random graph and compute a deep first-search node list. The first defined random symbol is n, namely the number of nodes in the graph as an integer from 5 to 9. The next symbol is named g and denotes the graph object created randomly using 3 parameters: the number of nodes, the minimum, and the maximum value for the weight. For the number of nodes, we used the previously computed value of n, whereas for the weights, we used two constants 0 and 1 since the graph is not weighted</p>
BRAIN Journal-Auto-generative Learning Objects in Online Assessment of Data Structures Disciplines-Figure 2. Auto-generative Learning Object Model Definition
<p>In this section, we will present the structure of AGLOs in the context of our approach. The AGLO meta-model is structured in XML as in Figure 2,a refinement from Chirila, Ciocarlie, and Stoicu (2015). The AGLO definition contains several sections like name, scenario, theory, question, answers, and feedback (line 01). The name element contains the name of the AGLO, possibly a small description in the human language (line 02). The section of the scenario (line 03) contains a comment (line 04) followed by a set of symbol definitions. The comment should describe the imagined scenario in details and it has the same role as code comments. The symbol is the central element of the AGLO model. The symbol has a name and is very similar to programming language variables. Symbols may be called also parameters since they control the content of the AGLO content in the process of instantiation. </p>
Data from: Automatic Definition of Robust Microbiome Sub-states in Longitudinal Data
<p>Output files of the application of our R software (available at https://github.com/wilkinsonlab/robust-clustering-metagenomics) to different microbiome datasets already published.</p> <p>Prefixes:</p> <ul> <li>David2014_: original microbiome dataset published in [David et al.,2014] (http://genomebiology.com/2014/15/7/R89)</li> <li>Ballou2016_: original microbiome dataset published in [Ballou et al.,2016] (http://journal.frontiersin.org/article/10.3389/fvets.2016.00002/full)</li> <li>Gajer2012_: original microbiome dataset published in [Gajer et al.,2012] (http://stm.sciencemag.org/content/4/132/132ra52.long)</li> <li>LaRosa2014_: original microbiome dataset published in [LaRosa et al.,2014] (http://www.pnas.org/cgi/doi/10.1073/pnas.1409497111)</li> </ul> <p>Suffixes:</p> <ul> <li> <p>_All: all taxa</p> </li> <li> <p>_Dominant: only 1% most abundant taxa</p> </li> <li> <p>_NonDominant: remaining taxa after removing above dominant taxa</p> </li> <li> <p>_GenusAll: taxa aggregated at genus level</p> </li> <li> <p>_GenusDominant: taxa aggregated at genes level and then to select only 1% most abundant taxa</p> </li> <li> <p>_GenusNonDominant: taxa aggregated at genus level and then to remove 1% most abundant taxa</p> </li> </ul> <p>Each folder contains 3 output files related to the same input dataset:<br> - data.normAndDist_definitiveClustering_XXX.RData: R data file with a) a phyloseq object (including OTU table, meta-data and cluster assigned to each sample); and b) a distance matrix object.<br> - definitiveClusteringResults_XXX.txt: text file with assessment measures of the selected clustering.<br> - sampleId-cluster_pairs_XXX.txt: text file. Two columns, comma separated file: sampleID,clusterID</p> <p>Abstract of the associated paper:</p> <p>The analysis of microbiome dynamics would allow us to elucidate patterns within microbial community evolution; however, microbiome state-transition dynamics have been scarcely studied. This is in part because a necessary first-step in such analyses has not been well-defined: how to deterministically describe a microbiome's "state". Clustering in states have been widely studied, although no standard has been concluded yet. We propose a generic, domain-independent and automatic procedure to determine a reliable set of microbiome sub-states within a specific dataset, and with respect to the conditions of the study. The robustness of sub-state identification is established by the combination of diverse techniques for stable cluster verification. We reuse four distinct longitudinal microbiome datasets to demonstrate the broad applicability of our method, analysing results with different taxa subset allowing to adjust it depending on the application goal, and showing that the methodology provides a set of robust sub-states to examine in downstream studies about dynamics in microbiome.</p> <p> </p>
Data from: Robust and Automatic Definition of Microbiome States
<p>Output files of the application of our R software (available at https://github.com/wilkinsonlab/robust-clustering-metagenomics) to different microbiome dataset already published.</p> <p>Prefixes:<br> * David2014_: original microbiome dataset published in [David et al.,2014] (http://genomebiology.com/2014/15/7/R89)<br> * Ballou2016_: original microbiome dataset published in [Ballou et al.,2016] (http://journal.frontiersin.org/article/10.3389/fvets.2016.00002/full)<br> * Gajer2012_: original microbiome dataset published in [Gajer et al.,2012] (http://stm.sciencemag.org/content/4/132/132ra52.long)<br> * LaRosa2014_: original microbiome dataset published in [LaRosa et al.,2014] (http://www.pnas.org/cgi/doi/10.1073/pnas.1409497111)<br> * Dam2016_: original microbiome dataset published in [Dam et al.,2016] (https://www.nature.com/articles/npjsba20167)<br> * Caporaso[Lpalm|Rpalm|Tongue]_: original microbiome dataset published in [Caporaso et al.,2011] (https://genomebiology.biomedcentral.com/articles/10.1186/gb-2011-12-5-r50)<br> * Ravel2011_: original microbiome dataset published in [Ravel et al.,2011] (http://www.pnas.org/content/108/Supplement_1/4680)</p> <p>Sufixes:<br> _All: all taxa<br> _Dominant: only 1% most abundant taxa<br> _NonDominant: remaining taxa after removing above dominant taxa<br> _GenusAll: taxa aggregated at genus level<br> _GenusDominant: taxa aggregated at genes level and then to select only 1% most abundant taxa<br> _GenusNonDominant: taxa aggregated at genus level and then to remove 1% most abundant taxa</p> <p><br> Each folder contains the following output files related to the same input dataset:<br> - data.normAndDist_definitiveClustering_XXX.RData: R data file with a) a phyloseq object (including OTU table, meta-data and cluster assigned to each sample); and b) a distance matrix object.<br> - definitiveClusteringResults_XXX.txt: text file with assessment measures of the selected clustering.<br> - sampleId-cluster_pairs_XXX.txt: text file. Two columns, comma separated file: sampleID,clusterID<br> - robustClustering_allTogether_formatted.pdf: graph file, with the results of the robust clustering assessment.<br> - pcoa_definitiveClustering_X_kY_colorByCluster.pdf: graph file, with samples represented in Principal COordinate Analysis, with different point color associated to the assigned cluster.<br> - statesSequence_XXX.pdf (if longitudinal data): graph file, a time series diagram representing the sequence of states over time per subject.</p> <p> </p>
Urheberrechtsgesetz (UrhG): software and data related definitions depicted using UML class modeling — Release 10
<p>Diagram depicting definitions and conceptual relationships within the German Copyright and Related Rights Act or Urheberrechtsgesetz (UrhG) up to and including the amendments of 28 November 2018. The diagram focuses on software and data related definitions and presents these using Unified Modeling Language (UML) class modeling. Please contact the author is you require the underlying Inkscape SVG vector art.</p>
Fig. 6 in New Stratigraphic Data from the Erlian Basin: Implications for the Division, Correlation, and Definition of Paleogene Lithological Units in Nei Mongol (Inner Mongolia)
Fig. 6. Sketch profiles from (above) Camp Margetts and (below) 7 miles southwest (235°) of Camp Margetts (Granger, 1930).
Fig. 4 in New Stratigraphic Data from the Erlian Basin: Implications for the Division, Correlation, and Definition of Paleogene Lithological Units in Nei Mongol (Inner Mongolia)
Fig. 4. Above, The Arshanto Formaiton (middle beds) and Irdin Manha Formation (upper beds) at the Nuhetingboerhe locality; the arrow points to the upper hiatus between the two formatitons. Below, Close-up view of the upper hiatus showing the lithology and erosional surface.
Fig. 3 in New Stratigraphic Data from the Erlian Basin: Implications for the Division, Correlation, and Definition of Paleogene Lithological Units in Nei Mongol (Inner Mongolia)
Fig. 3. Above, Beds at the transition of the Nomogen (lower) and Arshanto (upper) at the Huheboerhe locality. Below, Close-up view of the lower hiatus showing the lithology and erosional surface between the Nomogen and Arshanto fomarions.
Fig. 2 in New Stratigraphic Data from the Erlian Basin: Implications for the Division, Correlation, and Definition of Paleogene Lithological Units in Nei Mongol (Inner Mongolia)
Fig. 2. Above, Paleogene outcrops at the Nuhetingboerhe locality. The arrow points to the lower hiatus between the Nomogen (lower) and Arshanto (upper) formations; Below, Close-up view of the lower hiatus showing the lithology and erosional surface.
Fig. 1. A in New Stratigraphic Data from the Erlian Basin: Implications for the Division, Correlation, and Definition of Paleogene Lithological Units in Nei Mongol (Inner Mongolia)
Fig. 1. A. Localities (black dots) of the Erlian Basin, Nei Mongol (Inner Mongolia). The shaded area indicates the location of the Nuhetingboerhe-Huheboerhe area (NHA). B. Topographic map of NHA. The black bars indicate positions of measured sections at the Daoteyin Obo, Nuhetingboerhe, Wulanboerhe (Huheboerhe of Meng et al., 2004), and Huheboerhe.
Text-fig. 10. Phylogenetic relationships of Miocene hyaenodonts (for definitions of character states see Table 2). The data matrix was compiled in MacClade 4.05 and run in PAUP 4.0b10 (Macintosh version). We chose Cimolestes magnus CLEMENS et RUSSELL, 1965, (additional data from Lillegraven 1969), as the outgroup. The unordered and unweighted analysis produced 16 trees. a: Majority-rule consensus. b: Strict consensus. Consistency index (CI): 0.5882; Homoplasy index (HI): 0.4118; Retention index (RI): 0.7742. in New Hyaenodonts (Ferae, Mammalia) From The Early Miocene Of Napak (Uganda), Koru (Kenya) And Grillental (Namibia)
Text-fig. 10. Phylogenetic relationships of Miocene hyaenodonts (for definitions of character states see Table 2). The data matrix was compiled in MacClade 4.05 and run in PAUP 4.0b10 (Macintosh version). We chose Cimolestes magnus CLEMENS et RUSSELL, 1965, (additional data from Lillegraven 1969), as the outgroup. The unordered and unweighted analysis produced 16 trees. a: Majority-rule consensus. b: Strict consensus. Consistency index (CI): 0.5882; Homoplasy index (HI): 0.4118; Retention index (RI): 0.7742.
BIO4AFRICA_Co-definition of technologies to be transferred with local farmers and communities - Technology Validation Data (Findings)_D1.4_021122_1
<p>Validation Data (Findings) for Task 1.4: Co-definition of technologies to be transferred with local farmers and communities.</p>
BIO4AFRICA_Co-definition of technologies to be transferred with local farmers and communities - Technology Validation Data (Demographics)_D1.4_311022_1
<p>Validation Data (Demographics) for Task 1.4: Co-definition of technologies to be transferred with local farmers and communities.</p>
Data used in the definition of Harmful Algal Bloom hotspot for Northern Indian Ocean
<p>The zipped rar file contain raw data used in the harmful algal bloom hotspot analysis for the Northern Indian Ocean. Annual_MK_full folder gives the data on the annual Mann kendal analysis of the chlorophyll data from satellite data. Boreal_season_full folder gives data on seasonally calculated chlorophyll concentration from the Northern Indian ocean. Monthly_MK folder gives the monthly averaged chlorophyll data. The chlorophyll data is further grouped into two; (1) Chlorophyll concentration greater than 4 mg/m^3 and (2) Chlorophyll concentration greater than 10 mg/m^3.</p> <p> </p>
Definitive dataset framework of the Data Analysis on Sutri, HISMAGIS protocol.
<p>Definitive dataset framework of the Data Analysis on Sutri, HISMAGIS protocol.</p> <p>All the feature class, geodatabase and collector carpets have been translated in English.</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.