Nanopublications

Matches in Nanopublications for { ?s <http://purl.org/dc/terms/description> ?o ?g. }

Showing items 2,201 to 2,300 of ±2,453 with 100 items per page.

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assertion description "Such a nanopublication defines a new class. Classes represent sets of concrete or abstract things, and are by convention named with singular nouns (or noun phrases) like 'human', 'cardiovascular disease', or 'approach'. If the term you want to define does not refer to a set of things but a single instance, such as the planet Mars or Marie Curie, define them with the template for individuals instead." assertion.
assertion description "Such a nanopublication expresses a relation of synonymy between two organism taxons." assertion.
assertion description "Such a nanopublication expresses a relation of synonymy between two organism taxons." assertion.
assertion description "Such a nanopublication expresses an association between an organism taxon and a nucleotide sequence (that can correspond to a gene or subunit of a gene), for example expressing that Sequence ID (INSDC / Genbank accession number or BOLD Process ID) identifies a given taxon concept." assertion.
assertion description "Such a nanopublication expresses an association between two organism instances (subject and object), for example expressing that one subject individual ate the object individual. For expressing interactions or more general knowledge between classes of organisms (taxa), please use the alternative template "Associations between taxa"." assertion.
assertion description "Such a nanopublication expresses an association between an organism (normally identified as belonging to a certain taxon concept) and a nucleotide sequence (that can correspond to a gene or subunit of a gene), for example expressing that Sequence ID (INSDC / Genbank accession number or BOLD Process ID) identifies Organism X as belonging to a Taxon Concept Y." assertion.
assertion description "Such a nanopublication expresses an association between an organism instance and an environment, expressing that the given organism was observed in the specified type of environment." assertion.
assertion description "Such a nanopublication expresses an association between classes of organisms (taxa), a subject axon and an object taxon, for example expressing that the organisms of the subject taxon prey on organisms of the object taxon. For expressing observations of interactions between individual organisms, please use the alternative template "Associations between organisms"." assertion.
assertion description "Such a nanopublication expresses an association between an organism taxon (e.g. a species) and an environment, for example expressing that the organisms of the given species inhabit the specified environment." assertion.
assertion description "Such a nanopublication includes a text that refers to an existing paper or other document, such as stating an agreement or a correction. The reference to the existing paper is based on the <a href="https://sparontologies.github.io/cito/current/cito.html" target="_blank">Citation Typing Ontology (CiTO)</a>." assertion.
assertion description "Such nanopublications express biology-related relations, such as that a chemical can treat a disease or that a given type of protein interacts with another one. The relations of this template are based on the <a href="https://biolink.github.io/biolink-model/docs/">Biolink model</a>." assertion.
assertion description "Such nanopublications express a causal (e.g. "A causes B"), spatio-temporal (e.g. "A is included in B"), or comparative (e.g. "A is larger than B") relation between the instances of two classes. The subject and object are classes that are connected by the fact that their instances tend to have a particular relation. The aspect of "tending to" is left underspecified; for more precise statements, use the super-pattern template instead." assertion.
assertion description "Such nanopublications express a causal (e.g. "A caused B"), spatio-temporal (e.g. "A is included in B"), or comparative (e.g. "A is larger than B") relation between two things. Note that the left-hand side element (subject) as well as the right-hand side one (object) need to be individuals that exist just once, like the French revolution or the Internet, and not classes that stand for a set of things, like city or headache." assertion.
assertion description "Such a nanopublication contains the main high-level metadata about a scholarly article, including title, authors, and links to other nanopublications." assertion.
assertion description "Such a nanopublication declares that its creator is on the editorial board for a given journal." assertion.
assertion description "This nanopublication maps identifiers to labels, which can be used, for example, to populate drop-down lists of input forms." assertion.
assertion description "This nanopublication maps identifiers to labels, which can be used, for example, to populate drop-down lists of input forms." assertion.
assertion description "Such a nanopublications defines a group to which people can be linked as members." assertion.
assertion description "Such a nanopublication expresses that its creator thinks the given thing is either overrated or underrated. This thing can be anything, such as an approach, a website, or a city." assertion.
assertion description "Such a nanopublication describes the results of a Machine Learning experiment, such as the accuracy achieved by a applying a given ML technique on a given dataset. This template is based on <a href="https://ml-schema.github.io/documentation/ML%20Schema.html">ML Schema</a>." assertion.
assertion description "Such a nanopublication represents an annotation of a text according to the <a href="https://www.w3.org/ns/oa">Web Annotation Vocabulary</a>." assertion.
assertion description "The creator of such a nanopublication declares to have participated in the specified event." assertion.
assertion description "Template for defining a community of practice aiming at implementing the FAIR principles." assertion.
assertion description "Template for defining a community of practice aiming at implementing the FAIR principles." assertion.
research-activity description zookeys.740.23495 provenance.
research-activity description ece3.927 provenance.
assertion description "Such a nanopublication expresses an association between classes of organisms (taxa), a subject taxon and an object taxon, for example expressing that the organisms of the subject taxon prey on organisms of the object taxon. For expressing observations of interactions between individual organisms, please use the alternative template "Associations between organisms"." assertion.
research-activity description ece3.927 provenance.
assertion description "Template for defining a FAIR specification for the following subtypes: metadata schema, metadata-data linking schema, communication protocol, knowledge representation language, structured vocabulary, semantic model and provenance model." assertion.
assertion description "This template allows for describing the benchmark procedure and other metadata of benchmarks performed with RiverBench tasks and datasets. Please refer to RiverBench profiles and tasks using their permanent URLs (PURLs) with a specified version (not dev!). For example: https://w3id.org/riverbench/v/2.0.1/profiles/flat-mixed https://w3id.org/riverbench/v/2.0.1/tasks/flat-compression In the benchmark metrics field, use the names of the metrics specified in the task definition that you have used. If possible, link to the paper that reported this benchmark using the "cites as data source" field. This way, a citation of this paper will appear on RiverBench's website." assertion.
assertion description "This template allows you to make annotate external resources (e.g., papers, software, published datasets) with their RDF stream type, according to RDF-STaX. The assertion is subjective and it includes information on who made it (you) and how was it derived from the source material. More information about RDF-STaX: https://w3id.org/stax" assertion.
Evidence description "<body> <ul> <li>testing markdown</li> <li><h1 id="h1">h1</h1> <ul> <li>bold child</li> </ul> </li> <li>image<ul> <li><img src="https://firebasestorage.googleapis.com/v0/b/firescript-577a2.appspot.com/o/imgs%2Fapp%2Froamjs-dev%2FlZphQDbrTz.jpg?alt=media&token=fa383574-0d1f-40b9-94b4-25e59e76874e" alt=""></li> </ul> </li> </ul> </body>" assertion.
Evidence description "<body> <ul> <li>testing markdown</li> <li><h1 id="h1">h1</h1> <ul> <li>bold child</li> </ul> </li> <li>image<ul> <li><img src="https://firebasestorage.googleapis.com/v0/b/firescript-577a2.appspot.com/o/imgs%2Fapp%2Froamjs-dev%2FlZphQDbrTz.jpg?alt=media&token=fa383574-0d1f-40b9-94b4-25e59e76874e" alt=""></li> </ul> </li> </ul> </body>" assertion.
Evidence description "<body> <ul> <li><h2 id="summary">Summary</h2> <ul> <li>estimated semantic networks of animal concepts from montessori-educated children were more interconnected, with shorter paths between concepts and fewer subcommunities, compared to networks from traditional-schooled but comparable children<ul> <li><img src="https://firebasestorage.googleapis.com/v0/b/firescript-577a2.appspot.com/o/imgs%2Fapp%2Fmegacoglab%2F7aHUer0evF.png?alt=media&token=cf1ba046-bf98-4277-ab1f-99363dd9f278" alt=""> (p. 3: Figure 1, Figure 2)</li> </ul> </li> <li><img src="https://firebasestorage.googleapis.com/v0/b/firescript-577a2.appspot.com/o/imgs%2Fapp%2Fmegacoglab%2FAZen14iS-z.png?alt=media&token=dabe714b-1fb9-464e-b312-986ecacecb7b" alt=""> <img src="https://firebasestorage.googleapis.com/v0/b/firescript-577a2.appspot.com/o/imgs%2Fapp%2Fmegacoglab%2F4ogsqIPX-L.png?alt=media&token=9fa732ba-1fb2-4a01-8687-f5fbfee4ee07" alt=""> (pp. 2-3)</li> </ul> </li> <li><h2 id="grounding-context">Grounding Context</h2> <ul> <li>Who:: compare montessori kids to comparable kids (wrt SES, nonverbal intelligence) from other schooling systems, 67 kids in total<ul> <li><blockquote> A total of 67 children participated in the current study (Mage =9.31, SD=2.23, 47.8% girls) through the University Hospital of Lausanne researchpool as part of a broader research project on education and neurocognitive development. Children were compensated with a ~30 USD gift voucher for completion of the study. Inclusion criteria were schoolings ystem (participants had to be enrolled in Montessori or in traditional classes from the early years on, in the case of the youngest children, or for at least 3 years), age (5-14 years of age); exclusion criteria were parental report of learning disabilities or sensory impairment. To account for variability in our measures due to nonverbal intelligence, or socioeconomic background, we controlled for between-group homogeneity in nonverbal intelligence (black and white short version of the Progressive Matrices®) and family socioeconomic status (both parents’ education levels (score from 1 to 5) and current job (score from 1 to 4); scores were summed and averaged between both parents (max 9), with higher scores denoting higher SES). (p. 4) </blockquote> </li> <li><img src="https://firebasestorage.googleapis.com/v0/b/firescript-577a2.appspot.com/o/imgs%2Fapp%2Fmegacoglab%2FrycDCyaRtc.png?alt=media&token=445952fa-ab39-4e1d-a73d-c69bcead5233" alt=""> <img src="https://firebasestorage.googleapis.com/v0/b/firescript-577a2.appspot.com/o/imgs%2Fapp%2Fmegacoglab%2FKwSV1sEwC_.png?alt=media&token=ab464c01-2a06-4666-8096-791b9b21324c" alt=""> (p. 4)</li> <li><img src="https://firebasestorage.googleapis.com/v0/b/firescript-577a2.appspot.com/o/imgs%2Fapp%2Fmegacoglab%2FzboND2S8yj.png?alt=media&token=39ce2fe6-cb89-457f-9497-2231e700e1c9" alt=""> (p. 2, Table 1)</li> </ul> </li> <li>How:: procedures: do verbal fluency task (name as many animals in 60s), and creativity assessment (standard tasks from Evaluation of Potential Creativity)<ul> <li><blockquote> Children completed verbal fluency task. Category verbal fluency tasks have been widely used to efficiently assess semantic network organization. Consistent with traditional task administration, each child had 60s to name as many animals as he/she could. Based on previous work in children, we targeted the animal category. Children spoke their responses out loud, which were recorded (and later transcribed) by an experimenter. For each child, fluency data was preprocessed using the SemNA pipeline in R. Repetitions or variation on roots were converged and non-category members were excluded from the final analysis. Number of responses per participant were summed (total number of responses). (p. 5) </blockquote> </li> <li><img src="https://firebasestorage.googleapis.com/v0/b/firescript-577a2.appspot.com/o/imgs%2Fapp%2Fmegacoglab%2FN0IkB6RHUK.png?alt=media&token=923be4ff-bb03-42b4-b72e-ab0dc7c93e39" alt=""> (p. 5)</li> <li><blockquote> To assess creative thinking, children completed divergent and convergent creativity tasks from the Evaluation of Potential Creativity. Divergent thinking reflects the ability to think of ideas that differ from one another; convergent thinking reflects the ability to think of a single creative solution. Performance on such creative thinking tasks has been shown to predict both academic*' and creative achievement. In the divergent thinking task, the child was asked to draw as many different drawings as possible from one imposed abstract form (i.e, incomplete shape), within 10 min. The final score was the sum of all valid drawings. In the convergent thinking task, the child had to select three different abstract forms out of eight to create an original drawing that combined them, within 15 min. Three blind judges scored the drawings for originality following the EPoC scoring manual (inter-rater agreement; Krippendorff's alpha = 0.905). Independent t tests were computed on each creativity score (divergent and convergent) to test for between-group differences. Pearson’s correlations were computed between each creativity score and the verbal fluency metrics to test whether creative thinking relates to the quantity and quality of words retrieved from semantic memory. (p. 5) </blockquote> </li> <li><img src="https://firebasestorage.googleapis.com/v0/b/firescript-577a2.appspot.com/o/imgs%2Fapp%2Fmegacoglab%2FDnE4cskRkd.png?alt=media&token=22263a5d-7b20-4e30-9c1f-91477edb7892" alt=""> (p. 5)</li> </ul> </li> <li>What:: measures: clustering coefficient, average shortest path length (ASPL) and modularity (Q) of estimated filtered semantic networks of the animal names from the kids<ul> <li>network construction/estimation<ul> <li>dump all responses into a participant-word matrix, so each word is a column, and each participant is a row, and each cell is 0/1 depending on whether participant mentioned the word. this is kind of like a word-document matrix. equate number of nodes across groups<ul> <li><blockquote> The processed data were transferred into a binary response matrix, where columns represent the unique exemplars given by the sample and rows represent participants; the response matrix is filled out by 1 (if an exemplar was generated by that participant) and 0 (if that exemplar was not generated). To control for confounding factors (such as different nodes or edges in both groups), as in previous studies, the binary response matrices only include responses that are given by at least two participants in each group. Then, to avoid the two groups including a different number of nodes, which may bias comparison of network parameters, responses in the binary matrices were equated, so that the networks of both groups in each sample are compared using the same nodes. (p. 5) </blockquote> </li> <li><img src="https://firebasestorage.googleapis.com/v0/b/firescript-577a2.appspot.com/o/imgs%2Fapp%2Fmegacoglab%2F0oToYzqv9m.png?alt=media&token=13a38aa2-7bcf-4437-80ee-6d17b7847fe4" alt=""> <img src="https://firebasestorage.googleapis.com/v0/b/firescript-577a2.appspot.com/o/imgs%2Fapp%2Fmegacoglab%2FVVRiPfyuXv.png?alt=media&token=b5e316f7-ed9d-4b56-8325-916407d9a691" alt=""> (p. 5)</li> </ul> </li> <li>then for each pair, look at the two "participant vectors" (i.e., the column of 1s and 0s of occurrences across participants)<ul> <li><blockquote> Next, we computed a word association matrix for each group using the cosine similarity. The cosine similarity is commonly used in related to Pearson’s correlation, which can be considered as the cosine between two normalized vectors. With the cosine similarity measure, all values are positive ranging from 0 (two responses do not co-occur) to 1 (two responses always co-occur). For both groups, each element in the word association matrix, A;, represents the cosine similarity or the co-occurrence between response i and j. (p. 5) </blockquote> </li> <li><img src="https://firebasestorage.googleapis.com/v0/b/firescript-577a2.appspot.com/o/imgs%2Fapp%2Fmegacoglab%2FsZwDPGqwwj.png?alt=media&token=b1945d69-2503-44a6-8312-50be0384ebf7" alt=""> (p. 5)</li> </ul> </li> <li>then draw a filtered network from the similarity matrix using [[mTriangulated Maximally Filtered Graph (TMFG)]]<ul> <li><blockquote> Finally, using these word association matrices, we applied the triangulated maximally filtered graph TMFGY; to minimize noise and potential spurious associations. The TMFG method filters the word association matrices to capture only the most relevant information (i.e, removal of spurious associations and retaining the largest associations) within the original network. This approach retains the same number of edges between groups (i.e, 3n-6, where n equals the number of responses), which avoids the confound of difference network structures being due to a different number of edges, This resulted in a 68 nodes network with 198 edges for both groups. (p. 5) </blockquote> </li> <li><img src="https://firebasestorage.googleapis.com/v0/b/firescript-577a2.appspot.com/o/imgs%2Fapp%2Fmegacoglab%2FAjRpsnRRvW.png?alt=media&token=c04e35a2-f0aa-4356-99a6-39d29c03a0a1" alt=""> (p. 5)</li> <li>method is from [[massaraNetworkFilteringBig2017]]</li> </ul> </li> </ul> </li> <li>network metrics: clustering coefficient, average shortest path length (ASPL) and modularity (Q)<ul> <li><img src="https://firebasestorage.googleapis.com/v0/b/firescript-577a2.appspot.com/o/imgs%2Fapp%2Fmegacoglab%2Fk3I3R5wz7m.png?alt=media&token=23dc3f85-456e-46f0-b403-c57ccc02b58f" alt=""><ul> <li>The SemNA pipeline in R was used to compute the CC, ASPL, and Q measures for both groups. Clustering Coefficient (CC) refers to the extent that neighbors of anode will themselves be neighbors (i.e., a neighbor is a node i that is connected through an edge to node j). Higher clustering coefficient indicates a more interconnected semantic network,Average Shortest Path Length (ASPL) refers to the average shortest number of steps (i.e,, edges) needed to traverse between any pair of nodes; the higher the ASPL, the more spread out a network is. Previous research has shown that the ASPL in semantic networks corresponds to participants’ judgments as to whether two concepts are related to each other’*. Modularity (Q) estimates how a network breaks apart (or partitions) into smaller sub-networks or communities. Q measures the extent to which the network has dense connections between nodes within a community and sparse (or few) connections between nodes indifferent communities. Thus, the higher Q, the more the network breaks apart to subcommunities. Such subcommunities can bethought of as subcategories in a semantic network (e.g, farm animals in the “animals” category). Previous research has shown that modularity in semantic networks is inversely related to a network's flexibility. (p. 5)</li> </ul> </li> </ul> </li> <li>network estimation sampling for each group: estimate 1000 case-wise bootstrapped simulated networks from each group (basically, for each group, for 1000 times, grab N-M participants from the group, <a href="((E5BAJ-R3Q))">construct the network</a>, and then compute the <a href="((Z37PYNsQe))">network metrics</a>)</li> </ul> </li> </ul> </li> </ul> <hr> <ul> <li>[[ACL Recontextualizing Claims and Evidence Shared Task]]<ul> <li>Status:: #resultGrounded #methodsGrounded</li> <li>Annotator:: [[Joel Chan]]</li> <li>ResultGrounding:: [[figure]]</li> </ul> </li> </ul> </body>" assertion.
assertion description "Template for defining a FAIR specification for the following subtypes: metadata schema, metadata-data linking schema, communication protocol, knowledge representation language, structured vocabulary, semantic model and provenance model." assertion.
assertion description "Template for defining a FAIR specification for the following subtypes: metadata schema, metadata-data linking schema, communication protocol, knowledge representation language, structured vocabulary, semantic model and provenance model." assertion.
assertion description "Template for defining an identifier service." assertion.
assertion description "Template for defining a crosswalk." assertion.
assertion description "Template for defining an authentication and authorization service." assertion.
assertion description "Template for defining a registry of digital objects." assertion.
assertion description "Template for defining an editor service." assertion.
assertion description "Template for defining a validation service." assertion.
assertion description "Template for defining a FAIR practice" assertion.
assertion description "Template for defining an authentication and authorization service." assertion.
assertion description "Template for defining a validation service." assertion.
assertion description "Template for defining an editor service." assertion.
assertion description "Template for defining a crosswalk." assertion.
assertion description "Template for defining a registry of digital objects." assertion.
assertion description "Template for defining a FAIR specification for the following subtypes: metadata schema, metadata-data linking schema, communication protocol, knowledge representation language, structured vocabulary, semantic model and provenance model." assertion.
assertion description "Template for defining an identifier service." assertion.
assertion description "Template for defining an identifier service." assertion.
assertion description "Template for defining a FAIR specification for the following subtypes: metadata schema, metadata-data linking schema, communication protocol, knowledge representation language, structured vocabulary, semantic model and provenance model." assertion.
assertion description "Template for defining a registry of digital objects." assertion.
assertion description "Template for defining a crosswalk." assertion.
assertion description "Template for defining an editor service." assertion.
assertion description "Template for defining a validation service." assertion.
assertion description "Template for defining an authentication and authorization service." assertion.
assertion description "Template for defining a FAIR practice" assertion.
assertion description "Such a nanopublication contains the main high-level metadata about a scholarly article, including title, authors, and links to other nanopublications." assertion.
assertion description "Template for defining a FAIR practice" assertion.
Evidence description "<body> <ul> <li>testing markdown</li> <li><h1 id="h1">h1</h1> <ul> <li>bold child</li> </ul> </li> <li>image<ul> <li><img src="https://firebasestorage.googleapis.com/v0/b/firescript-577a2.appspot.com/o/imgs%2Fapp%2Froamjs-dev%2FlZphQDbrTz.jpg?alt=media&token=fa383574-0d1f-40b9-94b4-25e59e76874e" alt=""></li> </ul> </li> </ul> </body>" assertion.
get-instance-count description "This query returns the number of known instances for the given class IRI." assertion.
get-latest-instance-nps description "This query returns the latest nanopublications where an instance is assigned to the given class." assertion.
_step description "Step 1" assertion.
_step description "Step 2" assertion.
_step description "Step 3" assertion.
_plan description "This is a test workflow." assertion.
_step description "Step 1" assertion.
_step description " @is_fairstep(label='test_label') def add(a: int, b: int) -> int: """ Computational step adding two ints together. """ return a + b " assertion.
_step description "Step 1" assertion.
_step description " @is_fairstep(label='test_label') def add(a: int, b: int) -> int: """ Computational step adding two ints together. """ return a + b " assertion.
_step description "Step 1" assertion.
_step description " @is_fairstep(label='test_label') def add(a: int, b: int) -> int: """ Computational step adding two ints together. """ return a + b " assertion.
_step description "Step 1" assertion.
_step description " @is_fairstep(label='test_label') def add(a: int, b: int) -> int: """ Computational step adding two ints together. """ return a + b " assertion.
_step description "Step 1" assertion.
_step description " @is_fairstep(label='test_label') def add(a: int, b: int) -> int: """ Computational step adding two ints together. """ return a + b " assertion.
_step description "Step 1" assertion.
_step description " @is_fairstep(label='test_label') def add(a: int, b: int) -> int: """ Computational step adding two ints together. """ return a + b " assertion.
_step description "Step 1" assertion.
_step description "Step 2" assertion.
_step description "Step 3" assertion.
_plan description "This is a test workflow." assertion.
_step description "Preheat an oven to 200 degrees C." assertion.
_step description " @is_fairstep(label='test_label') def add(a: int, b: int) -> int: """ Computational step adding two ints together. """ return a + b " assertion.
_step description "Step 1" assertion.
_step description "Step 2" assertion.
_step description "Step 3" assertion.
_plan description "This is a test workflow." assertion.
_step description "Preheat an oven to 200 degrees C." assertion.
_step description " @is_fairstep(label='test_label') def add(a: int, b: int) -> int: """ Computational step adding two ints together. """ return a + b " assertion.
_step description "Step 1" assertion.
_step description "Step 2" assertion.
_step description "Step 3" assertion.
_plan description "This is a test workflow." assertion.
_step description "Preheat an oven to 200 degrees C." assertion.
_step description " @is_fairstep(label='test_label') def add(a: int, b: int) -> int: """ Computational step adding two ints together. """ return a + b " assertion.
_step description "Step 1" assertion.
_step description "Step 2" assertion.
_step description "Step 3" assertion.

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