Nanopublications

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

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find-things description "This query performs a full-text search on the things introduced in nanopublications." assertion.
find-things description "This query performs a full-text search on the labels for the 3PFF events." assertion.
find-3pff-events description "This query performs a full-text search on the labels for the 3PFF events." assertion.
assertion description "This nanopublication defines an assertion template. Such templates are used to generate the input form for the (blue) assertion part of nanopublications." 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.
get-3pff-events description "Returns a list of all events related to the Three Point FAIRification Framework (3PFF)" assertion.
get-3pff-events description "Returns a list of all events related to the Three Point FAIRification Framework (3PFF)" assertion.
Evidence description "<body> <ul> <li><img src="https://firebasestorage.googleapis.com/v0/b/firescript-577a2.appspot.com/o/imgs%2Fapp%2Froamjs-dev%2F7gNHw9OuTn.png?alt=media&token=4af2b697-fa2a-46c2-a3c6-f54a2531a33b" alt=""></li> </ul> </body>" assertion.
Evidence description "<body> <ul> <li><img src="https://firebasestorage.googleapis.com/v0/b/firescript-577a2.appspot.com/o/imgs%2Fapp%2Froamjs-dev%2F7gNHw9OuTn.png?alt=media&token=4af2b697-fa2a-46c2-a3c6-f54a2531a33b" alt=""></li> </ul> </body>" assertion.
Evidence description "<body> <ul> <li><img src="https://firebasestorage.googleapis.com/v0/b/firescript-577a2.appspot.com/o/imgs%2Fapp%2Froamjs-dev%2F7gNHw9OuTn.png?alt=media&token=4af2b697-fa2a-46c2-a3c6-f54a2531a33b" alt=""></li> </ul> </body>" assertion.
Evidence description "<body> <ul> <li><img src="https://firebasestorage.googleapis.com/v0/b/firescript-577a2.appspot.com/o/imgs%2Fapp%2Froamjs-dev%2F7gNHw9OuTn.png?alt=media&token=4af2b697-fa2a-46c2-a3c6-f54a2531a33b" alt=""></li> </ul> </body>" assertion.
Evidence description "<body> <ul> <li><img src="https://firebasestorage.googleapis.com/v0/b/firescript-577a2.appspot.com/o/imgs%2Fapp%2Froamjs-dev%2F7gNHw9OuTn.png?alt=media&token=4af2b697-fa2a-46c2-a3c6-f54a2531a33b" alt=""></li> </ul> </body>" assertion.
Evidence description "<body> <ul> <li><img src="https://firebasestorage.googleapis.com/v0/b/firescript-577a2.appspot.com/o/imgs%2Fapp%2Froamjs-dev%2F7gNHw9OuTn.png?alt=media&token=4af2b697-fa2a-46c2-a3c6-f54a2531a33b" alt=""></li> </ul> </body>" assertion.
Evidence description "<body> <ul> <li><img src="https://firebasestorage.googleapis.com/v0/b/firescript-577a2.appspot.com/o/imgs%2Fapp%2Froamjs-dev%2F7gNHw9OuTn.png?alt=media&token=4af2b697-fa2a-46c2-a3c6-f54a2531a33b" alt=""></li> </ul> </body>" assertion.
get-monthly-active-user-count description "Returns the number of active users per month." assertion.
research-activity description document provenance.
test description "this is just a test" assertion.
get-nanopubs-for-pubkey-and-type description "This query returns all nanopub IDs for the given pubkey hash and (optionally) type." assertion.
fip description "Nanopublication FIP" assertion.
fip description "Nanopublication FIP" assertion.
assertion description "Template for defining a registry of digital objects." assertion.
research-activity description C52P4X provenance.
MyExampleProject description "This is just an example project." assertion.
assertion description "Such nanopublications define project pages that are shown on Nanodash." assertion.
MyExampleProject description "This is just an example project for demonstration purposes. Does HTML work here?" assertion.
MyExampleProject description "This is just an example project for demonstration purposes. Does HTML work here?" assertion.
assertion description "Such nanopublications define project pages that are shown on Nanodash." assertion.
MyExampleProject description "This is just an example project for demonstration purposes. Does HTML work here?" assertion.
LEIbits description "Researchers spend the majority of their time twofold – producing output and then looking for the output of others to, in turn, help them produce more output. For both of these activities, computers assist us. Unfortunately, the latter activity is a challenge because we often provide information in a way that is hard for the computer to understand; computers like structured information to provide meaningful knowledge. Therefore, the more structured our research output, the better computers can help us with our research. LEIbits is a lean approach to self-reporting research findings in a structured way so that you can in turn more easily find and use them with the help of the computer." assertion.
LEIbits description "Researchers spend the majority of their time twofold – producing output and then looking for the output of others to, in turn, help them produce more output. For both of these activities, computers assist us. Unfortunately, the latter activity is a challenge because we often provide information in a way that is hard for the computer to understand; computers like structured information to provide meaningful knowledge. Therefore, the more structured our research output, the better computers can help us with our research. LEIbits is a lean approach to self-reporting research findings in a structured way so that you can in turn more easily find and use them with the help of the computer." assertion.
LEIbits description "Researchers spend the majority of their time twofold – producing output and then looking for the output of others to, in turn, help them produce more output. For both of these activities, computers assist us. Unfortunately, the latter activity is a challenge because we often provide information in a way that is hard for the computer to understand; computers like structured information to provide meaningful knowledge. Therefore, the more structured our research output, the better computers can help us with our research. LEIbits is a lean approach to self-reporting research findings in a structured way so that you can in turn more easily find and use them with the help of the computer." assertion.
LEIbits description "Researchers spend the majority of their time twofold – producing output and then looking for the output of others to, in turn, help them produce more output. For both of these activities, computers assist us. Unfortunately, the latter activity is a challenge because we often provide information in a way that is hard for the computer to understand; computers like structured information to provide meaningful knowledge. Therefore, the more structured our research output, the better computers can help us with our research. LEIbits is a lean approach to self-reporting research findings in a structured way so that you can in turn more easily find and use them with the help of the computer." assertion.
LEIbits description "Researchers spend the majority of their time twofold – producing output and then looking for the output of others to, in turn, help them produce more output. For both of these activities, computers assist us. Unfortunately, the latter activity is a challenge because we often provide information in a way that is hard for the computer to understand; computers like structured information to provide meaningful knowledge. Therefore, the more structured our research output, the better computers can help us with our research. LEIbits is a lean approach to self-reporting research findings in a structured way so that you can in turn more easily find and use them with the help of the computer." assertion.
LEIbits description "Researchers spend the majority of their time twofold – producing output and then looking for the output of others to, in turn, help them produce more output. For both of these activities, computers assist us. Unfortunately, the latter activity is a challenge because we often provide information in a way that is hard for the computer to understand; computers like structured information to provide meaningful knowledge. Therefore, the more structured our research output, the better computers can help us with our research. LEIbits is a lean approach to self-reporting research findings in a structured way so that you can in turn more easily find and use them with the help of the computer." assertion.
LEIbits description "Researchers spend the majority of their time twofold – producing output and then looking for the output of others to, in turn, help them produce more output. For both of these activities, computers assist us. Unfortunately, the latter activity is a challenge because we often provide information in a way that is hard for the computer to understand; computers like structured information to provide meaningful knowledge. Therefore, the more structured our research output, the better computers can help us with our research. LEIbits is a lean approach to self-reporting research findings in a structured way so that you can in turn more easily find and use them with the help of the computer." assertion.
LEIbits description "Researchers spend the majority of their time twofold – producing output and then looking for the output of others to, in turn, help them produce more output. For both of these activities, computers assist us. Unfortunately, the latter activity is a challenge because we often provide information in a way that is hard for the computer to understand; computers like structured information to provide meaningful knowledge. Therefore, the more structured our research output, the better computers can help us with our research. LEIbits is a lean approach to self-reporting research findings in a structured way so that you can in turn more easily find and use them with the help of the computer." assertion.
LEIbits description "Researchers spend the majority of their time twofold – producing output and then looking for the output of others to, in turn, help them produce more output. For both of these activities, computers assist us. Unfortunately, the latter activity is a challenge because we often provide information in a way that is hard for the computer to understand; computers like structured information to provide meaningful knowledge. Therefore, the more structured our research output, the better computers can help us with our research. LEIbits is a lean approach to self-reporting research findings in a structured way so that you can in turn more easily find and use them with the help of the computer." assertion.
LEIbits description "Researchers spend the majority of their time twofold – producing output and then looking for the output of others to, in turn, help them produce more output. For both of these activities, computers assist us. Unfortunately, the latter activity is a challenge because we often provide information in a way that is hard for the computer to understand; computers like structured information to provide meaningful knowledge. Therefore, the more structured our research output, the better computers can help us with our research. LEIbits is a lean approach to self-reporting research findings in a structured way so that you can in turn more easily find and use them with the help of the computer." assertion.
LEIbits description "FAIR Implementation Profiles (FIPs) are declared by communities to specify the technologies used to achieve the FAIR principles. This page provides an overview for users involved in creating FIPs." assertion.
LEIbits description "Researchers spend the majority of their time twofold – producing output and then looking for the output of others to, in turn, help them produce more output. For both of these activities, computers assist us. Unfortunately, the latter activity is a challenge because we often provide information in a way that is hard for the computer to understand; computers like structured information to provide meaningful knowledge. Therefore, the more structured our research output, the better computers can help us with our research. LEIbits is a lean approach to self-reporting research findings in a structured way so that you can in turn more easily find and use them with the help of the computer. For more information see: https://doi.org/10.5281/zenodo.15222956 " assertion.
research-activity description C52P4X provenance.
assertion description "Such nanopublications define project pages that are shown on Nanodash." assertion.
assertion description "Template for describing a dataset at summary level" assertion.
researchproject description "This project was carried out in affiliation with the Rijksmuseum Amsterdam and in collaboration with the Cultural Heritage Agency of The Netherlands, the European Synchrotron Radiaion Facility (ESRF), the University of Amsterdam, and the Vrije Universiteit Amsterdam. Ultramarine is a highly-valued, brilliant blue pigment naturally obtained from the mineral lazurite, which is the main component in the semi-precious stone lapis lazuli. Ultramarine-based paints – for instance, lazurite mixed with linseed oil – are known to exhibit a degradation phenomenon, known as “ultramarine sickness” or “ultramarine disease,” that drastically changes the aesthetic and integrity of the artwork, showing cracking, flaking, and/or apparent color change. Our research project is interested in understanding the mechanism of ultramarine paint degradation and determining a means to identify and prevent the onset of such degradation. We hypothesize that “ultramarine sickness/disease” is the result of accelerated binding medium (e.g. oil) degradation catalyzed by ultramarine (or lazurite), supported by evidence of its catalytic activity in catalytic test reactions. To determine the potential catalytic activity of lazurite toward oil degradation, we study the effect of pigment preparation on lazurite properties and, in turn, catalytic activity toward oil-relevant reactions. Further, we analyze historically relevant paint samples for comparison. With these areas of focus, we aim to gain a deeper understanding of the ultramarine degradation phenomenon as it naturally arises. (https://www.nicas-research.nl/projects/ultramarine-disease/)" assertion.
researchproject description "This project was carried out in affiliation with the Rijksmuseum Amsterdam and in collaboration with the Cultural Heritage Agency of The Netherlands (RCE), the European Synchrotron Radiaion Facility (ESRF), the University of Amsterdam (UvA), and the Vrije Universiteit Amsterdam (VU). Ultramarine is a highly-valued, brilliant blue pigment naturally obtained from the mineral lazurite, which is the main component in the semi-precious stone lapis lazuli. Ultramarine-based paints – for instance, lazurite mixed with linseed oil – are known to exhibit a degradation phenomenon, known as “ultramarine sickness” or “ultramarine disease,” that drastically changes the aesthetic and integrity of the artwork, showing cracking, flaking, and/or apparent color change. Our research project is interested in understanding the mechanism of ultramarine paint degradation and determining a means to identify and prevent the onset of such degradation. We hypothesize that “ultramarine sickness/disease” is the result of accelerated binding medium (e.g. oil) degradation catalyzed by ultramarine (or lazurite), supported by evidence of its catalytic activity in catalytic test reactions. To determine the potential catalytic activity of lazurite toward oil degradation, we study the effect of pigment preparation on lazurite properties and, in turn, catalytic activity toward oil-relevant reactions. Further, we analyze historically relevant paint samples for comparison. With these areas of focus, we aim to gain a deeper understanding of the ultramarine degradation phenomenon as it naturally arises. (https://www.nicas-research.nl/projects/ultramarine-disease/)" assertion.
research-activity description C52P4X provenance.
researchproject description "Measurement and evaluation of dynamic self-organizing systems to integrate Chinese and Western medicine (https://sinodutchcentre.nl/?Research___Projects)" assertion.
research-activity description 0002-9149(89)90524-9 provenance.
research-activity description 0002-9149(89)90524-9 provenance.
get-datasets description "This query returns all datasets found." assertion.
researchproject description "Metabolic characterization of the natural progression of chronic hepatitis B" assertion.
researchproject description "Fetal Metabolic Stress Disrupts Immune Homeostasis and Induces Proinflammatory Responses in Human Immunodeficiency Virus Type 1– and Combination Antiretroviral Therapy–Exposed Infants" assertion.
researchproject description "Exploring the Fecal Metabolome in Infants With Cow's Milk Allergy: The Distinct Impacts of Cow's Milk Protein Tolerance Acquisition and of Synbiotic Supplementation" assertion.
get-approved-nanopubs description "This query returns all nanopublications approved by somebody." assertion.
get-datasets description "This query returns all datasets found." assertion.
fip description "FLUXNET FIP: Fair Implementation Profile for the global network of eddy covariance stations FLUXNET " assertion.
researchproject description "PARC (Partnership for Assessment of the Risks of Chemcials) is an EU public-public partnership to drive the development of regulatory risk assessment and the implementation of New Approach Methodologies and Next Generation Risk Assesment tools into regulatory practice" assertion.
list_nonqualifed_fsr description "List all non-qualified FAIR Enabling/Supporting Resources." assertion.
get-services description "This query returns all nanopub services announced in nanopublications." assertion.
get-services description "This query returns all nanopub services announced in nanopublications." assertion.
get-overunderrated-entries description "This query returns all entries where users express under-/overrated opinion." assertion.
get-top-overrated description "This query returns the top things being claimed to be overrated." assertion.
get-top-overrated description "This query returns the top things being claimed to be overrated." assertion.
get-top-underrated description "This query returns the top things being claimed to be underrated." assertion.
get-latest-nanopubs description "This query returns the latest nanopublications, excluding superseded, retracted, or example ones." assertion.
get-latest-nanopubs description "This query returns the latest nanopublications, excluding superseded, retracted, or example ones." assertion.
get-latest-example-nanopubs description "This query returns the latest nanopublications marked as examples, excluding superseded and retracted ones." assertion.
FIP description "FAIR Implementation Profiles (FIPs) are declared by communities to specify the technologies used to achieve the FAIR principles. This page provides an overview for users involved in creating FIPs." assertion.
FIP description "FAIR Implementation Profiles (FIPs) are declared by communities to specify the technologies used to achieve the FAIR principles. This page provides an overview for users involved in creating FIPs." assertion.

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