Find research datasets worth reusing

opencc-by-4.0Mar 2023View details →

Database underlying the scientific publication: A global database of seahorse research and innovation, from the beginning to 2022

The present database supports the study "A global database of seahorse research and innovation, from the beginning to 2022".  Scientific knowledge on seahorses is rapidly expanding in response to declines in wild populations due to habitat loss and fishing. A plethora of information has accumulated and up until now, a global, publicly available, curated database has never been produced in a transparent and systematic way. Here, we present the largest open-access repository of scientific publications addressing seahorses and, for the first time, of theses and patents. Compilation followed the “Preferred Reporting Items for Systematic reviews and Meta-Analyses” (PRISMA) Statement for systematic reviews and meta-analyses, with modifications. The current repository duplicates the number of scientific publication records found from previous bibliometric/literature/review studies, using three extra repositories of source publications, and a lifetime window, e.i. from the beginning to March 2022. A total of 977 scientific publications, 101 theses and 533 patents are gathered in the dataset, covering 41 seahorse species out of 48 currently recognized. In addition, current work presents for the first time new metrics on authors, institutions, and research subject/field/discipline/thematic, as well as the organism’s stage of development (embryo, newborn, juvenile, subadult and adult). To expand metadata usage, the database was also made available in the Dublin Core™ Metadata Initiative format. This contribution can be used as a core reference for scientists, aquaculturists and conservationists, and is useful to rapidly identify relevant literature and knowledge gaps, better understand seahorse research and discover new trends in seahorse research and innovation. The database is available in two formats: 1) <a href="https://zenodo.org/api/files/5651b70d-a7a0-45e6-8ce6-cb92bbe6f7e5/SeahorseBibliometricDatabase.xlsx">SeahorseBibliometricDatabase.xlsx</a> and 2) <a href="https://zenodo.org/api/files/5651b70d-a7a0-45e6-8ce6-cb92bbe6f7e5/SeahorseBibliometricDatabase_DublinCore.xlsx">SeahorseBibliometricDatabase_DublinCore.xlsx</a>, which is a vocabulary standardized (Dublin Core™ Metadata Initiative) version of the previous one, for metadata reuse.

opencc-by-4.0Mar 2023View details →

Data and Code for "A planetary health innovation for disease, food, and water challenges in Africa"

Data and R code for both economic and non-economic analysis for the paper titled, "A planetary health innovation for disease, food, and water challenges in Africa"

opencc-by-4.0Mar 2023View details →

logo innovation, sustainability and business development research group

Logo research group. Innovación, Sostenibilidad y Desarrollo Empresarial (ISDE)

opencc-by-4.0Oct 2022View details →

Characterization of the flow through innovative permeable fairings at the VKI WAABLIEF facility

Aircraft-noise mitigation is a significant issue in the aviation industry due to the operation and expansion of airports and the growth of residential areas surrounding them. Take-off and landing are considered critical phases of a flight path regarding the noise impact. During a typical approach, the airframe noise has become a dominant noise source as the engine noise has been reduced after intensive studies performed for decades. Landing gears (LGs) are among the main contributors to the generation of this noise source, and one of the promising noise-reduction technologies (NRTs) that have been proposed consists of placing flow-permeable fairings in front of them. However, the development of these NRTs is limited due to the strong integration constraints imposed by the other disciplines and the lack of knowledge of the complex flow physics involved in the problem. The project INVENTOR, funded by the European Union in the framework of the H2020 MG-1-5-2019 call ”Advancements in aerodynamics and innovative propulsion systems for quieter and greener aircrafts” (Grant Agreement number: 860538), aims to fill this scientific gap and understand the noise generated by LGs integrated with porous fairings through numerical methods. Nevertheless, these flow-permeable materials must be modeled and validated through measurements to obtain high-accuracy numerical predictions. Therefore, the objective of the present submission is to provide an extensive experimental dataset of the characterization of the flow through fairings fitted with innovative porous materials. The measurement campaign has been carried out at the WAABLIEF facility of the von Karman Institute for Fluid Dynamics (VKI) and contains particle-image-velocimetry, hot-wire-anemometry, and total-pressure measurements at different flow speeds and with several materials. The document attached to the dataset includes all the information required to numerically reproduce the VKI experimental setup.

opencc-by-4.0Jun 2023View details →

Datasets for publication: FREEDA: an automated computational pipeline guides experimental testing of protein innovation

Supplementary materials related to the article: Dudka D, Akins RB, Lampson MA (2023) FREEDA: an automated computational pipeline guides experimental testing of protein innovation. Journal of Cell Biology.   The file includes FREEDA pipeline validation results: orthologue detection, comparison with previously published datasets, analysis of rodent centromere proteins and additional analyses of KIF4A, KIF4B, histone H4, MICA, MICB, NUP73 and HERC5. Manually aligned structural prediction models for MIS18A, MIS18B, AURKC, CENP-O and CENP-P are also included.

opencc-by-4.0Jun 2023View details →

Data for study "Digital tools for innovative higher education teaching - a Scoping Review of Empirical Studies".

The goal of this research is to point out, by a scoping review of the papers published between 2020 and 2023, the digital tools used in the teaching process at the higher education level. The dataset was collected using Web of Science and Scopus databases. The dataset contains the following elements: <ol> <li>paper details (Authors, Publication Year, Article Title, DOI),</li> <li>OECD classification,</li> <li>teaching tool assigned to paper.</li> </ol>

opencc-byDec 2022View details →

Guided a Research Project titled "Bottle Biospheres: An Innovative Idea to understanding life" which was submitted by Ms. Dakhni Fatima in Pure Sciences. December 12, 2022.

Guided a Research Project titled “Bottle Biospheres: An Innovative Idea to understanding life” which was submitted by Ms. Dakhni Fatima in Pure Sciences in Aavishkar. December 12, 2022

opencc-by-4.0Jun 2023View details →

MSCA ITN 2014-2017 innovation and patents

List of MSCA ITN projects from H2020-MSCA-ITN-2014-2017 that reported publications, innovations and/or patents.

opencc-by-sa-4.0Nov 2021View details →

A controlled vocabulary for research and innovation in the field of Cultural Heritage & Heritage Sciences

This controlled vocabulary of keywords related to the field of Cultural Heritage and Heritage Sciences was built by SIRIS Academic in collaboration with IRPET (the Regional Institute for Economic Planning of Tuscany) and the ISPC (Institute of Heritage Science of CNR), in order to identify Cultural related research, development, and innovation activities. The work was carried out by consulting domain experts' advice, and it was ultimately applied to inform regional strategies on Cultural Heritage and research and innovation policy. The aim of this vocabulary is to enable one to retrieve texts (e.g. R&D projects and scientific publications) featuring the concepts included in the present vocabulary in their titles and abstracts, assuming that these records have a certain contribution of applications, techniques and issues, in the domain of Cultural Heritage and Heritage Sciences. The aim of this classification is to identify research products in the domain of Cultural Heritage, ranging from documents in some of its “traditional” disciplines, but also from documents emerging from interdisciplinary projects that apply novel areas and technologies in the domain of Cultural Heritage. The identification of texts in the domain of Cultural Heritage requires a task of text classification. Developing a method that could be applied to decide if a text can be relevant or have some relation to the domain of Cultural Heritage is a challenging task. The definition of what Cultural Heritage is and what it includes is a complex activity, even for domain experts. This is in particular because Cultural Heritage is quite a broad field of knowledge, and there is no full agreement on where the borders of the domain are. To define the scope of the perimeter, in this project, many of the available definitions were taken into account. Because of the high number of resources available in the domain, among thesauruses and taxonomies, the construction of a weakly-supervised controlled vocabulary was considered as the best way of retrieving documents in the domain. Since there is no annotated corpus/dataset of research texts in the domain capable of generalising the diversity of publications that can be related to the cultural domain, but stemming from different disciplines, we have opted for a text classification technique based on rules – specifically, a weakly-supervised controlled vocabulary. As defined by the Getty Institute, a controlled vocabulary is an organized arrangement of words and phrases used to index content and/or to retrieve content through browsing or searching. It typically includes preferred and variant terms and has a defined scope or describes a specific domain. The purpose of controlled vocabularies is to organize information and to provide terminology to catalogue and retrieve information. While capturing the richness of variant terms, controlled vocabularies also promote consistency in preferred terms and the assignment of the same terms to similar content (Harping, 2010). In short, Cultural Heritage is a rather abstractly-defined field, and Heritage Science is a particularly “fuzzy” field within Cultural Heritage. One of the main limitations of the approach we used is that the controlled vocabularies never capture all the lexical and linguistic variants of a term, and we may miss relevant texts if we cannot find the correct pattern to match during the search. But on the other hand, the controlled vocabulary is built from available vocabularies and thesauruses in the domain of Cultural Heritage, which are large resources. All the concepts in these resources are not included directly in the controlled vocabularies, because they would add noise to the classification. Therefore, the automatic weak supervision and a human curation of the final controlled vocabulary is fundamental for achieving correct results. The controlled vocabulary is built taking advantage of these four resources: <ul> <li>The <a href="https://www.getty.edu/research/tools/vocabularies/aat/">Art and Architecture Thesaurus (AAT)</a>: this is a structured vocabulary with approximately 34,000 concepts, including 131,000 words, descriptions and other information related to art, architecture, decorative arts, archival material and material culture, commonly used for cataloguing and for information retrieval.</li> <li> Some cultural heritage categories in <a href="https://en.wikipedia.org/wiki/Category:Cultural_heritage">Wikipedia</a> and <a href="https://dbpedia.org/page/Cultural_heritage">DBpedia</a>: these categories have been used to collect all related articles and subcategories, in order to obtain relevant, similar and specific instances of concepts linked to the domain.  </li> <li> The <a href="https://www.riches-project.eu/riches-taxonomy.html">RICHES Taxonomy</a>: this taxonomy is a theoretical framework of related terms and their definitions, referring to the new concepts in the digital era, with the aim of defining the scope of some digital technologies applied to cultural heritage. </li> <li> <a href="https://www.heritagedata.org/blog/">Heritage Data - Linked Data Vocabularies for Cultural Heritage</a>: a dataset which includes several cultural heritage thesauruses and vocabularies and is recognised as a reference point in the United Kingdom in the domain of cultural heritage. </li> </ul> The collection of concepts extracted from these four resources was composed of more than 60,000 terms, which have been refined as described in the next section.   ## Automatic validation of the controlled vocabulary In order to refine the collection of concepts to have a final set of relevant concepts and terms in the domain of Cultural Heritage, a semi-automatic validation has been applied to remove the irrelevant, too general, and ambiguous terms. To keep the relevant ones, the <a href="https://ncses.nsf.gov/pubs/nsb20206/specialization-and-impact-analysis-combined#:~:text=The%20specialization%20index%20(SI)%20is,the%20total%20output%20across%20all">specialization index (SI) </a>metric has been calculated for each of the keywords in the collection. In this case, the SI can be obtained measuring the fraction of publications with a keyword in a set of publications in the domain of Cultural Heritage and normalizing over the fraction of publications in the open domain with that keyword. After the calculation of the SI, all the keywords below a certain threshold are removed, and a manual supervision step is applied in order to remove non-pertinent keywords. An example of this automatic validation can be observed in the next table: <table> <tbody> <tr> <td> Keyword </td> <td> Specialization Index </td> <td> Automatic threshold </td> <td> Manual supervision </td> </tr> <tr> <td> male </td> <td> 0.27 </td> <td> Removed </td> <td> Accepted </td> </tr> <tr> <td> 3-d laser scanning </td> <td> 0.7 </td> <td> Removed </td> <td> Accepted </td> </tr> <tr> <td> 78 rpm records </td> <td> 20.7 </td> <td> Accepted </td> <td> Removed </td> </tr> <tr> <td> vienna </td> <td> 3.48 </td> <td> Accepted </td> <td> Removed </td> </tr> <tr> <td> radiocarbon dating </td> <td> 13.6 </td> <td> Accepted </td> <td> Accepted </td> </tr> <tr> <td> graffiti </td> <td> 25 </td> <td> Accepted </td> <td> Accepted </td> </tr> <tr> <td> bark painting </td> <td> 20.7 </td> <td> Accepted </td> <td> Accepted </td> </tr> <tr> <td> pompeii </td> <td> 16.23 </td> <td> Accepted </td> <td> Accepted </td> </tr> </tbody> </table> The SI of the final keywords can be used as a probabilistic metric for each keyword. The final list of keywords was manually curated by domain experts.   ## Evaluation of the controlled vocabulary The final controlled vocabulary was evaluated with an external dataset with the aim of calculating its degree of precision. The evaluation dataset was composed of a collection of articles in 4 journals unequivocally considered to fall within the domain of Cultural Heritage. These four journals were: (1) Journal Of Cultural Heritage, (2) Journal On Computing And Cultural Heritage, (3) Journal Of Cultural Heritage Management And Sustainable Development and (4) Digital Applications In Archaeology And Cultural Heritage. This collection was composed of 5,000 articles, considered as the positive set, and another collection of randomly selected 5,000 articles outside of the Cultural Heritage domain, considered as the false set. The Cultural Heritage vocabulary was applied to the evaluation data set, obtaining a 95% of precision. After a set of improvements on the vocabulary, based on the exploration of publications not identified in the first test and the false positive results, we obtained a 98% of precision. The application of the vocabulary taking advantage of the probability of each keyword as its weight of being in the domain did not improve the results, and for this reason the probabilistic approach was discarded.   ## Using the vocabulary to classify publications concerning Cultural Heritage The definition of the vocabulary does not, per se, allow to identify research contributions in Cultural Heritage: this is performed by actually matching the terms in the controlled vocabulary to the content of the gathered research textual records. To successfully carry out this task, a series of pattern matching rules must be defined to capture possible variants of the same concept, such as permutations of words within the concept and/or the presence of null words to be skipped. For this reason, we have carefully crafted matching rules that take into account permutations of words and that allow words within concept to be within a certain distance. In the following table we present some examples of the tagging process on some abstracts: <table> <tbody> <tr> <td> Publication title </td> <td> Publication abstract </td> </tr> <tr> <td> Egocentric visitor localization and artwork detection in cultural sites using synthetic data </td> <td> Computer vision and machine learning can be used in cultural heritage to augment the experience of visitors during the exploration of the cultural site, as well as to assist its management. To achieve such goals, two fundamental tasks should be addressed, i.e., localizing visitors and recognizing the observed artworks. Wearable cameras offer a convenient setting to address both tasks through the analysis of images acquired from the visitors’ points of view. However, the engineering of approaches to address such tasks generally requires large amounts of labeled data. We propose a tool which can be used to collect and automatically label synthetic visual data suitable to study image-based localization and artwork detection. The tool simulates a virtual agent navigating the 3D model of a real cultural site and automatically captures video frames along with the related ground truth camera poses and semantic masks indicating the position of artworks. We generate a dataset of synthetic images starting from the 3D model of a museum located in Siracusa, Italy. The experiments suggest that the proposed tool allows to drastically reduce the effort needed to collect and label data, providing a means to generate large-scale datasets suitable to study localization and artwork detection in cultural sites. </td> </tr> <tr> <td> Discovering Leonardo with artificial intelligence and holograms: A user study </td> <td> Cutting-edge visualization and interaction technologies are increasingly used in museum exhibitions, providing novel ways to engage visitors and enhance their cultural experience. Existing applications are commonly built upon a single technology, focusing on visualization, motion or verbal interaction (e.g., high-resolution projections, gesture interfaces, chatbots). This aspect limits their potential, since museums are highly heterogeneous in terms of visitors profiles and interests, requiring multi-channel, customizable interaction modalities. To this aim, this work describes and evaluates an artificial intelligence powered, interactive holographic stand aimed at describing Leonardo Da Vinci's art. This system provides the users with accurate 3D representations of Leonardo's machines, which can be interactively manipulated through a touchless user interface. It is also able to dialog with the users in natural language about Leonardo's art, while keeping the context of conversation and interactions. Furthermore, the results of a large user study, carried out during art and tech exhibitions, are presented and discussed. The goal was to assess how users of different ages and interests perceive, understand and explore cultural objects when holograms and artificial intelligence are used as instruments of knowledge and analysis. </td> </tr> <tr> <td> Hybrid query expansion using lexical resources and word embeddings for sentence retrieval in question answering </td> <td> Question Answering (QA) systems based on Information Retrieval return precise answers to natural language questions, extracting relevant sentences from document collections. However, questions and sentences cannot be aligned terminologically, generating errors in the sentence retrieval. In order to augment the effectiveness in retrieving relevant sentences from documents, this paper proposes a hybrid Query Expansion (QE) approach, based on lexical resources and word embeddings, for QA systems. In detail, synonyms and hypernyms of relevant terms occurring in the question are first extracted from MultiWordNet and, then, contextualized to the document collection used in the QA system. Finally, the resulting set is ranked and filtered on the basis of wording and sense of the question, by employing a semantic similarity metric built on the top of a Word2Vec model. This latter is locally trained on an extended corpus pertaining the same topic of the documents used in the QA system. This QE approach is implemented into an existing QA system and experimentally evaluated, with respect to different possible configurations and selected baselines, for the Italian language and in the Cultural Heritage domain, assessing its effectiveness in retrieving sentences containing proper answers to questions belonging to four different categories. </td> </tr> <tr> <td> "3D reconstruction and validation of historical background for immersive VR applications and games: The case study of the Forum of Augustus in Rome" </td> <td> "In the last decades, thanks to the success of the video games industry, the sector of technologies applied to cultural heritage has begun to envisage, in this domain, new possibilities for the dissemination of heritage and the study of the past through edutainment models. More recently, experimentation in the field of virtual archaeology has led to the development of virtual museums and interactive applications. Among these, the “serious game” segment – the application of interactive technologies to the cultural heritage domain – is rapidly growing, also including immersive VR technologies. Applied VR games and applications are characterized by a thorough historical background and a validated 3D reconstruction. Indeed, producing such products requires a tailored workflow and large effort in terms of time and professionals involved to guarantee such faithfulness. Drawing on our previous work in the field of virtual archaeology and referring to recent experiences related to the deployment of applied VR games on PlayStation VR, we describe and assess a workflow for the production of historically accurate 3D assets, targeting interactive, immersive VR products. The workflow is supported by the case study of the Forum of Augustus and different output applications, highlighting peculiarities and issues emerging from a multi and interdisciplinary approach. </td> </tr> </tbody> </table> Through this classification process, we identified projects and publications related to heritage, with different levels of relationship and relevance, but mostly relevant to understanding the research competencies in the domain. The resulting research records were reviewed by experts in the domain, given the occurrence of some false positives. Among the main strengths of this step, it’s worth mentioning the fact that the vocabulary is broad and not restricted to the field of Heritage Science (that is, to STEM applications in Cultural Heritage), as it takes advantage of a variety of available resources. Moreover, by looking directly at the textual data, instead of using the assigned bibliometric areas, we can better capture interdisciplinary research. The limitations of this approach were presented at the beginning of this document: for example, relevant texts could be missed if the correct pattern to match during the search is not found.   ## Vocabulary of concepts related to Key Enabling Technologies in the domain of Cultural Heritage and Culture For the development of this vocabulary, the definition of key enabling technologies in the domain of Cultural Heritage and Culture, was based on reference of the <a href="http://www.irpet.it/archives/53165">report 'Technologies, Cultural Heritage and Culture' published on  March 2019</a> by IRPET. A vocabulary for each Key Enabling Technology (hereafter, KET) was prepared by extracting the relevant concepts, words, technologies and examples from the Platform Report document 'Technologies, Cultural Heritage and Culture, within APPENDIX A. DESCRIPTION OF MAIN TECHNOLOGIES FOR ROADMAP (p. 45-61). Each vocabulary contains a set of terms divided into subdomains. The KETs have been divided into the following six groups: <ul> <li> ICT </li> <li> PHOTONICS, MICRO- AND NANO-ELECTRONICS </li> <li> PLATFORMS </li> <li> NANO AND BIOTECHNOLOGY, ADVANCED MATERIALS </li> <li> PARTICLE ANALYTICAL SYSTEMS </li> </ul> The initial keywords extracted from the document were enriched following the approach based on semantic keyword enrichment based on combination of concurrent keywords and word embeddings  (Duran-Silva et al., 2019; Duran-Silva et al., 2021). This second vocabulary has to be used in combination with the Cultural Heritage vocabulary to capture KETs within the domain of cultural heritage.   ## Use of the controlled vocabulary The definition of the vocabulary does not, per se, allow identifying STI contributions to the domain: this activity in fact boils down to actually matching the terms in the controlled vocabulary to the content of the gathered STI textual records. To successfully carry out this task, a series of pattern matching rules must be defined to capture possible variants of the same concept, such as permutations of words within the concept and/or the presence of null words to be skipped. For this reason, we have carefully crafted matching rules that take into account permutations of words and that allow words within concept to be within a certain distance. Some relatively ambiguous keywords (which may match unwanted pieces of text), have a set of associated “extra” terms. These “extra” terms are defined as further terms that must co-appear, in the same sentence, together with their associated ambiguous keywords. SIRIS Academic has developed the <a href="https://github.com/sirisacademic/VocTagger">voc_tagger tool</a>, a multiprocess information extraction system able to identify hidden knowledge in textual documents using “controlled vocabularies”, openly available at GitHub and compatible with these controlled vocabularies.   ## Bibliography Harpring, P. (2010). Introduction to controlled vocabularies: terminology for art, architecture, and other cultural works. Getty Publications. Nicolau Duran-Silva, Enric Fuster, Francesco Alessandro Massucci, César Parra-Rojas, Arnau Quinquillà, Fernando Roda, Bernardo Rondelli, Nicandro Bovenzi, & Chiara Toietta. (2021). A controlled vocabulary for research and innovation in the field of Artificial Intelligence (AI) (Version 2) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5591987 Duran-Silva, Nicolau, Fuster, Enric, Massucci, Francesco Alessandro, & Quinquillà, Arnau. (2019). A controlled vocabulary defining the semantic perimeter of Sustainable Development Goals (1.2) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3567769

opencc-by-4.0Jul 2023View details →

Data to Deliverable 3.4 - Co-creation process for innovative planning and governance able to facilitate the transition to a water circular economy

Transcription of co-creation workshops; Worksheets filled by groups of the co-creation workshops; and final worksheets filled of the co-creation workshops.

dryad36/100

Data from: Evolution of a cross-feeding interaction following a key innovation in a long-term evolution experiment with Escherichia coli

The evolution of a novel trait can profoundly change an organism's effects on its environment, which can in turn affect the further evolution of that organism and any coexisting organisms. We examine these effects and feedbacks following the evolution of a novel function in the Long-Term Evolution Experiment (LTEE) with Escherichia coli. A characteristic feature of E. coli is its inability to grow aerobically on citrate (Cit−). Nonetheless, a Cit+ variant with this capacity evolved in one LTEE population after 31,000 generations. The Cit+clade then coexisted stably with another clade that retained the ancestral Cit− phenotype. This coexistence was shaped by the evolution of a cross-feeding relationship based on C4-dicarboxylic acids, particularly succinate, fumarate, and malate, that the Cit+ variants release into the medium. Both the Cit− and Cit+ cells evolved to grow on these excreted resources. The evolution of aerobic growth on citrate thus led to a transition from an ecosystem based on a single limiting resource, glucose, to one with at least five resources that were either shared or partitioned between the two coexisting clades. Our findings show that evolutionary novelties can change environmental conditions in ways that facilitate diversity by altering ecosystem structure and the evolutionary trajectories of coexisting lineages.

opencc-zeroAug 2023View details →

Reverse Innovation and Patient Engagement to Improve Quality of Care and Patient Outcomes

ClinicalTrials.gov study NCT02222909. IPD Sharing: NO. Countries: 1. Publications: 3.

restrictedIPD-UNDECIDEDFeb 2026View details →

Triomune Bioequivalence With Innovators

ClinicalTrials.gov study NCT01025830. IPD Sharing: Not stated. Countries: 1. Publications: 1.

Survival Prolongation by Rationale Innovative Genomics

ClinicalTrials.gov study NCT03386929. IPD Sharing: NO. Countries: 4. Publications: 1.

Myopia Adjustment Using Dual-intervention: Red-Light Therapy & Innovative Design in Orthokeratology

ClinicalTrials.gov study NCT06899139. IPD Sharing: NO. Countries: 1. Publications: 1.

controlledIPD-YESFeb 2026View details →

Neonates and Azithromycin, an Innovation in the Treatment of Children in Burkina Faso

ClinicalTrials.gov study NCT03682653. IPD Sharing: YES. Countries: 1. Publications: 5.

controlledIPD-YESFeb 2026View details →

Tele-SSM: A Telephone Technology Innovation in the Intervention on Supported Self-management for Depression in Vietnam

ClinicalTrials.gov study NCT06456775. IPD Sharing: YES. Countries: 1. Publications: 14.

Enabling Patient-Reported Outcomes Through Innovative Care - ACS (EPIC-ACS)

ClinicalTrials.gov study NCT07390006. IPD Sharing: NO. Countries: 1. Publications: 6.

restrictedIPD-UNDECIDEDFeb 2026View details →

Project Impact: An Innovative Approach to Weight Loss Maintenance

ClinicalTrials.gov study NCT02363010. IPD Sharing: Not stated. Countries: 1. Publications: 5.