DataONE

Socio–environmental synthesis as a research approach contributes to broader sustainability policy and practice by reusing data from disparate disciplines in innovative ways. Synthesizing diverse data sources and types of evidence can help to better conceptualize, investigate and address increasingly complex socio–environmental problems. However, sharing qualitative data for re-use remains uncommon when compared to sharing quantitative data. We argue that qualitative data present untapped opportunities for sustainability science, and discuss practical pathways to facilitate and realize the benefits from sharing and reusing qualitative data. However, these opportunities and benefits are also hindered by practical, ethical and epistemological challenges. To address these challenges and accelerate qualitative data sharing, we outline enabling conditions and suggest actions for researchers, institutions, funders, data repository managers and publishers. Opportunities, challenges and recommended targeted actions to accelerate qualitative data sharing to address complex socio–environmental problems

In current web search engines, the relevance between a query and web pages (i.e. documents) is measured by Text Matching (TM) models. The documents retrieved mainly focus on matching text queries themselves but fail to find target information towards the user intent (e.g. the direct answer to question-style queries). Thus the document containing target information that users want may not be ranked at the top 1 in the search result or even not be recalled. Besides, as voice search and voice-powered assistants are entering our life, queries tend to be long tail ones, which needs a search engine evolved into a higher level of semantic relevance matching. Therefore, it is necessary to build an intent-target relevance matching model in modern search scenarios. This paper proposes a unified model of Machine Comprehension-incorporated Relevance Matching (MCRM). Totally, MCRM models how web users choose the relevant documents to read by observing the titles or summaries, and further look for target information from them. To accomplish that, we first formulate two tasks as Text Matching and Target Extracting. For learning each task, a Context-augmented Matching network (ContMatch) and a Matching-fused machine Comprehension network (MatComprehend) are proposed. Then, they are integrated into an end-to-end framework that can not only measure the semantic relevance but also extract the intent-related target, by deeply comprehending the semantics hidden in queries and exploiting intent-target relations between queries and documents. In MCRM, the two tasks are jointly learned by a multi-task learning approach, where the semantic relevance measured by ContMatch, and the intent-target relevance captured by MatComprehend, are combined and enhanced mutually for boosting the final performance. We conduct extensive experiments on real-world data. The experimental results demonstrate the superiority of MCRM against the state-of-the-art relevance models.

<p><span>Jatropha is a plant that has many functions, but this plant can be attacked by various diseases. Expert systems can be applied in identifying so that can help both farmers and extension workers to identify the disease. one of method that can be used is Extreme Learning Machine. Extreme Learning Machine is a method of learning in Neural Network which has a one-time iteration concept in each process. In this study get a maximum accuracy of 66.67% with an average accuracy of 60.61%. This proves the identification using Extreme Learning Machine is better than the comparison method that has been done before.</span></p>