Schema integration is the task of integrating several local schemata into one global database schema. It is a complex, error-prone and time consuming task. Problems arise in recognizing and resolving problems, such as differences and similarities, between two schemata. Problems also arise in integrating static and dynamic schemata. In this thesis, three research topics are addressed: Maintaining Vocabulary in Schema Integration, Integration of Static Schemata and Integration of Static and Dynamic Schemata, while applying the notation in the Enterprise Modeling approach. In Maintaining Vocabulary in Schema Integration an analysis of what semantic loss is and why it occurs in schema integration is conducted. Semantic loss is a problem that should be avoided because both concepts and dependencies might be lost. In the thesis, it is argued that concepts and dependencies should be retained as long as possible in the schemata. This should facilitate user involvement since the users’ vocabulary is retained even after resolving similarities and differences between two schemata. In Integration of Static Schemata two methods are developed. These methods facilitate recognition and resolution of similarities and differences between two conceptual database schemata.  By applying the first method, problems between two schemata can be recognized that otherwise could pass unnoticed; by applying the second method, problems can be resolved without causing semantic loss by retaining concepts and dependencies in the schemata. In Integration of Static and Dynamic Schemata a method on how to integrate static and dynamic schemata is developed. In the method, focus is put on pre- and post-conditions and how to map these to states and state changes in the database. By applying the method, states that are important for the database can be designed and integrated into the conceptual database schema. Also, by applying the method, active database rules can be designed and integrated into the conceptual database schema.

Biodiversity surveys conducted by consulting firms generate valuable biodiversity data. However, the full potential of this data remains untapped without proper structuring and integration into a collaborative network of shared data. This thesis addresses the lack of studies exploring data management in biodiversity surveys and the specific requirements for data structuring. The objective of this Bachelor’s thesis is to introduce the concepts of data management in biodiversity surveys and collaborative data sharing, which have gained scientific popularity due to advancements in data-driven technologies and growing public interest in environmental sustainability. Using a qualitative and exploratory approach based on the Design Science Research paradigm, this research draws general conclusions on data management practices in biodiversity surveys and identifies the requirements for new data standards. The findings of this thesis serve as a starting point for ongoing efforts toward establishing a standardised structure for biodiversity survey data. Such a structure would enable meaningful insights through data-driven technologies and facilitate integration into a collaborative network of shared data. By addressing the gap in understanding data management and data standards in biodiversity surveys, this research contributes to the effective utilization of biodiversity data, ultimately supporting the achievement of sustainability goals. / Naturvärdesinventeringar utförda av konsultfirmor i Sverige genererar en betydande mängd biodiversitetsdata. För att utnyttja värdet av denna data fullt ut är strukturerad hantering och öppen samverkan och delning av data inom öppna data-ekosystem avgörande. Genom att organisera och standardisera datan på ett enhetligt sätt blir den mer användbar och kan bidra till att uppnå globala och nationella hållbarhetsmål. Trots behovet finns det brist på studier som fokuserar på datahantering vid Naturvärdesinventeringar och specifikt specificerar kraven för strukturerad hantering av data. Detta kandidatarbete introducerar ämnet datahantering från Naturvärdesinventeringar samt delning av data inom öppna data-ekosystem. Dessa ämnen har blivit alltmer populära inom forskningen på grund av framstegen inom datadrivna teknologier och det ökande allmänna intresset för miljömässig hållbarhet. Med en kvalitativ och utforskande metod baserad på Design Science Research-paradigmet drar denna forskning generella slutsatser om god praxis för datahantering vid Naturvärdesinventeringar samt identifierar krav som behöver uppfyllas för att kunna dela datan inom öppna data-ekosystem. Resultaten av detta arbete utgör en grund för fortsatt arbete mot att etablera en standardiserad struktur för biodiversitetsdata från Naturvärdesinventeringar. En sådan struktur skulle möjliggöra djupare insikter genom datadrivna teknologier samt underlätta integreringen av data inom öppna data-ekosystem. Genom att belysa kunskapsbristen inom datahantering och datastandarder vid Naturvärdesinventeringar bidrar detta arbete till en effektivare användning av biodiversitetsdata och stödjer därmed arbetet med att uppnå hållbarhetsmålen.