scholarly journals Camtrap DP: A frictionless data exchange format for camera trapping data

Author(s):  
Peter Desmet ◽  
Jakub Bubnicki ◽  
Ben Norton

Camera trapping is one of the most important technologies in conservation and ecological research and a well-established, non-invasive method of collecting field data on animal abundance, distribution, behaviour, temporal activity, and space use (Wearn and Glover-Kapfer 2019). Collectively, camera trapping projects are generating a massive and continuous flow of data, consisting of images and videos (with and without animal observations) and associated identifications (Scotson et al. 2017, Kays et al. 2020). In recent years, significant progress has been made by the global camera trapping community to resolve the challenges this brings, from the development of specialized data management tools and analytical packages, to the application of cloud computing and artificial intelligence to automate species recognition (Tabak et al. 2018). However, to effectively exchange camera trap data between infrastructures and to (automatically) harmonize data into large-scale wildlife datasets, there is a need for a common data exchange format—one that captures the essential information about a camera trap study, allows expression of different study and identification approaches, and aligns well with existing biodiversity standards such as Darwin Core (Wieczorek et al. 2012). Here we present Camera Trap Data Package (Camtrap DP), a data exchange format for camera trap data. It is managed by the Machine Observations Interest Group of Biodiversity Information Standards (TDWG) and developed publicly, soliciting community feedback for every change. Camtrap DP is built on Frictionless Standards, a set of generic specifications to describe and package (tabular) data and metadata. Camtrap DP extends these with specific requirements and constraints for camera trap data. By building on an existing framework, users can employ existing open source software to read and validate Camtrap DP formatted data. Validation especially is useful to automatically check if provided data meets the requirements set forth by Camtrap DP, before analysis or integration. Supported by the major camera trap data management systems e.g. Agouti, TRAPPER, eMammal, and Wildlife Insights, Camtrap DP is reaching its first stable version. The first Camtrap DP dataset was published on Zenodo (Cartuyvels et al. 2021b). This dataset was also published to the Global Biodiversity Information Facility (GBIF) (Cartuyvels et al. 2021a), demonstrating the ability and limitations of transforming the data to the Darwin Core standard.

2012 ◽  
Vol 4 (S1) ◽  
Author(s):  
Georg Birkenheuer ◽  
Dirk Blunk ◽  
Sebastian Breuers ◽  
André Brinkmann ◽  
Ines dos Santos Vieira ◽  
...  

2021 ◽  

Inhalt Normung & Richtlinien Normung in der Zahnradmesstechnik – Stand und aktuelle Entwicklungen . . . . 1 Aktuelle Arbeiten des FA 3.61 Verzahnungsmesstechnik, neue Richtlinien . . . . . 7 Die Hirth-Verzahnung – ein Klassiker etabliert sich als moderne Welle-Nabe-Verbindung . . . .23 Berührungslose Messung Optische Verzahnungsmessung, schnell und hochgenau? – Hybride Verzahnungsmessung mit Klingelnberg . . . . . . .35 Weniger Zähne knirschen – Vollständige und flächenhafte Auswertung von Verzahnungsgeometrien verschieden großer Werkstücke aus einer Hand . . . . .47 Einsatz optischer Messtechnik für die Qualitätsprüfung von linearen Zahnstangen – Durchsatz, Genauigkeit und Flexibilität durch variable Inspektion . . . . . 55 Geräuschentwicklung & Ursachen Vom Geräusch zur Ursache: Entstehung von Welligkeiten auf Verzahnungen . . . . . .69 Verzahnungsgeräusche – Beispiele zu ursächlichen Geometrieabweichungen an Verzahnungen. . . . . . . .83 Digitalisierung & Software GDE – Gear Data Exchange Format – Durchgängiger Datenaustausch in der Zahnradproduktion . . . . ....


Author(s):  
Atul Jain ◽  
ShashiKant Gupta

JavaScript Object Notation is a text-based data exchange format for structuring data between a server and web application on the client-side. It is basically a data format, so it is not limited to Ajax-style web applications and can be used with API’s to exchange or store information. However, the whole data never to be used by the system or application, It needs some extract of a piece of requirement that may vary person to person and with the changing of time. The searching and filtration from the JSON string are very typical so most of the studies give only basics operation to query the data from the JSON object. The aim of this paper to find out all the methods with different technology to search and filter with JSON data. It explains the extensive results of previous research on the JSONiq Flwor expression and compares it with the json-query module of npm to extract information from JSON. This research has the intention of achieving the data from JSON with some advanced operators with the help of a prototype in json-query package of NodeJS. Thus, the data can be filtered out more efficiently and accurately without the need for any other programming language dependency. The main objective is to filter the JSON data the same as the SQL language query.


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