@inproceedings {42796,
	title = {Njord: a fishing trawler dataset},
	journal = {Proceedings of the 13th ACM Multimedia Systems Conference (MMSYS)},
	year = {2022},
	month = {08/2022},
	publisher = {ACM},
	address = {New York, NY, USA},
	abstract = {Fish is one of the main sources of food worldwide. The commercial fishing industry has a lot of different aspects to consider, ranging from sustainability to reporting. The complexity of the domain also attracts a lot of research from different fields like marine biology, fishery sciences, cybernetics, and computer science. In computer science, detection of fishing vessels via for example remote sensing and classification of fish from images or videos using machine learning or other analysis methods attracts growing attention. Surprisingly, little work has been done that considers what is happening on board the fishing vessels. On the deck of the boats, a lot of data and important information are generated with potential applications, such as automatic detection of accidents or automatic reporting of fish caught. This paper presents Njord, a fishing trawler dataset consisting of surveillance videos from a modern off-shore fishing trawler at sea. The main goal of this dataset is to show the potential and possibilities that analysis of such data can provide. In addition to the data, we provide a baseline analysis and discuss several possible research questions this dataset could help answer.},
	isbn = {9781450392839},
	doi = {10.1145/3524273.3532886},
	url = {https://dl.acm.org/doi/pdf/10.1145/3524273.3532886},
	author = {Tor-Arne Schmidt {Nordmo} and Aril Bernhard {Ovesen} and Bj{\o}rn Aslak {Juliussen} and Hicks, Steven and Thambawita, Vajira and H{\r a}vard Dagenborg {Johansen} and Halvorsen, P{\r a}l and Riegler, Michael and Johansen, Dag},
	editor = {Murray, Niall and Simon, Gwendal and Farias, Mylene and Viola, Irene and Montagud, Mario}
}
@inproceedings {26171,
	title = {Depresjon: A Motor Activity Database of Depression Episodes in Unipolar and Bipolar Patients},
	journal = {Proceedings of the 9th ACM Multimedia Systems Conference},
	year = {2018},
	month = {06/2018},
	pages = {472-477},
	publisher = {ACM Press},
	address = {New York, NY, USA},
	abstract = {Wearable sensors measuring different parts of people{\textquoteright}s activity are a common technology nowadays. In research, data collected using these devices also draws attention. Nevertheless, datasets containing sensor data in the field of medicine are rare. Often, data is non-public and only results are published. This makes it hard for other researchers to reproduce and compare results or even collaborate. In this paper we present a unique dataset containing sensor data collected from patients suffering from depression. The dataset contains motor activity recordings of 23 unipolar and bipolar depressed patients and 32 healthy controls. For each patient we provide sensor data over several days of continuous measuring and also some demographic data. The severity of the patients{\textquoteright} depressive state was labeled using ratings done by medical experts on the Montgomery-Asberg Depression Rating Scale (MADRS). In this respect, the here presented dataset can be useful to explore and understand the association between depression and motor activity better. By making this dataset available, we invite and enable interested researchers the possibility to tackle this challenging and important societal problem.},
	isbn = {9781450351928},
	doi = {10.1145/3204949.3208125},
	author = {Garcia-Ceja, Enrique and Riegler, Michael and Jakobsen, Petter and T{\o}rresen, Jim and Nordgreen, Tine and Ketil J. {Oedegaard} and Ole Bernt {Fasmer}},
	editor = {Cesar, Pablo and Zink, Michael and Murray, Niall}
}
