Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Seiten | 6-26 |
| Seitenumfang | 21 |
| ISBN (elektronisch) | 978-3-319-66435-4 |
| Publikationsstatus | Veröffentlicht - 2017 |
Publikationsreihe
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Band | 10264 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (elektronisch) | 1611-3349 |
Abstract
Crowdsourcing solutions are increasingly being adopted across a variety of domains these days. An important consequence of the flourishing crowdsourcing markets is that experiments which were traditionally carried out in laboratories on a much smaller scale can now tap into the immense potential of online labor. Researchers in different fields have shown considerable interest in attempting to carry out priorly constrained lab experiments in the crowd. In this chapter, we reflect on the key factors to consider while transitioning from controlled laboratory experiments to large scale experiments in the crowd.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Theoretische Informatik
- Informatik (insg.)
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2017. S. 6-26 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 10264 LNCS).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Beitrag in Buch/Sammelwerk › Forschung › Peer-Review
}
TY - CHAP
T1 - Crowdsourcing versus the laboratory
T2 - Towards human-centered experiments using the crowd
AU - Gadiraju, Ujwal
AU - Möller, Sebastian
AU - Nöllenburg, Martin
AU - Saupe, Dietmar
AU - Egger-Lampl, Sebastian
AU - Archambault, Daniel
AU - Fisher, Brian
N1 - Funding Information: Acknowledgment. We would like to thank Dagstuhl for facilitating the seminar (titled, ‘Evaluation in the Crowd: Crowdsourcing and Human-Centred Experiments ’) that brought about this collaboration. Part of this work (Sect. 4) was supported by the German Research Foundation (DFG) within project A05 of SFB/Transregio 161. We also thank Andrea Mauri and Christian Keimel for their valuable contributions and feedback during discussions.
PY - 2017
Y1 - 2017
N2 - Crowdsourcing solutions are increasingly being adopted across a variety of domains these days. An important consequence of the flourishing crowdsourcing markets is that experiments which were traditionally carried out in laboratories on a much smaller scale can now tap into the immense potential of online labor. Researchers in different fields have shown considerable interest in attempting to carry out priorly constrained lab experiments in the crowd. In this chapter, we reflect on the key factors to consider while transitioning from controlled laboratory experiments to large scale experiments in the crowd.
AB - Crowdsourcing solutions are increasingly being adopted across a variety of domains these days. An important consequence of the flourishing crowdsourcing markets is that experiments which were traditionally carried out in laboratories on a much smaller scale can now tap into the immense potential of online labor. Researchers in different fields have shown considerable interest in attempting to carry out priorly constrained lab experiments in the crowd. In this chapter, we reflect on the key factors to consider while transitioning from controlled laboratory experiments to large scale experiments in the crowd.
UR - http://www.scopus.com/inward/record.url?scp=85031492049&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-66435-4_2
DO - 10.1007/978-3-319-66435-4_2
M3 - Contribution to book/anthology
AN - SCOPUS:85031492049
SN - 978-3-319-66434-7
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 6
EP - 26
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ER -