Journal archives for November 2020

November 02, 2020

22. Houd je van “Tinder” ? Deze identificatie app lijkt hier namelijk enorm op

Ben je ook zo gecharmeerd van Tinder ? Voor iNaturalist is een app gemaakt die net zo werkt als Tinder. Op de webpagina van iNaturalist.org is al een erg goed werkende Identificatie tool beschikbaar. Maar als je op een prettige en snelle manier de fotos in de goede soortgroep Vogels, Planten of Dieren wil zetten (rubriceren) is er nog wel een verbetering mogelijk. Deze iNaturalist “Tinder” is er voorzowel de iPhone als de Android.

https://forum.inaturalist.org/t/am-looking-for-some-test-users-of-an-identification-app/430

On different occasions in the forum I have now seen it mentioned that there is a growing discrepancy in numbers between the amount of observers and identifiers.

From my experience the Identification tool on the homepage is already extremely well done . For some specific tasks though, e.g. identifying lots of observations coarsely, I think there might be some improvements. I would like to help to make identifying a bit easier and a bit more fun.

I am a smartphone app developer and have made a rough “tinder” like identifying app. For now I have followed only my own needs in the development. However, I am looking for some people who want to try it out and give me some feedback to see if that is something that the community could possibly use. Also to see where it would need to be developed further.

If you are interested wave here or better send me a DM in iNat. Currently, I have an iPhone and Android version to test.

https://forum.inaturalist.org/t/am-looking-for-some-test-users-of-an-identification-app/430
https://forum.inaturalist.org/t/making-identifying-more-fun-interesting-wiki/242/14


I know I can get a bit discouraged sometimes when the first batch of observations I see in Identify are all “picked over”, either because they’re all species that are difficult to identify, or the photos aren’t high quality or sufficient for showing diagnostic characters, or something else. A few tricks for finding more interesting/IDable/fun observations to identify:

Any other tricks you have for making identifying more fun/interesting?

Plenty of filters to play around with:

https://forum.inaturalist.org/t/making-identifying-more-fun-interesting-wiki/242/14

Krijg Lol in het helpen van anderen met hun waarneming

I know I can get a bit discouraged sometimes when the first batch of observations I see in Identify are all “picked over”, either because they’re all species that are difficult to identify, or the photos aren’t high quality or sufficient for showing diagnostic characters, or something else. A few tricks for finding more interesting/IDable/fun observations to identify:

Any other tricks you have for making identifying more fun/interesting?

Plenty of filters to play around with:


As I’ve started using this, my technique is to coarsely id the ones I know I can go finer with. After doing that for a bit, I pull up this view on desktop:

https://www.inaturalist.org/observations/identify?reviewed=true&order_by=updated_at&lrank=kingdom&per_page=100 1

This view pulls up all the ones I just id’d to go back over for both refinement and checking. The checking step is necessary for me because besides catching my inevitable mistakes, I like to zoom in on many things to doublecheck for birds in trees etc. Even with my extra step that lengthens the id process, I feel the overall process is more efficient for me than without using the app.

An in-app zoom function (pinch-expand?) would be handy if doable.

Houd je van “Tinder” ? Deze identificatie app lijkt hier namelijk enorm op (23)

Yes.

You can go to the identify page, set the filter for your observations, check all the quality grades, and set the Without Annotation field to Alive or Dead = Any

This will show you all your observations that don’t have Alive or Dead annotated.

Posted on November 02, 2020 22:25 by ahospers ahospers | 0 comments | Leave a comment

22. Houd je van “Tinder” ? Deze identificatie app lijkt hier namelijk enorm op

Ben je ook zo gecharmeerd van Tinder ? Voor iNaturalist is een app gemaakt die net zo werkt als Tinder. Op de webpagina van iNaturalist.org is al een erg goed werkende Identificatie tool beschikbaar. Maar als je op een prettige en snelle manier de fotos in de goede soortgroep Vogels, Planten of Dieren wil zetten (rubriceren) is er nog wel een verbetering mogelijk. Deze iNaturalist “Tinder” is er voorzowel de iPhone als de Android.

https://forum.inaturalist.org/t/am-looking-for-some-test-users-of-an-identification-app/430

On different occasions in the forum I have now seen it mentioned that there is a growing discrepancy in numbers between the amount of observers and identifiers.

From my experience the Identification tool on the homepage is already extremely well done . For some specific tasks though, e.g. identifying lots of observations coarsely, I think there might be some improvements. I would like to help to make identifying a bit easier and a bit more fun.

I am a smartphone app developer and have made a rough “tinder” like identifying app. For now I have followed only my own needs in the development. However, I am looking for some people who want to try it out and give me some feedback to see if that is something that the community could possibly use. Also to see where it would need to be developed further.

If you are interested wave here or better send me a DM in iNat. Currently, I have an iPhone and Android version to test.

https://forum.inaturalist.org/t/am-looking-for-some-test-users-of-an-identification-app/430
https://forum.inaturalist.org/t/making-identifying-more-fun-interesting-wiki/242/14


I know I can get a bit discouraged sometimes when the first batch of observations I see in Identify are all “picked over”, either because they’re all species that are difficult to identify, or the photos aren’t high quality or sufficient for showing diagnostic characters, or something else. A few tricks for finding more interesting/IDable/fun observations to identify:

Any other tricks you have for making identifying more fun/interesting?

Plenty of filters to play around with:

https://forum.inaturalist.org/t/making-identifying-more-fun-interesting-wiki/242/14

Krijg Lol in het helpen van anderen met hun waarneming

I know I can get a bit discouraged sometimes when the first batch of observations I see in Identify are all “picked over”, either because they’re all species that are difficult to identify, or the photos aren’t high quality or sufficient for showing diagnostic characters, or something else. A few tricks for finding more interesting/IDable/fun observations to identify:

Any other tricks you have for making identifying more fun/interesting?

Plenty of filters to play around with:


As I’ve started using this, my technique is to coarsely id the ones I know I can go finer with. After doing that for a bit, I pull up this view on desktop:

https://www.inaturalist.org/observations/identify?reviewed=true&order_by=updated_at&lrank=kingdom&per_page=100 1

This view pulls up all the ones I just id’d to go back over for both refinement and checking. The checking step is necessary for me because besides catching my inevitable mistakes, I like to zoom in on many things to doublecheck for birds in trees etc. Even with my extra step that lengthens the id process, I feel the overall process is more efficient for me than without using the app.

An in-app zoom function (pinch-expand?) would be handy if doable.

Houd je van “Tinder” ? Deze identificatie app lijkt hier namelijk enorm op (22)

Yes.

You can go to the identify page, set the filter for your observations, check all the quality grades, and set the Without Annotation field to Alive or Dead = Any

This will show you all your observations that don’t have Alive or Dead annotated.

Posted on November 02, 2020 22:44 by ahospers ahospers | 0 comments | Leave a comment

22. Houd je van “Tinder” ? Deze identificatie app lijkt hier namelijk enorm op

Ben je ook zo gecharmeerd van Tinder ? Voor iNaturalist is een app gemaakt die net zo werkt als Tinder. Op de webpagina van iNaturalist.org is al een erg goed werkende Identificatie tool beschikbaar. Maar als je op een prettige en snelle manier de fotos in de goede soortgroep Vogels, Planten of Dieren wil zetten (rubriceren) is er nog wel een verbetering mogelijk. Deze iNaturalist “Tinder” is er voorzowel de iPhone als de Android.

https://forum.inaturalist.org/t/am-looking-for-some-test-users-of-an-identification-app/430

On different occasions in the forum I have now seen it mentioned that there is a growing discrepancy in numbers between the amount of observers and identifiers.

From my experience the Identification tool on the homepage is already extremely well done . For some specific tasks though, e.g. identifying lots of observations coarsely, I think there might be some improvements. I would like to help to make identifying a bit easier and a bit more fun.

I am a smartphone app developer and have made a rough “tinder” like identifying app. For now I have followed only my own needs in the development. However, I am looking for some people who want to try it out and give me some feedback to see if that is something that the community could possibly use. Also to see where it would need to be developed further.

If you are interested wave here or better send me a DM in iNat. Currently, I have an iPhone and Android version to test.

https://forum.inaturalist.org/t/am-looking-for-some-test-users-of-an-identification-app/430
https://forum.inaturalist.org/t/making-identifying-more-fun-interesting-wiki/242/14


I know I can get a bit discouraged sometimes when the first batch of observations I see in Identify are all “picked over”, either because they’re all species that are difficult to identify, or the photos aren’t high quality or sufficient for showing diagnostic characters, or something else. A few tricks for finding more interesting/IDable/fun observations to identify:

Any other tricks you have for making identifying more fun/interesting?

Plenty of filters to play around with:

https://forum.inaturalist.org/t/making-identifying-more-fun-interesting-wiki/242/14

Krijg Lol in het helpen van anderen met hun waarneming

I know I can get a bit discouraged sometimes when the first batch of observations I see in Identify are all “picked over”, either because they’re all species that are difficult to identify, or the photos aren’t high quality or sufficient for showing diagnostic characters, or something else. A few tricks for finding more interesting/IDable/fun observations to identify:

Any other tricks you have for making identifying more fun/interesting?

Plenty of filters to play around with:


As I’ve started using this, my technique is to coarsely id the ones I know I can go finer with. After doing that for a bit, I pull up this view on desktop:

https://www.inaturalist.org/observations/identify?reviewed=true&order_by=updated_at&lrank=kingdom&per_page=100 1

This view pulls up all the ones I just id’d to go back over for both refinement and checking. The checking step is necessary for me because besides catching my inevitable mistakes, I like to zoom in on many things to doublecheck for birds in trees etc. Even with my extra step that lengthens the id process, I feel the overall process is more efficient for me than without using the app.

An in-app zoom function (pinch-expand?) would be handy if doable.

Houd je van “Tinder” ? Deze identificatie app lijkt hier namelijk enorm op (22)

Yes.

You can go to the identify page, set the filter for your observations, check all the quality grades, and set the Without Annotation field to Alive or Dead = Any

This will show you all your observations that don’t have Alive or Dead annotated.

Posted on November 02, 2020 22:44 by ahospers ahospers | 0 comments | Leave a comment

22. Houd je van “Tinder” ? Deze identificatie app lijkt hier namelijk enorm op

Ben je ook zo gecharmeerd van Tinder ? Voor iNaturalist is een app gemaakt die net zo werkt als Tinder. Op de webpagina van iNaturalist.org is al een erg goed werkende Identificatie tool beschikbaar. Maar als je op een prettige en snelle manier de fotos in de goede soortgroep Vogels, Planten of Dieren wil zetten (rubriceren) is er nog wel een verbetering mogelijk. Deze iNaturalist “Tinder” is er voorzowel de iPhone als de Android.

https://forum.inaturalist.org/t/am-looking-for-some-test-users-of-an-identification-app/430

On different occasions in the forum I have now seen it mentioned that there is a growing discrepancy in numbers between the amount of observers and identifiers.

From my experience the Identification tool on the homepage is already extremely well done . For some specific tasks though, e.g. identifying lots of observations coarsely, I think there might be some improvements. I would like to help to make identifying a bit easier and a bit more fun.

I am a smartphone app developer and have made a rough “tinder” like identifying app. For now I have followed only my own needs in the development. However, I am looking for some people who want to try it out and give me some feedback to see if that is something that the community could possibly use. Also to see where it would need to be developed further.

If you are interested wave here or better send me a DM in iNat. Currently, I have an iPhone and Android version to test.

https://forum.inaturalist.org/t/am-looking-for-some-test-users-of-an-identification-app/430
https://forum.inaturalist.org/t/making-identifying-more-fun-interesting-wiki/242/14


I know I can get a bit discouraged sometimes when the first batch of observations I see in Identify are all “picked over”, either because they’re all species that are difficult to identify, or the photos aren’t high quality or sufficient for showing diagnostic characters, or something else. A few tricks for finding more interesting/IDable/fun observations to identify:

Any other tricks you have for making identifying more fun/interesting?

Plenty of filters to play around with:

https://forum.inaturalist.org/t/making-identifying-more-fun-interesting-wiki/242/14

Krijg Lol in het helpen van anderen met hun waarneming

I know I can get a bit discouraged sometimes when the first batch of observations I see in Identify are all “picked over”, either because they’re all species that are difficult to identify, or the photos aren’t high quality or sufficient for showing diagnostic characters, or something else. A few tricks for finding more interesting/IDable/fun observations to identify:

Any other tricks you have for making identifying more fun/interesting?

Plenty of filters to play around with:


As I’ve started using this, my technique is to coarsely id the ones I know I can go finer with. After doing that for a bit, I pull up this view on desktop:

https://www.inaturalist.org/observations/identify?reviewed=true&order_by=updated_at&lrank=kingdom&per_page=100 1

This view pulls up all the ones I just id’d to go back over for both refinement and checking. The checking step is necessary for me because besides catching my inevitable mistakes, I like to zoom in on many things to doublecheck for birds in trees etc. Even with my extra step that lengthens the id process, I feel the overall process is more efficient for me than without using the app.

An in-app zoom function (pinch-expand?) would be handy if doable.

Houd je van “Tinder” ? Deze identificatie app lijkt hier namelijk enorm op (22)

Yes.

You can go to the identify page, set the filter for your observations, check all the quality grades, and set the Without Annotation field to Alive or Dead = Any

This will show you all your observations that don’t have Alive or Dead annotated.

Posted on November 02, 2020 22:45 by ahospers ahospers | 0 comments | Leave a comment

November 03, 2020

23. Leuke Projecten op iNaturalist zoals Camera vallen, Aliens species en audio waarnemingen

Op iNaturalist zijn honderden, so niet duizenden projecten die Microscopie, Voedselplanten van rupsen, schimmels of Nectarplanten aangeven. Ik ken een voorbeeld waar je aan de hand van vliegtijden van mannetjes, voedselzoeksters, werksters of koninginnen de miersoort kon achterhalen.
Nog niet gevonden maar " Found Feathers", Scatology, North American Animal Tracking Database en Tinctorial zijn speciale projecten met bijzondere waarnemingen.

  1. Skulls and bones
  2. Global Roadkill Observations
  3. Challenging Bird Identifications
  4. Birds on Ships
  5. Found Feathers
  6. Amazing Aberrants
  7. North American Caterpillars
  8. Leafminers of North America
  9. Galls of North America
  10. Leaf and Plant Galls
  11. European Plant Galler Faunistics

https://www.inaturalist.org/projects/audio-observations-from-around-the-world 2
https://www.inaturalist.org/projects/euromediterranean-alien-species
https://www.inaturalist.org/projects/alien-parrots-observatory 1
https://www.inaturalist.org/projects/camera-traps-trail-cams 1
https://www.inaturalist.org/projects/cal-cam-california-trail-cams
https://www.inaturalist.org/projects/hand-feeding 1
https://www.inaturalist.org/projects/dead-animals 1

Een paar projecten die de interactie tussen Motten, vlinders en Nectorplanten of Voedselplanten weergeven
https://www.inaturalist.org/projects/butterfly-moth-nectar-plants
https://www.inaturalist.org/projects/butterfly-moth-host-plants

Deze kan een filter ook niet vinden
https://www.inaturalist.org/projects/never-home-alone-the-wild-life-of-homes 11

Of Microscopie
https://www.inaturalist.org/projects/microscopic-microbes

En natuurlijk Vogels
https://www.inaturalist.org/projects/bird-feeders 16
https://www.inaturalist.org/projects/dead-birds

Leuke Projecten op iNaturalist zoals Camera vallen, Aliens species en audio waarnemingen(23)

Posted on November 03, 2020 14:51 by ahospers ahospers | 1 comment | Leave a comment

23. Leuke Projecten op iNaturalist zoals Camera vallen, Microscopie, Aliens en audio waarnemingen

Op iNaturalist zijn honderden, so niet duizenden projecten die Microscopie, Voedselplanten van rupsen, schimmels of Nectarplanten aangeven. Ik ken een voorbeeld waar je aan de hand van vliegtijden van mannetjes, voedselzoeksters, werksters of koninginnen de miersoort kon achterhalen.
Nog niet gevonden maar " Found Feathers", Scatology, North American Animal Tracking Database en Tinctorial zijn speciale projecten met bijzondere waarnemingen.

  1. Skulls and bones
  2. Global Roadkill Observations
  3. Challenging Bird Identifications
  4. Birds on Ships
  5. Found Feathers
  6. Amazing Aberrants
  7. North American Caterpillars
  8. Leafminers of North America
  9. Galls of North America
  10. Leaf and Plant Galls
  11. European Plant Galler Faunistics

https://www.inaturalist.org/projects/audio-observations-from-around-the-world 2
https://www.inaturalist.org/projects/euromediterranean-alien-species
https://www.inaturalist.org/projects/alien-parrots-observatory 1
https://www.inaturalist.org/projects/camera-traps-trail-cams 1
https://www.inaturalist.org/projects/cal-cam-california-trail-cams
https://www.inaturalist.org/projects/hand-feeding 1
https://www.inaturalist.org/projects/dead-animals 1
https://www.inaturalist.org/projects/orthoptera
https://www.inaturalist.org/projects/passengers-parasites-taking-rides

Een paar projecten die de interactie tussen Motten, vlinders en Nectorplanten of Voedselplanten weergeven
https://www.inaturalist.org/projects/butterfly-moth-nectar-plants
https://www.inaturalist.org/projects/butterfly-moth-host-plants

Deze kan een filter ook niet vinden
https://www.inaturalist.org/projects/never-home-alone-the-wild-life-of-homes 11

Of Microscopie
https://www.inaturalist.org/projects/microscopic-microbes

En natuurlijk Vogels
https://www.inaturalist.org/projects/bird-feeders 16
https://www.inaturalist.org/projects/dead-birds

Leuke Projecten op iNaturalist zoals Camera vallen, Aliens species en audio waarnemingen(23)

Posted on November 03, 2020 15:22 by ahospers ahospers | 1 comment | Leave a comment

23. Leuke Projecten op iNaturalist zoals Camera vallen, Aliens species en audio waarnemingen

Op iNaturalist zijn honderden, so niet duizenden projecten die Microscopie, Voedselplanten van rupsen, schimmels of Nectarplanten aangeven. Ik ken een voorbeeld waar je aan de hand van vliegtijden van mannetjes, voedselzoeksters, werksters of koninginnen de miersoort kon achterhalen.
Nog niet gevonden maar " Found Feathers", Scatology, North American Animal Tracking Database en Tinctorial zijn speciale projecten met bijzondere waarnemingen.

  1. Skulls and bones
  2. Global Roadkill Observations
  3. Challenging Bird Identifications
  4. Birds on Ships
  5. Found Feathers
  6. Amazing Aberrants
  7. North American Caterpillars
  8. Leafminers of North America
  9. Galls of North America
  10. Leaf and Plant Galls
  11. European Plant Galler Faunistics

https://www.inaturalist.org/projects/audio-observations-from-around-the-world 2
https://www.inaturalist.org/projects/euromediterranean-alien-species
https://www.inaturalist.org/projects/alien-parrots-observatory 1
https://www.inaturalist.org/projects/camera-traps-trail-cams 1
https://www.inaturalist.org/projects/cal-cam-california-trail-cams
https://www.inaturalist.org/projects/hand-feeding 1
https://www.inaturalist.org/projects/dead-animals 1

Een paar projecten die de interactie tussen Motten, vlinders en Nectorplanten of Voedselplanten weergeven
https://www.inaturalist.org/projects/butterfly-moth-nectar-plants
https://www.inaturalist.org/projects/butterfly-moth-host-plants

Deze kan een filter ook niet vinden
https://www.inaturalist.org/projects/never-home-alone-the-wild-life-of-homes 11

Of Microscopie
https://www.inaturalist.org/projects/microscopic-microbes

En natuurlijk Vogels
https://www.inaturalist.org/projects/bird-feeders 16
https://www.inaturalist.org/projects/dead-birds

Leuke Projecten op iNaturalist zoals Camera vallen, Aliens species en audio waarnemingen(23)

Posted on November 03, 2020 15:24 by ahospers ahospers | 0 comments | Leave a comment

23. Leuke Projecten op iNaturalist zoals Camera vallen, Aliens species en audio waarnemingen

Op iNaturalist zijn honderden, so niet duizenden projecten die Microscopie, Voedselplanten van rupsen, schimmels of Nectarplanten aangeven. Ik ken een voorbeeld waar je aan de hand van vliegtijden van mannetjes, voedselzoeksters, werksters of koninginnen de miersoort kon achterhalen.
Nog niet gevonden maar " Found Feathers", Scatology, North American Animal Tracking Database en Tinctorial zijn speciale projecten met bijzondere waarnemingen.

  1. Skulls and bones
  2. Global Roadkill Observations
  3. Challenging Bird Identifications
  4. Birds on Ships
  5. Found Feathers
  6. Amazing Aberrants
  7. North American Caterpillars
  8. Leafminers of North America
  9. Galls of North America
  10. Leaf and Plant Galls
  11. European Plant Galler Faunistics

https://www.inaturalist.org/projects/audio-observations-from-around-the-world 2
https://www.inaturalist.org/projects/euromediterranean-alien-species
https://www.inaturalist.org/projects/alien-parrots-observatory 1
https://www.inaturalist.org/projects/camera-traps-trail-cams 1
https://www.inaturalist.org/projects/cal-cam-california-trail-cams
https://www.inaturalist.org/projects/hand-feeding 1
https://www.inaturalist.org/projects/dead-animals 1

Een paar projecten die de interactie tussen Motten, vlinders en Nectorplanten of Voedselplanten weergeven
https://www.inaturalist.org/projects/butterfly-moth-nectar-plants
https://www.inaturalist.org/projects/butterfly-moth-host-plants

Deze kan een filter ook niet vinden
https://www.inaturalist.org/projects/never-home-alone-the-wild-life-of-homes 11

Of Microscopie
https://www.inaturalist.org/projects/microscopic-microbes

En natuurlijk Vogels
https://www.inaturalist.org/projects/bird-feeders 16
https://www.inaturalist.org/projects/dead-birds

Leuke Projecten op iNaturalist zoals Camera vallen, Aliens species en audio waarnemingen(23)

Posted on November 03, 2020 15:27 by ahospers ahospers | 0 comments | Leave a comment

25. iNaturalist in het Nieuws, In de Druk, Op de TV, Citizen Sciences

https://www.inaturalist.org/pages/press Turorial https://www.youtube.com/watch?v=eS_9KpXgPdk

Citizen Science Burger Wetenschap

  1. Piper, Alana. “Digital Crowdsourcing and Public Understandings of the Past: Citizen Historians Meet Criminal Characters.” History Australia 0, no. 0 (August 14, 2020): 1–17. https://doi.org/10.1080/14490854.2020.1796500.
  2. Herodotou, Christothea, Maria Aristeidou, Grant Miller, Heidi Ballard, and Lucy Robinson. “What Do 1. We Know about Young Volunteers? An Exploratory Study of Participation in Zooniverse.” Citizen Science: Theory and Practice 5, no. 1 (January 13, 2020). http://oro.open.ac.uk/69002/.
  3. Aristeidou, Maria, and Christothea Herodotou. “Online Citizen Science: A Systematic Review of Effects on Learning and Scientific Literacy.” Citizen Science: Theory and Practice 5, no. 1 (2020): 1–12.
  4. Tyson, Anya. “NOLS and Nutcrackers: The Motivations, Barriers, and Benefits Experienced by Outdoor Adventure Educators in the Context of a Citizen Science Project.” Citizen Science: Theory and Practice 4, no. 1 (June 13, 2019): 20. https://doi.org/10.5334/cstp.127.
  5. Locritani, M., S. Merlino, and M. Abbate. “Assessing the Citizen Science Approach as Tool to Increase Awareness on the Marine Litter Problem.” Marine Pollution Bulletin 140 (March 1, 2019): 320–29. https://doi.org/10.1016/j.marpolbul.2019.01.023.
  6. Kermish-Allen, Ruth, Karen Peterman, and Christine Bevc. “The Utility of Citizen Science Projects in K-5 Schools: Measures of Community Engagement and Student Impacts.” Cultural Studies of Science Education 14, no. 3 (September 1, 2019): 627–41. https://doi.org/10.1007/s11422-017-9830-4.
  7. Bonney, Rick, Tina B. Phillips, Heidi L. Ballard, and Jody W. Enck. “Can Citizen Science Enhance Public Understanding of Science?” Public Understanding of Science 25, no. 1 (January 1, 2016): 2–16. https://doi.org/10.1177/0963662515607406.
  8. McKinley, Duncan C., Abe J. Miller-Rushing, Heidi L. Ballard, Rick Bonney, Hutch Brown, Susan C. Cook-Patton, Daniel M. Evans, et al. “Citizen Science Can Improve Conservation Science, Natural Resource Management, and Environmental Protection.” Biological Conservation, The role of citizen science in biological conservation, 208 (April 1, 2017): 15–28. https://doi.org/10.1016/j.biocon.2016.05.015.
  9. Mitchell, Nicola, Maggie Triska, Andrea Liberatore, Linden Ashcroft, Richard Weatherill, and Nancy Longnecker. “Benefits and Challenges of Incorporating Citizen Science into University Education.” PloS One 12, no. 11 (2017): e0186285.
  10. Kern, Anne L., Gillian H. Roehrig, Devarati Bhattacharya, Jeremy Y. Wang, Frank A. Finley, Bree J. Reynolds, and Younkyeong Nam. “Drawing on Place and Culture for Climate Change Education in Native Communities.” In EcoJustice, Citizen Science and Youth Activism, 121–38. Environmental Discourses in Science Education. Springer, Cham, 2015. https://doi.org/10.1007/978-3-319-11608-2_8.
  11. Bonney, Rick, Caren B. Cooper, Janis Dickinson, Steve Kelling, Tina Phillips, Kenneth V. Rosenberg, and Jennifer Shirk. “Citizen Science: A Developing Tool for Expanding Science Knowledge and Scientific Literacy.” BioScience 59, no. 11 (December 1, 2009): 977–84. https://doi.org/10.1525/bio.2009.59.11.9.
  12. Brosnan, Tess, Sebastian Filep, and Jenny Rock. “Exploring Synergies: Hopeful Tourism and Citizen Science.” Annals of Tourism Research 53 (July 2015): 96–98. https://doi.org/10.1016/j.annals.2015.05.002.
  13. Brossard, Dominique, Bruce Lewenstein, and Rick Bonney. “Scientific Knowledge and Attitude Change: The Impact of a Citizen Science Project.” International Journal of Science Education 27, no. 9 (January 1, 2005): 1099–1121. https://doi.org/10.1080/09500690500069483.
  14. Conrad, Cathy C., and Krista G. Hilchey. “A Review of Citizen Science and Community-Based Environmental Monitoring: Issues and Opportunities.” Environmental Monitoring and Assessment 176, no. 1 (May 1, 2011): 273–91. https://doi.org/10.1007/s10661-010-1582-5.
  15. Cox, Joe, Eun Young Oh, Brooke Simmons, Chris Lintott, Karen Masters, Anita Greenhill, Gary Graham, and Kate Holmes. “Defining and Measuring Success in Online Citizen Science: A Case Study of Zooniverse Projects.” Computing in Science Engineering 17, no. 4 (July 2015): 28–41. https://doi.org/10.1109/MCSE.2015.65.
  16. Evans, Celia, Eleanor Abrams, Robert Reitsma, Karin Roux, Laura Salmonsen, and Peter P. Marra. “The Neighborhood Nestwatch Program: Participant Outcomes of a Citizen-Science Ecological Research Project.” Conservation Biology 19, no. 3 (2005): 589–94. https://doi.org/10.1111/j.1523-1739.2005.00s01.x.
  17. Prather, Edward E., Sébastien Cormier, Colin S. Wallace, Chris Lintott, M. Jordan Raddick, and Arfon Smith. “Measuring the Conceptual Understandings of Citizen Scientists Participating in Zooniverse Projects: A First Approach.” Astronomy Education Review 12, no. 1 (December 2013): 1–14. https://doi.org/10.3847/AER2013002.
  18. Toomey, Anne H., and Margret C. Domroese. “Can Citizen Science Lead to Positive Conservation Attitudes and Behaviors?” Human Ecology Review 20, no. 1 (2013): 50–62.

iNaturalist is frequently mentioned in a variety of local news outlets. We suggest searching Google News for recent examples. Below are some notable examples of broader media coverage about iNaturalist.

2020 August, The Conversation

The next invasion of insect pests will be discovered via social media

"iNaturalist has become a world-leading resource that combines observational data with artificial intelligence and community expertise to bring natural history into the digital age." by Paul Manning and Morgan Jackson

2020 August, The New York Times

The Pleasures of Moth Watching

How "mothing" turned Margaret Roach into a citizen scientist.

2020 August, The New York Times

17 Learning Tools For Your Next Outdoor Excursion

Stephanie Rosenbloom endorses Seek by iNaturalist for learning more about what's around you.

2020 August, Sierra

iNaturalist Does More Than ID Plants

iNaturalist also helped Korrin L. Bishop find meaning during the pandemic.

2020 June, Mashable

Don't know how to tell trees apart? There's an app for that.

Sarah Lindenfield Hall tells of neighborhood nature explorations with her daughter during the covid-19 pandemic.

2020 February, Wired

The Secret to Enjoying Nature Is … Your Phone

Catherine LeClair writes about iNaturalist and Seek by iNaturalist, and how using them helped her become more connected to nature.

2020 February, USA Today

How a bizarre, monster fish hoodwinked researchers and reeled in a wave of citizen scientists

Covers the collaborative ID of a hoodwinker sunfish found in California.

2019 July, CBC.ca and CTV

What's that bug? How to identify any plant or animal with your smartphone

iNaturalist and iNaturalist Canada are discussed in this article, as well as the importance of crowdsourced data. iNaturalist staff member Carrie Seltzer was also interviewed for CTV in connection with the article.

2019 June, Bay Nature

An Update to the App to Identify (Almost) Anything (Almost) Anywhere

Seek team members discuss Seek 2.0's live ID suggestions feature.

2019 Apr, FlyTimes

Diptera and iNaturalist: A case study from Asiloidea

Dipterists Even Dankowicz and Chris Cohen discuss the use of iNaturalist in their research.

2019 Apr, Slate

Plants and Birds Need Privacy Online, Too

April Glaser explores the tradeoffs between sharing biodiversity information and keeping it secret, using eBird and iNaturalist as examples.

2019 Feb, NPR

Scientists Shocked By Rare, Giant Sunfish Washed Up On California Beach

The iNaturalist observation of a rare Hoodwinker Sunfish in the wrong hemisphere spawned many news stories in outlets around the world. This piece by Merrit Kennedy describes the dialogue on iNaturalist that made this discovery possible.

2019 Jan, The Daily - Case Western Reserve University

How male dragonflies adapt wing color to temperature

Hundreds of dragonfly photos on iNaturalist were examined as part of a study looking into the connection between wing color and local temperature.

2018 Oct, Mongabay

The iNaturalist species data sharing platform reaches one million users

Sue Palminteri talking with co-director Scott Loarie about how iNaturalist has scaled over the last 10 years and what new challenges emerge with new technologies.

2018 Oct, The New York Times

With Bugs, You're Never Home Alone

Coverage by Nicola Twilley of the Never Home Alone project (and book of the same name by Rob Dunn) that aims to understand the wildlife inside our homes.

2018 Aug, Associated Press

The Green Big Apple: New Yorkers document the city’s plants

Emiliano Rodríguez Mega writes about the New York Botanical Garden's endeavor to map all of the city's plants.

2018 Apr, Microsoft News

Like taking a whole scientific team with you on a walk: iNaturalist helps spawn a generation of citizen scientists

A story for Earth Day highlighting the contributions of users, the impact of the community, and the support of Microsoft AI for Earth.

2018 Mar, Earther

This New App Is Like Shazam for Your Nature Photos

Asher Elbein writes about the new gamified, kid-safe nature exploration app for iOS Seek by iNaturalist that uses solely computer vision.

2017 Dec, South China Morning Post

Conservation in Hong Kong: citizen scientists enlisted to record and safeguard city’s amazing biodiversity

A piece about Hong Kong's Biodiversity Strategy and Action Plan (BSAP) and its citizen science initiatives, including a BioBlitz that used iNaturalist.

2017 Dec, The New York Times Magazine

Letter of Recommendation: iNaturalist

Ferris Jabr writes a piece about iNat's computer vision ID feature and the importance of knowing the names of living things.

2017 Jul, The Atlantic

Finally: An App That Can Identify the Animal You Saw on Your Hike

Ed Yong describes iNaturalist's computer vision functionality released in June 2017 and puts it to the test with his own observations.

2017 Jul, Bay Nature

Identify Anything, Anywhere, Instantly (Well, Almost) With the Newest iNaturalist Release

In-depth coverage by Eric Simons of the evolution and initial launch of iNaturalist's computer vision/image recognition tool, with many quotes the iNaturalist team and broader iNaturalist community.

2017 Mar, Science Friday

Where to Find Wildflowers? Experts Weigh In

iNat co-founder Ken-ichi Ueda shares how to use iNaturalist to record and share the spring wildflower bloom alongside a panel of fellow wildlife-enthusiasts.

2016 Nov, DatingAdvice.com

Nature Lovers Come Together on iNaturalist.org to Document Their Environment and Share Their Passion

Not quite "press", but still kind of amusing, and not a bad write-up! Also brings to mind John Muir Laws's thoughts on love and nature.

2016 Jul, National Public Radio

The App That Aims To Gamify Biology Has Amateurs Discovering New Species

This story featuring iNaturalist by KERA in Texas got picked up by NPR for national broadcast on All Tech Considered.

2016 Jul, Mongabay

Citizen science leads to snail rediscovery in Vietnam

Describes a snail posted to iNat from Vietnam that hadn't been seen in over 100 years.

2015 Nov, Forbes

How Emerging Technologies Could Help Protect Biodiversity

Story about a recent paper on technology for conservation that covers iNaturalist

2015 Aug, National Geographic

People-Powered Data Visualization

Highlighting the power of big data generated by citizen science using iNaturalist, eBird, and other examples.

2014 Nov, MongaBay

Citizen scientist site hits one million observations of life on Earth

Nice story on iNat reaching 1,000,000 observations and launching improved maps.

2014 May, Science

The biodiversity of species and their rates of extinction, distribution, and protection

Pimm et al. assess the current rate of extinction compared with a hypothetical background rate. They cite iNaturalist as an important tool in helping scientists fill the gaps in our knowledge of where species currently persist.

2014 February, San Francisco Chronicle

Bioblitz volunteers help catalog species

SF Chronicle coverage of an iNat-powered bioblitz that we helped organize in collaboration with Nerds for Nature, Wild Oakland, and numerous other partners. Nerds for Nature has conducted numerous bioblitzes like this, and they're both tons of fun and a great model for using iNat to engage people with nature while collecting potentially useful data.

Want more? Noteworthy observations and other news can be found on Facebook and Twitter. The iNaturalist blog highlights news and and stories from the Observation of the Week. You can also search Google for even more news about iNaturalist.

Contact

iNaturalist is a joint initiative of the California Academy of Sciences and the National Geographic Society. iNaturalist has a physical office at:

California Academy of Sciences
55 Music Concourse Drive
Golden Gate Park
San Francisco, CA 94118
USA

You can email us at help@inaturalist.org.

Branding

If you're interested in using our brand in press coverage or to link to us, here are some files. If you're interested in higher resolution images or vector formats, please contact us.

PNG and White text PNG

PNG and White text PNG

PNG and White text PNG

Citing

iNaturalist. Available from https://www.inaturalist.org. Accessed [date].

https://www.inaturalist.org/pages/press Turorial https://www.youtube.com/watch?v=eS_9KpXgPdk iNaturalist in het Nieuws, In de Druk, Op de TV (25)
Posted on November 03, 2020 15:53 by ahospers ahospers | 0 comments | Leave a comment

24. Herkenning van Soorten met Model 5 (Voorjaar 2020) in iNaturalist (TensorFlow 2)

Op dit moment is Inaturalist al weer bezig met de zesde versie van het Computer Kijk (Computer Vision) model
waarbij in September 2020 18 miljoen fotos apart gezet zijn waarme zo'n 35.000 soorten wereld wijd herkend kunnen worden.
De aanpak is het zelfde als voor model 5 alleen met veel meer fotos omdat er nu veel meer soorten in iNaturalist
2000 fotos heeft. In het verleden werden wel meer dan 2000 fotos per soort gebruikt maar de extra rekenkracht weegt niet op tegen het succes.
In totaal zal het doorrekenen van het model 210 dagen kosten en in het voorjaar van 2021 klaar zijn.
Naast het doorrekenen van hetzelfde model met meer fotos en meer soorten wordt tgelijkertijd het huidige systeem vergeleken
met "TensorFlow 2, Xception vs Inception" wat waarschijnlijk ditzelfde model niet in 210 dagen maar in 60 dagen doorrekend.
Als dit nieuwe TensorFlow 2, Xception vs Inception goed werkt dan wordt het zelfs nog winter 2021 een nieuwe model opgeleverd.
Om dit door rekenen was een nieuwe hardware besteld maar door COVID is dit nog niet geinstalleerd.
In het huidige model zijn 25.000 van de 300.000 soorten die waargenomen zijn in iNaturalist.
https://www.inaturalist.org/blog/42626-we-passed-300-000-species-observed-on-inaturalist#comments

Hoe wordt nu bepaald of een soort opgenomen wordt in het model ?
Als van een soort 100 waarnemingen met foto waarvan er minsten 50 een Research Grade community ID heeft wordt opgenomen in de training. (actually, that’s really verifiable + would-be-verifiable-if-not-captive, In het model worden ook ontsnapte en gekweekte soorten opgenomen). Voor de training wordt dus niet alleen en uitsluitend Research Grade fotos gebruikt.

18. Beeldherkenning bij iNaturalist en Wildcamera's Citizen Science Snapshot

Waarneming.nl

  1. December 2017 Photos van Voor 2017
  2. December 2019 Photos van Voor 2018
  3. December 2020 Photos van Voor 2019

    Globaal waren de oude versies:

    1. May 2017 Model 1 2-20 photos per species
    2. Aug 2017 Model 2 40 photos per species
    3. Jan 2018 Model 3 40 photographers per species
    4. Feb 2019 Model 4
    5. Sep 2019 Model 5 <1000 photos per species/li>
    6. Mar 2020 Model 6

    Training


    Training Set 1


    In deze groep zitten geidentificeerde met
    1. De waarneming heeft een Taxon of een Genus, Familie toegewezen
    2. De waarneming heeft geen flags
    3. De waarneming heeft alle quality metrics gehaald behalve het toegestasnde wild / naturalized, dit zijn items die genoemd worden in de DQA, Quality Assesment

    Validation Set 1

    Met deze groep fotos wordt tijdens de training de voortgang van de training bekeken, een Toets of Examen dat het trainingmodel moet afleggen. De eisen aan deze validatieset zijn hetzelfde als van de Training Set 1 maar het is maar 5% van het aantal fotos.

    TestSet 1

    Met deze groep fotos wordt als de training is afgelopen gekeken of het model goed werkt. Het betreft uitsluitend
    fotos met een Community taxon, dus fotos die waarschijnlijk wel goed moeten zijn omdat meerdere personen een determinatie toegeveogd hebben aan de waarneming.
    Het bijzondere is dus dat aan de training ook minder zekere fotos mee mogen doen terwijl het testen tegen absoluut zekere waarnemingen gedaan wordt.
    Zie ook https://forum.inaturalist.org/t/identification-quality-on-inaturalist/7507
    Om te voorkomen dat er te veel soorten zijn waarvan er te weinig fotos zijn worden er niet te veel beperkingen aan de fotos gesteld. In de toekomst worden de eisen misschien strenger

    1. Fotos van Nieuwe gebruikers
    2. CID'd obs, waarnemingen met alleen een Communyt ID's
    3. vision-based ID
    4. Gebruik geen fotos van IDs by users with X maverick IDs

    Het computer is niet te downloaden maar misschien dat er later nog een API komt. Training your own with https://www.kaggle.com/c/inaturalist-challenge-at-fgvc-2017

    Croppen van fotos, Volgorde, Best Photo First

    Al hoewel het op iNaturalist neit vaak gezegd wordt is het Croppen van een foto een goede methode om betere resultaten te krijgen.
    Het model neemt ook geografische data nog niet echt mee. In het verleden werden enorme aantallen Californische soorten voorgesteld maar in de loop van de modellen is dat wel afgenomen.

    Best Photo First
    Het is naast croppen erg verstandig om je beste foto het eerste neer te zetten omdat het model alleen de eerste foto van de waarneming gebruikt om een voorstel voor de soort te doen.
    De locatie, nauwkeurigheid van een foto die je neemt buiten de iNat app om is meestasl minder nauwkeurig dan wanner je de interne app gebruikt van iNat. Ook kun je dan inzoomen met je vingers spread out, zodat je de crop functionaliteit niet hoeft te gebruiken. Het model gebruikt niet het tijd van het seizoen (eikels en kastanjes in de herfst, Trekvogels in voorjaar en herfst. Geen zomervogels als gierzwaluw in de winter en verspreidinggegevens van soorten.. ALpenroosjes worden niet tot de ALpen beperkt.

    In 2017 the amount of recognised species was 20.000 and now it is still.....20.000?

    https://www.inaturalist.org/pages/help#cv-taxa
    FWIW, there's also discussion and some additional charts at https://forum.inaturalist.org/t/psst-new-vision-model-released/10854/11
    https://forum.inaturalist.org/t/identification-quality-on-inaturalist/7507
    https://www.pyimagesearch.com/2017/03/20/imagenet-vggnet-resnet-inception-xception-keras/
    https://www.inaturalist.org/posts/31806-a-new-vision-model#activity_comment_5763380

    Neural Networks (specifically, VGG16) pre-trained on the ImageNet dataset with Python and the Keras deep learning library.

    The pre-trained networks inside of Keras are capable of recognizing 1,000 different object categories, similar to objects we encounter in our day-to-day lives with high accuracy.

    Back then, the pre-trained ImageNet models were separate from the core Keras library, requiring us to clone a free-standing GitHub repo and then manually copy the code into our projects.

    This solution worked well enough; however, since my original blog post was published, the pre-trained networks (VGG16, VGG19, ResNet50, Inception V3, and Xception) have been fully integrated into the Keras core (no need to clone down a separate repo anymore) — these implementations can be found inside the applications sub-module.

    Because of this, I’ve decided to create a new, updated tutorial that demonstrates how to utilize these state-of-the-art networks in your own classification projects.

    Specifically, we’ll create a special Python script that can load any of these networks using either a TensorFlow or Theano backend, and then classify your own custom input images.

    To learn more about classifying images with VGGNet, ResNet, Inception, and Xception, just keep reading.

    = = = = = = = = = = = = = = = = =
    : https://www.youtube.com/watch?v=xfbabznYFV0

    https://towardsdatascience.com/xception-from-scratch-using-tensorflow-even-better-than-inception-940fb231ced9

    Xception: Implementing from scratch using Tensorflow
    Even better than Inception
    Convolutional Neural Networks (CNN) have come a long way, from the LeNet-style, AlexNet, VGG models, which used simple stacks of convolutional layers for feature extraction and max-pooling layers for spatial sub-sampling, stacked one after the other, to Inception and ResNet networks which use skip connections and multiple convolutional and max-pooling blocks in each layer. Since its introduction, one of the best networks in computer vision has been the Inception network. The Inception model uses a stack of modules, each module containing a bunch of feature extractors, which allow them to learn richer representations with fewer parameters.
    Xception paper — https://arxiv.org/abs/1610.02357

    = = = = = = = = = = = = = = = = = = = = =
    https://towardsdatascience.com/review-xception-with-depthwise-separable-convolution-better-than-inception-v3-image-dc967dd42568
    Inthis story, Xception [1] by Google, stands for Extreme version of Inception, is reviewed. With a modified depthwise separable convolution, it is even better than Inception-v3 2 for both ImageNet ILSVRC and JFT datasets. Though it is a 2017 CVPR paper which was just published last year, it’s already had more than 300 citations when I was writing this story. (Sik-Ho Tsang @ Medium)

    = = = = = = = = = = = = = = = = = = = = = = = = = = = =
    https://laptrinhx.com/xception-from-scratch-using-tensorflow-even-better-than-inception-212761016/
    Convolutional Neural Networks (CNN) have come a long way, from the LeNet-style, AlexNet, VGG models, which used simple stacks of convolutional layers for feature extraction and max-pooling layers for spatial sub-sampling, stacked one after the other, to Inception and ResNet networks which use skip connections and multiple convolutional and max-pooling blocks in each layer. Since its introduction, one of the best networks in computer vision has been the Inception network. The Inception model uses a stack of modules, each module containing a bunch of feature extractors, which allow them to learn richer representations with fewer parameters.

    Xception paper — https://arxiv.org/abs/1610.02357
    Herkenning van Soorten met Model 5 (Voorjaar 2020) in iNaturalist (TensorFlow 2, (24))
    : https://www.youtube.com/watch?v=xfbabznYFV0

    Posted on November 03, 2020 20:50 by ahospers ahospers | 3 comments | Leave a comment