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6 results for “zooniverse”
Vortex Catalog from Jovian Vortex Hunter Zooniverse citizen science project
<p>This dataset contains the aggregated results from the <a href="https://www.zooniverse.org/projects/ramanakumars/jovian-vortex-hunter/" target="_blank" rel="noopener">Jovian Vortex Hunter citizen science project</a>, where citizen science volunteers labeled images from the JunoCam instrument and determined locations and sizes of vortices.</p> <p>The CSV file contains results from the first workflow and details the consensus from volunteers on different features in each image (given by the Zooniverse Subject ID). There are five categories to choose from:</p> <ol> <li>Vortex</li> <li>Turbulent (i.e. Folded Filamentary Regions: FFRs)</li> <li>Cloud bands</li> <li>Blurry (i.e., image issues)</li> <li>Featureless (there is no discernable feature in the image)</li> </ol> <p>The CSV file also contains an additional column defining the number of classifications of the subject.</p> <p>The JSON file contains the aggregated catalog of vortices and their properties from the second workflow. The format of the JSON file is as below:</p> <p>Each entry contains a dictionary of vortex properties, as shown below. The entry is determined by aggregating the vortex properties across multiple Zooniverse images which share the vortex, and building a consensus from multiple volunteer responses.</p> <pre><code>{ "subject_ids": [array of Zooniverse subject ID for each vortex], "perijove": the perijove corresponding to the image from this vortex was determined "color": the aggregated color of the vortex determined from volunteer responses, "lon": System III planetographic longitude [degree], "lat": planetographic latitude [degree], "x0", "y0": reference coordinate on the Zooniverse crop image for longitude/latitude, "x", "y": coordinate of the vortex center on the Zooniverse crop image "rx", "ry": radius of the vortex in pixel coordinates on the Zooniverse crop image "angle": angle in degree from horizontal of the orientation of the vortex in the Zooniverse crop image, "sigma": 1sigma error in the scale of the vortex, "angular_width": width of the vortex in degrees on the planet, "angular_height": height of the vortex in degrees on the planet, "physical_width", "physical width": width/height of the vortex in km, "extracts": [ array of dictionaries consisting of individual ellipses that make up the vortex consensus ] "colors": { "brown": consensus on the vortex being brown [0-1], "red": consensus on the vortex being red [0-1], "dark": consensus on the vortex being dark [0-1], "white": consensus on the vortex being white [0-1], "white-brown": consensus on the vortex being white and brown [0-1], "white-red": consensus on the vortex being white and red [0-1], "red-brown": consensus on the vortex being red and brown [0-1], } }</code></pre> <p>Each extract is a dictionary containing the following properties. An extract is a single aggregated ellipse on a single Zooniverse image.</p> <pre><code>{ "subject_id": Zooniverse subject ID, "perijove": the perijove when the JunoCam image was taken, "color": the color of the vortex with the highest vote fraction, "lon": System III longitude of the vortex [degree], "lat": planetographic latitude of the vortex [degree], "x0", "y0": reference coordinate on the Zooniverse crop image for longitude/latitude, "x", "y": coordinate of the vortex center on the Zooniverse crop image "rx", "ry": radius of the vortex in pixel coordinates on the Zooniverse crop image "angle": angle in degree from horizontal of the orientation of the vortex in the Zooniverse crop image, "probability": the 1sigma error in the scale of the vortex, "angular_width": width of the vortex in degrees on the planet, "angular_height": height of the vortex in degrees on the planet, "physical_width", "physical width": width/height of the vortex in km, }</code></pre>
Unprocessed data from the Jungle Weather Zooniverse citizen science project
<p>The <a href="https://www.zooniverse.org/projects/khufkens/jungle-weather">Jungle Weather project</a> aimed to transcribe weather observations recorded between 1949 and 1958 in the tropical rainforest of the Democratic Republic of the Congo. Long-term observations of tropical weather are rare. The Jungle Weather, as part of the COBECORE project, contains observations of three decades of data of weather in the central African tropical forest, and are therefore an extraordinary source of information to support our understanding of for example drought resilience of trees species.</p> <p><strong>Summary</strong></p> <p>Both input and output of the citizen science transcriptions are provided in this data set. This includes the original cut-outs as used in the Zoonivese project, and the output as generated by the Zooniverse data export routines. The data export routines provided CSV output with JSON subfields on the content of each classification made. In addition, we provided the exported subject list and the details of each workflow.</p> <p>In total the project output constitutes of four files:</p> <ul> <li>transcribe-climate-data-classifications.csv (annotations of the table cells)</li> <li>transcribe-meta-data-classifications.csv (annotations of table headers)</li> <li>jungle-weather-workflows.csv (description of the citsci workflow)</li> <li>jungle-weather-subjects.csv (list of all images transcribed, and their online location for validation / referencing)</li> </ul> <p>and roughly ~3GB in data volume.</p> <p><strong>Context</strong></p> <p>Our understanding of forest ecosystem responses to climate change relies on consistent long-term observations to provide baseline measurements. In the central Congo Basin established long-term observation programs are rare. In terms of meteorological observations, the central Congo Basin is currently represented by only a few rain gauges, limiting climate forecasts across the Congo Basin and the central African continent. This lack of long-term (historical) climatological data leaves the central Congo Basin spatially and temporally under-represented. However, old climate records could provide valuable information about previous growing conditions of the forest.</p> <p>Large amounts of ecological and climatological data, approximately five decades (~1910 – 1960), exists as unexplored heritage, stored in various Belgian federal archives and collections. As part of a larger project called Congo Basin eco-climatological data recovery and valorization (COBECORE, see) the Jungle Weather project will help transcribe historical climatological data as measured throughout the Congo Basin. These data will in part complement the completed <a href="https://www.zooniverse.org/projects/khufkens/jungle-rhythms">Jungle Rhythms Zooniverse project</a>, further valorizing these transcribed data.</p> <p><strong>Historical data</strong></p> <p>Within this project we will focus on data records as recorded throughout the tropical part of what is currently the Democratic Republic of the Congo (DRC). The area which we will cover is shown above in the map as an open polygon. The project will not cover the southern province of Katanga (red crosshatches) as this area transitions here from tropical to a humid subtropical climate.</p> <p>The historical data is archived and stored in the Belgian State Archives. The Belgian State Archive harbour almost all data regarding colonial affairs, ranging from communications about trade to the raw data as digitized within the context of the Jungle Weathers project. Row upon row of data is stored in the basement. Below you see a part of the INEAC (Institut National pour l’Etude Agronomique du Congo belge) archive, which holds all climatological records.</p> <p>These climatological records were noted rigorously on carbon copy paper. However, due to the hand written nature of the data (and the volume involved) automated processing is not possible. Although optical character recognition (OCR) works wonderfully on printed data the high variability in characters and the low contrast pencil markings contribute to the failure of current automated approaches. Similar to the <a href="https://www.oldweather.org/">Old Weather project</a> and in spirit of the Jungle Rhythms project, a keen eye is required to decipher the numbers written down on these sheets.</p> <p><strong>Pre-processing / digitization</strong></p> <p>The project provided citizen scientists with digital pictures of the original sheets. Scanning these climate data sheets was a laborious process. In total more than 70 000 records were digitized. Unlike the Old Weather project we did not require citizen scientists to outline valid sections of the sheet. This part of the processing has been automated. We refer to our<a href="https://doi.org/10.5281/zenodo.3378864"> Jungle Weather pre/post-processing repository </a>for more details and example code</p> <p>As such, once digitized and properly aligned the whole record was divided into an estimated 30 million cells and 70 000 header files. Below you find an example of a header file and a table cell. During the Jungle Weather project we selected a subset of ~300K table cells for transcription in efforts to validate further Machine Learning based, automated, transcriptions approaches. All data were transcribed by citizen scientists in the spring/summer of 2020.</p> <p><strong>Notes</strong></p> <p>The provided data is raw data, and expert knowledge is required for the correct interpretation of this data. Please contact the authors for the proper context if you are interested in using this data in your project.</p>
Image descriptions for 7471 of the DEArt images, obtained via the Zooniverse crowdsourcing platform
<p>This dataset contains crowdsourced image descriptions for 7471 images from the DEArt dataset. There are typically 4-5 descriptions per image, provided by volunteers of the Zooniverse crowdsourcing platform. The guidelines we used, together with some examples of possible captions, are also published in Zooniverse. We are in debt with all the Zooniverse volunteers and coordinators, and in particular with Samantha Blickhan, for making this possible. Some image descriptions were filtered out whenever they did not contain any textual data, nevertheless the dataset may still contain some descriptions that are not according to the guidelines we provided to the annotators. The dataset is a data export from Zooniverse in CSV format, in which the user information has been anonymized. The CSV also includes a field with the filename of the described image in the DEArt dataset.</p>
Data from: Time-lapse imagery and volunteer classifications from the Zooniverse Penguin Watch project
Automated time-lapse cameras can facilitate reliable and consistent monitoring of wild animal populations. In this report, data from 73,802 images taken by 15 different Penguin Watch cameras are presented, capturing the dynamics of penguin (Spheniscidae; Pygoscelis spp.) breeding colonies across the Antarctic Peninsula, South Shetland Islands and South Georgia (03/2012 to 01/2014). Citizen science provides a means by which large and otherwise intractable photographic data sets can be processed, and here we describe the methodology associated with the Zooniverse project Penguin Watch, and provide validation of the method. We present anonymised volunteer classifications for the 73,802 images, alongside the associated metadata (including date/time and temperature information). In addition to the benefits for ecological monitoring, such as easy detection of animal attendance patterns, this type of annotated time-lapse imagery can be employed as a training tool for machi ne learning algorithms to automate data extraction, and we encourage the use of this data set for computer vision development.
Data from: Time-lapse imagery and volunteer classifications from the Zooniverse Penguin Watch project
Open the record for dataset details and reuse information.
HUMANE: Zooniverse logs dataset
<p>The dataset contains 54,033,154 records of classifications made to 17 projects by 345,898 unique contributors to the Zooniverse project. </p>
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