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16 results for “crowd sourcing”
Supplementary Material for "Using Unstructured Crowd-sourced Data to Evaluate Urban Tolerance of Terrestrial Native Animal Species within a California Mega-City"
<p>This data repository is for the publication "Using Unstructured Crowd-sourced Data to Evaluate Urban Tolerance of Terrestrial Native Animal Species within a California Mega-City" and contains all R scripts and data files to reproduce results as well as all supplementary tables and figures.</p>
OpenChart-SE: A corpus of artificial Swedish electronic health records for imagined emergency care patients written by physicians in a crowd-sourcing project
<p>Electronic health records (EHRs) are a rich source of information for medical research and public health monitoring. Information systems based on EHR data could also assist in patient care and hospital management. However, much of the data in EHRs is in the form of unstructured text, which is difficult to process for analysis. Natural language processing (NLP), a form of artificial intelligence, has the potential to enable automatic extraction of information from EHRs and several NLP tools adapted to the style of clinical writing have been developed for English and other major languages. In contrast, the development of NLP tools for less widely spoken languages such as Swedish has lagged behind. A major bottleneck in the development of NLP tools is the restricted access to EHRs due to legitimate patient privacy concerns. To overcome this issue we have generated a citizen science platform for collecting artificial Swedish EHRs with the help of Swedish physicians and medical students. These artificial EHRs describe imagined but plausible emergency care patients in a style that closely resembles EHRs used in emergency departments in Sweden. In the pilot phase, we collected a first batch of 50 artificial EHRs, which has passed review by an experienced Swedish emergency care physician. We make this dataset publicly available as OpenChart-SE corpus (version 1) under an open-source license for the NLP research community. The project is now open for general participation and Swedish physicians and medical students are invited to submit EHRs on the project website (<a href="https://github.com/Aitslab/openchart-se">https://github.com/Aitslab/openchart-se</a>), where additional batches of quality-controlled EHRs will be released periodically. </p> <p> </p> <p><strong>Dataset content</strong></p> <p><em>OpenChart-SE, version 1 corpus (txt files and and dataset.csv)</em></p> <p>The OpenChart-SE corpus, version 1, contains 50 artificial EHRs (note that the numbering starts with 5 as 1-4 were test cases that were not suitable for publication). The EHRs are available in two formats, structured as a .csv file and as separate textfiles for annotation. Note that flaws in the data were not cleaned up so that it simulates what could be encountered when working with data from different EHR systems. All charts have been checked for medical validity by a resident in Emergency Medicine at a Swedish hospital before publication.</p> <p> </p> <p><em>Codebook.xlsx</em></p> <p>The codebook contain information about each variable used. It is in XLSForm-format, which can be re-used in several different applications for data collection.</p> <p> </p> <p><em>suppl_data_1_openchart-se_form.pdf</em></p> <p>OpenChart-SE mock emergency care EHR form.</p> <p> </p> <p><em>suppl_data_3_openchart-se_dataexploration.ipynb</em></p> <p>This jupyter notebook contains the code and results from the analysis of the OpenChart-SE corpus.</p> <p> </p> <p>More details about the project and information on the upcoming preprint accompanying the dataset can be found on the project website (<a href="https://github.com/Aitslab/openchart-se">https://github.com/Aitslab/openchart-se</a>).</p>
The Collection Management System Collection - Crowd-sourcing a list of digital repository options
<p><strong>The Collection Management System Collection - Crowd-sourcing a list of digital repository options</strong></p> <p>This dataset contains a list of digital repository options for collection management systems. It has been started and complited by Ashley Blewer.<br> The data set contains:</p> <ul> <li>a PDF capture of the blog describing motivation and background, columns of the spreadsheet and further resources; originally published at https://bits.ashleyblewer.com/blog/2017/08/09/collection-management-system-collection/</li> <li>The dataset / spreadsheet of The Collection Management System Collection, originally published at https://docs.google.com/spreadsheets/d/1cXOug3qM0pNNeD_wssiVEv9c0W1Y5I1VDTnSPTk7fb4/<br> The data was exported from the google spreadsheet on November 14th 2020 into the following formats: <ul> <li>PDF</li> <li>XLSX</li> <li>CSV</li> <li>TSV</li> </ul> </li> </ul> <p>The list contains basic information, administration considerations, interface considerations, technical considerations and social considerations for 70 different repository systems.</p>
Health Record Hiccups - 5526 real-world time series with change points labelled by crowd-sourced visual inspection
<p>5526 real-world time series with labels for the location of all abrupt changes in level, variability, trend, presence/absence of data points, and irregular outliers. The time series were produced from a range of electronic health record data extracts from a large UK hospital group. Values in each data field were aggregated by day/week/month, and numeric summary values calculated for each timepoint from the (often non-numeric) data by applying simple functions (e.g. number of values present, percentage of missing values, number of distinct values, median value). Labels were produced by visual inspection of time series plots from ~2000 volunteers, via the Health Record Hiccups project on the Zooniverse platform (https://www.zooniverse.org/projects/phuongquan/health-record-hiccups). Volunteers drew a vertical line on the image wherever they saw a change point (green line if they were certain, yellow line if they were unsure). Consensus labels per image were calculated using density based clustering with noise (R v3.6.3, dbscan v1.1-5), and converted back to a date.</p>
Crowds & Machines Next level: Meditteranean wheat classification labels from gamified crowd-sourcing
<p>Machine learning (and especially deep learning) algorithms need lots of training and validation datasets, which are often unavailable. Creating on-ground datasets is costly and time consuming. Within the European Space Agency funded project ‘Crowds & Machine – Next Level’ (by <a href="https://www.blackshore.eu">Blackshore B.V.</a>, <a href="https://www.52impact.nl">52impact B.V.</a> and <a href="https://hcss.nl">The Hague Centre for Strategic Studies</a>), we aimed to solve this issue by generating labelled data effectively using an innovative gamified crowdsourced-based method.</p> <p>The objective of the project ‘Crowds & Machines Next Level’ was to generate labelled data for the training and validation of machine learning algorithms to classify the crop wheat. We make those labelled datasets freely available as open data to organisations that use machine learning for their activities, mainly companies and knowledge institutes. As part of the project we developed example scripts (Jupyter notebooks) that enable organisations to use the crowdsourced generated data smoothly for their own machine learning systems. </p> <p>BlackShore has developed the online platform Cerberus to enable large scale generation of labelled datasets, which is deployed on twenty locations around the Mediterranean Sea to generate labelled datasets of wheat and other land cover classes (see table). Those different locations encompass a diversity of climate regions, harvest cultures and crop calendars, posing a challenge to the training of machine learning algorithms. Gamers click on hexagons plotted on top of very high resolution satellite imagery (captured during the harvest period in 2021), and by combining 3 different hexagon grids those clicks are converted into triangles. Each triangle has a number of clicks (by different users) per land cover category, which provides a measure of accuracy to the label.</p> <p>52impact developed example tutorials to use the data to train pixel-based (Random Forest) and segmentation-based (U-Net) machine learning models, using Sentinel-2 imagery (provided in the data folder), which can be forked here: <a href="https://bitbucket.org/52impact/crowds-machines">https://bitbucket.org/52impact/crowds-machines</a>.<br> </p> <table> <caption><strong>Overview of locations</strong></caption> <thead> <tr> <th scope="col">ID</th> <th scope="col">location_id</th> <th scope="col">Country</th> <th scope="col">Region</th> <th scope="col">Shape</th> <th scope="col">Harvest period</th> <th scope="col">VHR image date</th> <th scope="col">S-2 pre-harvest</th> <th scope="col">S-2 harvest</th> <th scope="col">S-2 post-harvest</th> </tr> </thead> <tbody> <tr> <td>01</td> <td>portugalAlentejo</td> <td>Portugal</td> <td>Alentejo</td> <td>01_Portugal_Alentejo_SELECTION</td> <td>10 Jul - 1 Aug</td> <td>07/07/2021</td> <td>14/05/2021</td> <td>13/07/2021</td> <td>22/08/2022</td> </tr> <tr> <td>02</td> <td>spainAndalusia</td> <td>Spain</td> <td>Andalusia</td> <td>02_Spain_Andalusia_SELECTION</td> <td>10 Jul - 1 Aug</td> <td>02/07/2021</td> <td>16/05/2021</td> <td>15/07/2021</td> <td>03/09/2021</td> </tr> <tr> <td>03</td> <td>spainAragon</td> <td>Spain</td> <td>Aragon</td> <td>03_Spain_Aragon_SELECTION</td> <td>10 Jul - 1 Aug</td> <td>26/10/2021</td> <td>20/05/2021</td> <td>19/07/2021</td> <td>05/09/2021</td> </tr> <tr> <td>04</td> <td>franceAude</td> <td>France</td> <td>Aude</td> <td>04_France_Aude_SELECTION</td> <td>1 Jul - 1 Oct</td> <td>22/09/2021</td> <td>12/05/2021</td> <td>10/08/2021</td> <td>18/11/2021</td> </tr> <tr> <td>05</td> <td>franceCamargue</td> <td>France</td> <td>Camargue</td> <td>05_France_Camargue_SELECTION</td> <td>1 Jul - 1 Oct</td> <td>07/10/2021</td> <td>12/05/2021</td> <td>10/08/2021</td> <td>18/11/2021</td> </tr> <tr> <td>06</td> <td>franceProvence</td> <td>France</td> <td>Provence</td> <td>06_France_Provence_SELECTION</td> <td>1 Jul - 1 Oct</td> <td>26/10/2021</td> <td>19/05/2021</td> <td>17/08/2021</td> <td>20/11/2021</td> </tr> <tr> <td>07_08</td> <td>italyMarche</td> <td>Italy</td> <td>Marche (East and West)</td> <td>07_08_Italy_Marche_SELECTION</td> <td>1 Jul - 1 Sept</td> <td>09/08/2021</td> <td>26/05/2021</td> <td>25/07/2021</td> <td>20/11/2021</td> </tr> <tr> <td>09</td> <td>italySardinia</td> <td>Italy</td> <td>Sardinia</td> <td>09_Italy_Sardinia_SELECTION</td> <td>1 Jul - 1 Sept</td> <td>31/08/2021</td> <td>26/05/2021</td> <td>22/07/2021</td> <td>10/10/2021</td> </tr> <tr> <td>10</td> <td>italySicily</td> <td>Italy</td> <td>Sicily</td> <td>10_Italy_Sicily_SELECTION</td> <td>1 Jul - 1 Sept</td> <td>19/09/2021</td> <td>22/05/2021</td> <td>26/07/2021</td> <td>10/10/2021</td> </tr> <tr> <td>11</td> <td>italyPugliaNorth</td> <td>Italy</td> <td>Puglia (North)</td> <td>11_Italy_PugliaNorth_SELECTION</td> <td>1 Jul - 1 Sept</td> <td>06/10/2021</td> <td>11/06/2021</td> <td>31/07/2021</td> <td>04/10/2021</td> </tr> <tr> <td>12</td> <td>italyPuglia</td> <td>Italy</td> <td>Puglia</td> <td>12_Italy_Puglia_SELECTION</td> <td>1 Jul - 1 Sept</td> <td>19/08/2021</td> <td>03/06/2021</td> <td>02/08/2021</td> <td>21/10/2021</td> </tr> <tr> <td>13</td> <td>greeceWest</td> <td>Greece</td> <td>West</td> <td>13_Greece_West_SELECTION</td> <td>1 Sept - 1 Nov</td> <td>02/09/2021</td> <td>27/07/2021</td> <td>05/10/2021</td> <td>14/12/2021</td> </tr> <tr> <td>14</td> <td>greeceThessaly</td> <td>Greece</td> <td>Thessaly</td> <td>14_Greece_Thessaly_SELECTION</td> <td>1 Sept - 1 Nov</td> <td>14/07/2021</td> <td>27/07/2021</td> <td>25/09/2021</td> <td>19/12/2021</td> </tr> <tr> <td>15</td> <td>greeceMacedoniaCentral</td> <td>Greece</td> <td>Macedonia (Central)</td> <td>15_Greece_MacedoniaCentral_SELECTION</td> <td>1 Jun - 1 Aug</td> <td>22/07/2021</td> <td>13/05/2021</td> <td>22/07/2021</td> <td>15/09/2021</td> </tr> <tr> <td>16</td> <td>greeceMacedoniaEast</td> <td>Greece</td> <td>Macedonia (East)</td> <td>16_Greece_MacedoniaEast_SELECTION</td> <td>1 Jun - 1 Aug</td> <td>05/08/2021</td> <td>25/05/2021</td> <td>29/07/2021</td> <td>27/10/2021</td> </tr> <tr> <td>17</td> <td>greeceRhodes</td> <td>Greece</td> <td>Rhodes</td> <td>17_Greece_Rhodes_SELECTION</td> <td>15 May - 1 Jul</td> <td>09/05/2021</td> <td>25/03/2021</td> <td>24/05/2021</td> <td>22/08/2021</td> </tr> <tr> <td>18</td> <td>cyprusLarnaca</td> <td>Cyprus</td> <td>Larnaca</td> <td>18_Cyprus_Larnaca_SELECTION</td> <td>15 May - 1 Jul</td> <td>05/06/2021</td> <td>19/03/2021</td> <td>07/06/2021</td> <td>21/08/2021</td> </tr> <tr> <td>19</td> <td>turkeyCyprus</td> <td>Cyprus (T)</td> <td>Farmagusta</td> <td>19_Turkey_Cyprus_SELECTION</td> <td>15 May - 1 Jul</td> <td>05/06/2021</td> <td>29/03/2021</td> <td>17/06/2021</td> <td>26/08/2021</td> </tr> <tr> <td>20</td> <td>egyptBehera</td> <td>Egypt</td> <td>Behera</td> <td>20_Egypt_Behera_SELECTION</td> <td>1 Apr - 1 Jul</td> <td>06/03/2021</td> <td>26/01/2021</td> <td>07/03/2021</td> <td>19/08/2021</td> </tr> </tbody> </table> <p>The following data is provided:</p> <ul> <li>Triangulated_data.zip: contains per region and per category a geopackage (gpkg) file containing triangular polygons with the number of clicks per polygon. The filename of the polygon files depends on the location and category. For example, a file that contains the triangles corresponding to Cattle in Alentejo, Portugal, is called: 01_Portugal_Alentejo_Cattle.gpkg</li> <li>Data.zip: all data necessary to run the Jupyter notebooks, i.e., location data, cropped Sentinel-2 satellite imagery (for training location IDs 01, 02, 12 and 15, and validation locations near IDs 02 and 15) and also the triangulated polygons.</li> <li>Models.zip: pre-trained random forest and U-Net models based on the data, which can be generated by the Jupyter notebooks.<br> </li> </ul>
Data from: Using molecular and crowd-sourcing methods to assess breeding ground diet of a migratory brood parasite of conservation concern
<p>Breeding ground food availability is critical to the survival and productivity of adult birds. The common cuckoo <i>Cuculus canorus</i> is a brood-parasitic Afro-Palearctic migrant bird exhibiting long-term (breeding) population declines in many European countries. Variation in population trend between regions and habitats suggests breeding ground drivers such as adult food supply. However, cuckoo diet has not been studied in detail since before the most significant population declines in Europe began in the mid-1980s. 20th century studies of cuckoo diet largely comprised field observations likely to carry bias towards larger prey taxa. Here we demonstrate the potential value of 1) using high-throughput DNA sequencing of invertebrate prey in faeces to determine cuckoo diet with minimal bias towards large prey taxa, and 2) using crowd-sourced digital photographs from across Britain to identify lepidopteran cuckoo prey taxa during recent years post-decline (2005-2016). DNA analysis found a high frequency of Lepidoptera, including moths of family Lasiocampidae, prominent within the past literature, but also grasshoppers (Orthoptera) and flies (Diptera) that may be overlooked by field observation methodologies. The range of larval lepidopteran prey identified from photographs largely agreed with those previously documented, with potential signs of reduced diversity, and identities of key adult prey taxa were supported by molecular results. Notably, many identified cuckoo prey taxa have shown severe declines due to agricultural intensification, suggesting this has driven spatial patterns of cuckoo loss. Landscape-scale, lowland rewilding interventions provide opportunities to understand the scale of reversal of previous agricultural intensification that may be necessary to restore prey populations sufficiently to permit recolonization by cuckoos.</p>
Crowd-sourced Fitbit datasets 03.12.2016-05.12.2016
<p>These datasets were generated by respondents to a distributed survey via Amazon Mechanical Turk between 03.12.2016-05.12.2016. Thirty eligible Fitbit users consented to the submission of personal tracker data, including minute-level output for physical activity, heart rate, and sleep monitoring. Individual reports can be parsed by export session ID (column A) or timestamp (column B). Variation between output represents use of different types of Fitbit trackers and individual tracking behaviors / preferences. <br /> </p> <p> </p> <p> </p> <p> </p>
Crowd-sourced SfM: Best practices for high resolution monitoring of coastal cliffs and bluffs
<p>Digital oblique photos of an approximately 2.0 km alongshore reach of seaward-facing coastal bluff faces on the Strait of Juan de Fuca, Washington State, were collected at least quarterly between 2016 and 2022. In total over 3900 photos were collected over 38 separate surveys. The photos were collected to support digital surface reconstructions using Structure-from-Motion (SfM) photogrammetry, and specifically to assess the quality of surfaces generated with photos collected using relatively simple techniques that could be accessible to community science programs. A secondary goal was to use the photos, and the digital surfaces generated with them, to evaluate patterns and rates of erosion on the bluff face. </p>
Post-op Crowd Sourcing Health Data Via Text-messaging
ClinicalTrials.gov study NCT03532256. IPD Sharing: NO. Countries: 1. Publications: 8.
Crowd-sourced SfM: Best practices for high resolution monitoring of coastal cliffs and bluffs
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Data from: Using molecular and crowd-sourcing methods to assess breeding ground diet of a migratory brood parasite of conservation concern
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Crowd and community sourcing to update authoritative LULC data in urban areas
<p>The French National Mapping Agency (Institut National de l'Information Géographique et Forestière - IGN) is responsible for producing and maintaining the spatial data sets for all of France. At the same time, they must satisfy the needs of different stakeholders who are responsible for decisions at multiple levels from local to national. IGN produces many different maps including detailed road networks and land cover/land use maps over time. The information contained in these maps is crucial for many of the decisions made about urban planning, resource management and landscape restoration as well as other environmental issues in France. Recently, IGN has started the process of creating a high-resolution land use land cover (LULC) maps, aimed at developing smart and accurate monitoring services of LULC over time. To help update and validate the French LULC database, citizens and interested stakeholders can contribute using the <a href="https://paysages.ign.fr/">Paysages</a> mobile and web applications. This approach presents an opportunity to evaluate the integration of citizens in the IGN process of updating and validating LULC data.</p> <p><strong>Dataset 1: Change detection validation 2019</strong></p> <p>This dataset contains web-based validations of changes detected by time series (2016 – 2019) analysis of Sentinel-2 satellite imagery. Validation was conducted using two high resolution orthophotos from respectively 2016 and 2019 as reference data. Two tools have been used: <a href="https://paysages.ign.fr/">Paysages</a> web application and <a href="https://laco-wiki.net/">LACO-Wiki</a>. Both tools used the same validation design: blind validation and the same options. For each detected change, contributors are asked to validate if there is a change and if it is the case then to choose a LU or LC class from a pre-defined list of classes.</p> <p>The dataset has the following characteristics:</p> <ul> <li>Time period of the change detection: 2016-2019.</li> <li>Time period of data collection: February 2019-December 2019</li> <li>Total number of contributors: 105</li> <li>Number of validated changes: 1048; each change was validated by between 1 to 6 contributors.</li> <li>Region of interest: Toulouse and surrounding areas</li> </ul> <p>Associated files: 1- Change validation locations.png, 1-Change validation 2019 – Attributes.csv, 1-Change validation 2019.csv, 1-Change validation 2019.geoJSON</p> <p>This dataset is licensed under a Creative Commons Attribution 4.0 International. It is attributed to the <a href="https://landsense.eu/">LandSense Citizen Observatory</a>, <a href="http://www.ign.fr/">IGN-France</a>, and <a href="https://www.geoville.com/">GeoVille</a>.</p> <p><strong>Dataset 2: Land use classification 2019</strong></p> <p>The aim of this data collection campaign was to improve the LU classification of authoritative LULC data (<a href="https://geoservices.ign.fr/documentation/diffusion/telechargement-donnees-libres.html#ocs-ge">OCS-GE 2016</a> ©IGN) for built-up area. Using the Paysages web platform, contributors are asked to choose a land use value among a list of pre-defined values for each location. </p> <p>The dataset has the following characteristics:</p> <ul> <li>Time period of data collection: August 2019</li> <li>Types of contributors: Surveyors from the production department of IGN</li> <li>Total number of contributors: 5</li> <li>Total number of observations: 2711</li> <li><a href="https://geoservices.ign.fr/ressources_documentaires/Espace_documentaire/BASES_VECTORIELLES/OCS_GE/DC_OCS_GE_1-1.pdf">Data specifications of the OCS-GE</a> ©IGN</li> <li>Region of interest: Toulouse and surrounding areas</li> </ul> <p>Associated files: 2- LU classification points.png, 2-LU classification 2019 – Attributes.csv, 2-LU classification 2019.csv, 2-LU classification 2019.geoJSON</p> <p>This dataset is licensed under a Creative Commons Attribution 4.0 International. It is attributed to the <a href="https://landsense.eu/">LandSense Citizen Observatory</a>, <a href="http://www.ign.fr/">IGN-France</a> and the <a href="https://iiasa.ac.at/">International Institute for Applied Systems Analysis</a>.</p> <p><strong>Dataset 3: In-situ validation 2018</strong></p> <p>The aim of this data collection campaign was to collect in-situ (ground-based) information, using the Paysages mobile application, to update authoritative LULC data. Contributors visit pre-determined locations, take photographs, of the point location and in the four cardinal directions away from the point and answer a few questions with respect with the task. Two tasks were defined: </p> <ul> <li>Classify the point by choosing a LU class between three classes: industrial (US2), commercial (US3) or residential (US5).</li> <li>Validate changes detected by the LandSense Change Detection Service: for each new detected change, the contributor was requested to validate the change and choose a LU and LC class from a pre-defined list of classes.</li> </ul> <p>The dataset has the following characteristics </p> <ul> <li>Time period of data collection: June 2018 – October 2018</li> <li>Types of contributors: students from the School of Agricultural and Life Sciences and citizens</li> <li>Total number of contributors: 26</li> <li>Total number of observations: 281</li> <li>Total number of photos: 421</li> <li>Region of interest: Toulouse and surrounding areas</li> </ul> <p>Associated files: 3- Insitu locations.png, 3- Insitu validation 2018 – Attributes.csv, 3- Insitu validation 2018.csv, 3- Insitu validation 2018.geoJSON</p> <p>This dataset is licensed under a Creative Commons Attribution 4.0 International. It is attributed to the <a href="https://landsense.eu/">LandSense Citizen Observatory</a>, <a href="http://www.ign.fr/">IGN-France</a>.</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no 689812.</p>
A sizable fraction of workers provides hasty, wrong answers in crowd-sourced tasks
<p>The data collected for the purposes of the work: "A sizable fraction of workers provides hasty, wrong answers in crowd-sourced tasks". The collected data represent the answers reported by crowdsourcing workers on Amazon Mechanical Turk (AMT). Three type of tasks, HITs where published on AMT, namely, Color, Majority and Count. Each file represents the deanonymized answers of 100 workers reporting their answers to the each task. The experiments where repeated after seven months and those data are included as well. The ending in the file name *batch2, reveals whether data refer to the first or second round of experimentation. In total the dataset includes 399 entries x 12 features and 200 entries x 16 features. </p>
Using a new video rating tool to crowd-source analysis of behavioural reaction to stimuli
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Crowd-sourced collected building attributes of the Colouring Dresden project (from 06 March 2023 to 01 October 2023)
<p><strong>Abstract</strong></p> <p>The building attribute data was collected by citizens as part of the citizen science project “Colouring Dresden”. Main goal was to collect information about buildings (like building age, usage, number of storeys, roof shape etc.) in an interactive online map. Different action formats (like dialogue series, presentations, mapathons, hackathons, and monthly meetings) were organised to bring citizens into action and discuss building-related topics. Focus was the sustainable construction of buildings and the question how good current buildings of the city are prepared for natural catastrophes like floods, heavy rain incidents or heat stress. The project period was from October 2022 to September 2023 and the mapping platform was launched in March 2023.</p> <p>Project page: <a href="https://colouring.dresden.ioer.info/">https://colouring.dresden.ioer.info/</a></p> <p>The project is part of the international research network “Colouring Cities Research Programme” (CCRP) and the first local project in Germany. Colouring Dresden is currently coordinating the European Hub of CCRP.</p> <p>The project was led by Leibniz Institute of Ecological Urban and Regional Development Dresden, Germany in cooperation with:</p> <p>- <strong>Sächsische Landesbibliothek - Staats- und Universitätsbibliothek Dresden (SLUB) - Regionalportal Saxorum</strong></p> <p>- <strong>Bund Deutscher Architektinnen und Architekten (BDA</strong>)</p> <p>- <strong>Zentrum für Baukultur Sachsen (ZfBK)</strong></p> <p>- <strong> Technische Sammlungen Dresden (TSD) / DLR_School_Lab TU Dresden</strong></p> <p>- <strong>Zentralbibliothek der Städtische Bibliotheken Dresden (SBD)</strong></p> <p> </p> <p><strong>Data description</strong></p> <p>The data contains several files:</p> <p>- <strong>building_atributes_geometry_20231001.gpkg</strong>: building geometries and building attributes in Geopackage file format</p> <p>- <strong>building_attributes.csv</strong>: building attributes</p> <p>- <strong>building_verification.csv</strong>: building verification data (crowd-sourced)</p> <p>- <strong>edit_history.csv</strong>: edit history of collected building_attributes</p> <p>- <strong>README.md</strong></p> <p> </p> <p><strong>Building geometries:</strong></p> <p><strong>Data source</strong>: 3D-Stadtmodell (3D city model) LoD2, Open Data Dresden</p> <p><strong>URL</strong>: <a href="https://daten.dresden.de/SONST/Datenbeschreibung_3D_Gebaeude_Dresden_OpenData.pdf">https://daten.dresden.de/SONST/Datenbeschreibung_3D_Gebaeude_Dresden_OpenData.pdf</a></p> <p><strong>Contact</strong>: opendata@dresden.de</p> <p><strong>Data license</strong>: Data license Germany – Attribution – Version 2.0 <a href="https://www.govdata.de/dl-de/by-2-0">https://www.govdata.de/dl-de/by-2-0</a>., Attribution: “Datenquelle: Landeshauptstadt Dresden, dl-de/by-2-0, opendata.dresden.de”</p> <p><strong>Spatial reference</strong>: WGS 84 / Web Mercator (EPSG: 3857)</p> <p><strong>Spatial extent</strong>: City of Dresden, Germany</p> <p><strong>Number of buildings</strong>: 135598</p> <p><strong>Completeness</strong>: Almost complete. Recently built buildings are not included.</p> <p><strong>Positional accuracy</strong>: Building footprints based on official cadastre data (ALKIS)</p> <p><strong>Up-to-dateness</strong>: Data is from 4<sup>th</sup> of May, 2023. Recently built buildings are not included.</p> <p> </p> <p> </p> <p><strong>Building attributes:</strong></p> <p><strong>(</strong>Including<strong> building verification </strong>and<strong> edit history)</strong></p> <p><strong>Data source:</strong> crowd-sourced collected building attributes from citizen science project “Colouring Dresden”, led by Leibniz Institute of Ecological Urban and Regional Development Dresden, Germany</p> <p><strong>Data collection:</strong> most attributes were collected by citizens using Citizen Science platform “Colouring Dresden”. Some attributes (address, centroid or identifier of external datasets like OpenStreetMap, Wikipedia or Wikidata) were linked automated.</p> <p><strong>URL:</strong> <a href="https://colouring.dresden.ioer.info/">https://colouring.dresden.ioer.info/</a></p> <p><strong>Contact:</strong> Dr Robert Hecht, <a href="https://www.ioer.de/institut/beschaeftigte/hecht">https://www.ioer.de/institut/beschaeftigte/hecht</a></p> <p><strong>Data license:</strong> Open Data Commons Open Database License (ODbL), Attribution: “Colouring Dresden contributors”, <a href="https://opendatacommons.org/licenses/odbl/">https://opendatacommons.org/licenses/odbl/</a></p> <p><strong>Spatial extent:</strong> City of Dresden, Germany</p> <p><strong>Number of buildings:</strong> 135598</p> <p><strong>Completeness: </strong></p> <p>Completeness of collected building attributes differs and depends on mapping activities of the Citizen Scientists from March to September in 2023. Completeness of automated linked attributes is 47.6% or more. Completeness of crowd-sourced attributes is up to 5.3%.</p> <p>100.0% building_id; ref_toid; location_latitude; location_longitude; revision_id; size_height_apex</p> <p>47.6% location_number; location_street; location_postcode; location_town</p> <p>5.3% building_attachment_form</p> <p>2.0% is_domestic</p> <p>1.1% size_storeys_core</p> <p>1.0% architectural_style; architectural_style_source; use_building_current</p> <p>0.6% size_storeys_attic; size_roof_shape</p> <p>0.3% use_building_origin</p> <p>And more attributes</p> <p><strong>Up-to-dateness:</strong> the data extract is from 1<sup>st</sup> of October, 2023 and includes the collecting period from March to September in 2023.</p>
A Crowd Sourcing Platform for Patients With Cancer
ClinicalTrials.gov study NCT03530826. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
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