Science Project

## Overview

Science Project is a dataset for object detection tasks - it contains Hand Sign annotations for 1,802 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Context

This comes from Society for Science's Abstract Search.

This project is also hosted on GitHub

Content

This contains the projects of every international science fair participant.

Data includes: - Project Title - Category - Abstract - Awards Won - Region - School

Acknowledgements

Because this comes from a web scrape, all of the data belongs to Science for Society.

Inspiration

I want someone to do a meta science fair project. Just the thought of doing a science fair project about science fair is incredibly cool.

The iratebirds database contains comprehensive visual aesthetic attractiveness, as seen by humans, data for bird taxonomic units (following the eBird/Clements integrated checklist v. 2019). The data were collected with the iratebirds.app -website citizen science project, where users rated the appearance of birds on a linear scale from 1-10. The rating were based on photographs of the birds available from the Macaulay Library database. Each rating score of a bird species or subspecies is based on several photographs of the same bird species. The application code is openly available on GitHub: https://github.com/luomus/iratebirds The application was spread during August 2020 – April 2021, globally, to as wide audiences as possible using social media, traditional media, collaborators and email-lists.

The iratebirds database is based on 408 207 ratings from 6 212 users. It consists of raw visual aesthetic attractiveness rating data as well as complementary data from an online survey that sourced demographic information from a subset of 2 785 users who scored the birds. The online survey gives information on these users’ birding skills, nature connectedness, profession, home country, age and gender. On top of these, the data scores for birds’ visual aesthetic attractiveness to humans have been modelled with hierarchical models to obtain overall average scores for the bird species and subspecies. More details on the data are found in this file’s section “Methodological information” as well as in the publication Haukka, A. et al. (2023), The iratebirds Citizen Science Project: a Dataset on Birds’ Visual Aesthetic Attractiveness to Humans, Scientific Data. The full database "iratebirds_raw_data_taxonomy_photoinfo_ratings_survey_251022.csv" includes all the data related to the photographs scored (e.g. place and location of the photograph, and its quality), the species and subspecies names (following the eBird/Clements integrated checklist v. 2019), the raw scores made by the users, details of the users (e.g. language used), and internal user ID, and for the users who took the online survey, also detailed information about their demography, e.g. home country and other information related to their knowledge of and connection to nature and birds. The modeled rating scores database "iratebirds_final_predictions_average_fullmodel_subsetmodel_151122.csv" includes visual aesthetic attractiveness of birds, as perceived by humans, calculated in three different ways. The most appropiate score can be chosen by the user according to the specific research needs, but in general we recommend using the scores from the full model (ii). The three different measures are i) raw visual aesthetic attractiveness for each bird species (or subspecies), ii) full model: visual aesthetic attractiveness corrected for language group of the scorer and the quality of the photo scored, iii) subset model: visual aesthetic attractiveness corrected as in ii) plus other user specific factors (related to bird and nature knowlegde and connections, home country, age. and gender). The file also gives information on how many photos were used for scoring each bird and how many users have scored the species. The latter subset model iii) represents only a subset of all the species. The data on visual aesthetic attractiveness are also available at the species and the sex within-species level, for the sexually dichromatic species, in the file "iratebirds_pred_ratings_species_and_sex_level_120123.csv".

All database files are given both as .csv- and .xlsx -files. The data and code to reproduce the analyses, figures and tables presented in Haukka et al. 2023 The iratebirds citizen science project: a dataset of birds’ visual aesthetic attractiveness to humans (Scientific Data doi: https://doi.org/10.1038/s41597-023-02169-0) are included in the 'iratebirds_raw_data_taxonomy_photoinfo_ratings_survey_251022.csv' and 'Haukka_et_al_Scientific_Data_modelling.R','Haukka_et_al_Scientific_Data_Figure.R' and 'Haukka_et_al_Scientific_Data_Tables.R' -files. Detailed information on dataprosessing and models can be found in the publication Haukka et al. 2023 The iratebirds Citizen Science Project: a Dataset on Birds’ Visual Aesthetic Attractiveness to Humans, Scientific Data doi: https://doi.org/10.1038/s41597-023-02169-0)

Science Fair 7r503m

## Overview

Science Fair 7r503m is a dataset for object detection tasks - it contains Science Fair 7r503m annotations for 24,991 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [MIT license](https://creativecommons.org/licenses/MIT).

A database of citizen science projects identified from Wikipedia's List of Citizen Science Projects, SciStarter and contributions from the ACTION consortium members. Updated to include

Overview of scales for knowledge and skills (Peter et al. 2021).

This data set contains records for 57 species covered by the project that have been verified by project staff using iRecord verification guidance. Records are from datasets made available via the iRecord verification process from the project website that links to the BRC database for verification purposes. Zero abundance records and associated environmental data are not included.

The Giotto Radio Science Experiment data set consists of four tables. Each table contains a measurement value listed as a function of time. The measurements are: closed-loop receiver carrier signal amplitude, closed-loop receiver carrier frequency residual, open-loop receiver carrier signal amplitude, and open-loop receiver carrier frequency.

This collection contains all resources generated as part of the Climate Adaptation Science (CAS) project (https://climateadaptation.usu.edu/). Resources include student course projects, research projects, internship work, assessments of educational outcomes, and other project materials. When creating resources, CAS participants will make all input data, models, code, results, instructions, and other digital artifacts developed for the project available for others to use, with the exception of sensitive human subjects data (expected level of reproducibility of at least Artifacts available). The steps at http://climateadaptation.usu.edu/project-data-models-code/ provide instructions for CAS participants to create a Hydroshare resource and request to add the resource to this collection. These steps were approved by the CAS Leadership Team on Nov. 15, 2018 and will be updated as needed. This collection is maintained by the CAS project coordinator.

This dataset is about book subjects. It has 3 rows and is filtered where the books is Science projects. It features 2 columns including publication dates.

Il n'y a pas de description pour ce jeu de données.

This dataset includes reduced data records from the HiRISE instrument on MRO.

Results of the one-way independent ANOVA test for whether participants had been in contact with other participants or not.

An infrastructure for integrating a UV-Vis-NIR spectrometer that can address broad planetary science goals will be developed. Earth and other solar system bodies have characteristic spectral signatures in this spectral range. Results will be used to explore the new concept for planetary science missions and evaluate system parameters.

A system that integrates a commercially available UV- Vis-NIR spectrometer will be developed for studying planetary objects. Trade studies will be performed to explore application limits in furthering planetary and earth science goals. The signal-to-noise is also sufficient to look for particular Mineralogical signatures in the UV.

Biodiversity citizen science projects are growing in number, size, and scope, and are gaining recognition as valuable data sources that build public engagement. Yet publication rates indicate that citizen science is still infrequently used as a primary tool for conservation research and the causes of this apparent disconnect have not been quantitatively evaluated. To uncover the barriers to the use of citizen science as a research tool, we surveyed professional biodiversity scientists (n = 423) and citizen science project managers (n = 125). We conducted three analyses using non-parametric recursive modeling (random forest), using questions that addressed: scientists' perceptions and preferences regarding citizen science, scientists' requirements for their own data, and the actual practices of citizen science projects. For all three analyses we identified the most important factors that influence the probability of publication using citizen science data. Four general barriers emerged: a narrow awareness among scientists of citizen science projects that match their needs; the fact that not all biodiversity science is well-suited for citizen science; inconsistency in data quality across citizen science projects; and bias among scientists for certain data sources (institutions and ages/education levels of data collectors). Notably, we find limited evidence to suggest a relationship between citizen science projects that satisfy scientists' biases and data quality or probability of publication. These results illuminate the need for greater visibility of citizen science practices with respect to the requirements of biodiversity science and show that addressing bias among scientists could improve application of citizen science in conservation.

This is the accompanying data for our paper "Lessons Learned from a Citizen Science Project for Natural Language Processing". Many Natural Language Processing (NLP) systems use annotated corpora for training and evaluation. However, labeled data is often costly to obtain and scaling annotation projects is difficult, which is why annotation tasks are often outsourced to paid crowdworkers. Citizen Science is an alternative to crowdsourcing that is relatively unexplored in the context of NLP. To investigate whether and how well Citizen Science can be applied in this setting, we conduct an exploratory study into engaging different groups of volunteers in Citizen Science for NLP by re-annotating parts of a pre-existing crowdsourced dataset. Our results show that this can yield high-quality annotations and at- tract motivated volunteers, but also requires considering factors such as scalability, participation over time, and legal and ethical issues. We summarize lessons learned in the form of guidelines and provide our code and data to aid future work on Citizen Science.

Explanation/Overview: Corresponding dataset for the analyses and results achieved in the CS Track project in the research line on participation analyses, which is also reported in the publication "Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects", a conference paper for the conference CollabTech 2022: Collaboration Technologies and Social Computing and published as part of the Lecture Notes in Computer Science book series (LNCS,volume 13632) here. The usernames have been anonymised. Purpose: The purpose of this dataset is to provide the basis to reproduce the results reported in the associated deliverable, and in the above-mentioned publication. As such, it does not represent raw data, but rather files that already include certain analysis steps (like calculated degrees or other SNA-related measures), ready for analysis, visualisation and interpretation with R. Relatedness: The data of the different projects was derived from the forums of 7 Zooniverse projects based on similar discussion board features. The projects are: 'Galaxy Zoo', 'Gravity Spy', 'Seabirdwatch', 'Snapshot Wisconsin', 'Wildwatch Kenya', 'Galaxy Nurseries', 'Penguin Watch'. Content: In this Zenodo entry, several files can be found. The structure is as follows (files and folders and descriptions). corresponding_calculations.html Quarto-notebook to view in browser corresponding_calculations.qmd Quarto-notebook to view in RStudio assets data annotations annotations.csv List of annotations made per day for each of the analysed projects comments comments.csv Total list of comments with several data fields (i.e., comment id, text, reply_user_id) rolechanges 478_rolechanges.csv List of roles per user to determine number of role changes 1104_rolechanges.csv ... ... totalnetworkdata Edges 478_edges.csv Network data (edge set) for the given projects (without time slices) 1104_edges.csv ... ... Nodes 478_nodes.csv Network data (node set) for the given projects (without time slices) 1104_nodes.csv ... ... trajectories Network data (edge and node sets) for the given projects and all time slices (Q1 2016 - Q4 2021) 478 Edges edges_4782016_q1.csv edges_4782016_q2.csv edges_4782016_q3.csv edges_4782016_q4.csv ... Nodes nodes_4782016_q1.csv nodes_4782016_q4.csv nodes_4782016_q3.csv nodes_4782016_q2.csv ... 1104 Edges ... Nodes ... ... scripts datavizfuncs.R script for the data visualisation functions, automatically executed from within corresponding_calculations.qmd import.R script for the import of data, automatically executed from within corresponding_calculations.qmd corresponding_calculations_files files for the html/qmd view in the browser/RStudio Grouping: The data is grouped according to given criteria (e.g., project_title or time). Accordingly, the respective files can be found in the data structure

Structure-formation energy pairs from February 2022 version of MP database.

This dataset includes derived Digital Terrain Models and their corresponding orthoimages from the HiRISE instrument on MRO.

Bottle Project

## Overview

Bottle Project is a dataset for object detection tasks - it contains Bottle annotations for 993 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).