dataesr/scientific-paragraphs-categorization huggingface dataset:
For sake of consistency, we use the term "participant" throughout this paper. Additionally, seminal work in the field has developed typologies to describe activities in a range of ways, focusing, for example, on different models for public participation in scientific research  38  , the levels of participation  35, 39  , or the orientation and aims of the activities  40. CONTRIBUTORY CITIZEN SCIENCE, as presented in some of these typologies, mainly involves participants in data collection activities and is a prevalent approach used in the fields of environment and ecology  41. Our focus within this Primer is on the application of citizen science approaches within the environmental and ecological sciences, where much of the recent growth in the field has taken place. Our main objective is to introduce contributory citizen science, as highlighted above, to scientists and practitioners who are new to the field.
Experimentation In this section, we provide an overview of different design and implementation stages of contributory citizen science projects in the field of ecology and environmental sciences, some of which will be described in more detail in subsequent sections. Various guidelines exist for designing and implementing citizen science projects covering aspects from data management to stakeholder engagement  [42] [43] [44] [45] [46] [47] [48] [49]. Examples of such guidelines are presented in TABLE  1. Here, we summarize some of the most relevant issues and considerations from these resources and offer additional insights. Note that each of the stages presented in this section are interconnected and one step does not necessarily need to end for the other to begin (FIG. 1 ). All stages and steps should be reviewed throughout the project cycle to actively incorporate changing factors, lessons learned, and participant feedback.
Acknowledging, as is sometimes the case in ecological and environmental studies, that stakeholders may not have a specific problem or research question identified, but instead have noted the need for baseline monitoring, can help to guide the work. Additionally, it is important to have an overview of similar projects and methods available that could be useful for the project. Within the rapidly growing citizen science field and literature, it is likely to identify similar problems and needs that were addressed through other initiatives. Some early considerations on the evaluation and sustainability of the project are also helpful in framing the overall project idea and establishing a sound basis for the upcoming stages  48.
Stage 2: Determining if citizen science is the right approach It is important to recognize that not all research projects can be addressed with the citizen science approach. This stage is about ensuring that citizen science is the right approach in addressing the problem or need and the research questions identified in the first stage  45. The goal is to understand if involving citizen science participants will help achieve the desired results, while at the same time benefitting them by addressing their needs or fostering new skills and expertise  50. If both those conditions can be met, then citizen science approaches are likely to be appropriate for the project  42. Deciding if citizen science is the right approach depends on various factors, such as the research questions, the spatial and temporal scale of the project, the type and amount of data needed to obtain results, the level of expertise required to collect the data, the training and coordination efforts needed or the target groups of the project, such as the participants, policymakers, funders, and scientific and practitioner communities  44.
Funding is another key consideration, and it is important to review the available resources and requirements for delivering on project objectives prior to starting  42. This includes considerations related to human resources including the skills that are needed in the team and tasks and responsibilities of the project staff. Equipment, travel, or training necessary for data collection should also be regarded. Examples of projects that are suitable for citizen science approaches are observations of the natural environment including wildlife and species, detecting change in land use and land cover through IN-SITU monitoring, where observations take place on site, classifying satellite images to identify deforestation, and monitoring water or air quality, or disease threats, among many others.
Stage 5: Managing the Data This stage highlights the processes and steps related to data management, which may apply to any research project. However, the aspects presented here reflect the peculiarities of citizen science projects. These steps are not necessarily taken in sequential order, some may take place simultaneously, while others occur more than once  43. Note that the steps related to (i) Planning, (ii) Collecting and (iii) Assuring are presented in this section, while (iv) Analyzing, (v) Describing and Preserving, and (vi) Integrating are discussed in subsequent sections.
Results In this section, we provide examples of QA and QC approaches including the training and testing of participants, community-based quality review, automatic control, and statistical tools in contributory citizen science. Furthermore, we provide examples of tools and methods to support data analysis in citizen science. Training and testing participants constitutes one approach to improve data quality  77  and is considered good practice  84, 85. Many projects offer online tools and training materials to improve the quality of participant observations such as species identification guides or videos  86, 87. Some projects also provide customized feedback to participants based on expert validation as a training to provide higher quality contributions  55, 88, 89. Additionally, training can occur through community consensus of data, where data are cross-checked and validated by other participants  90.
Dynamics of ecosystems Citizen science data may also be analyzed to study more complex dynamics of ecosystems, using statistical, computing, or experimental tools. For example, citizen science data about insect abundances can be used to test spatial variations in insect-flower affinities by taking the total number of taxa recorded in the collections as a proxy of flower visitor richness  118. Through the analysis of the occurrence of insects from different families on flowers of different morphologies, citizen science data can help assess the role of floral morphology in flower-feeding  119. Other citizen science data are constituted by material samples, such as fecal pellets, leaves, or soil samples  120. They can be analyzed following biological, chemical, or physical laboratory protocols such as using visual interpretation keys, DNA extraction, amplification, and sequencing  121, 122.
Projects collecting such samples may blend the above-mentioned citizen science data analyses with common analytical tools such as those used in bioinformatics. Data visualisation is also key to initial understanding and exploration of citizen science data  123. This can be done using open-source software such as R or QGIS, or their (proprietary) counterparts like Stata, SAS, and ArcGIS. These tools, as well as many others, such as the "Data Visualization Overlay" from the SPOTTERON citizen science platform, the CesiumJS opensource JavaScript library  124  or the CWDAT open-source tool  125  can be used to explore data, to formulate hypotheses and guide future research, to relate one's contributions to the whole data set of the project, or to identify data gaps  126. Data visualization also allow participants to become more active in different steps of data collection and analysis  127.
Biodiversity research Biodiversity-related research is prevalent amongst citizen science projects 3. Citizen science projects, at the same time, have become a significant factor in biodiversity research, historically  130, 131  , and contemporarily  22  , with a focus on species monitoring, contributing at least 50% of observations to international and global biodiversity databases, such as GBIF  132, 133  , although at times remaining unrecognized as a significant contribution to these efforts  134. One format for a biodiversity-focused, contributory citizen science activity which has gained popularity and traction in recent years is the so-called BIOBLITZ, such as the Great Southern BioBlitz, with more than 270 local and regional initiatives in the southern hemisphere contributing more than 190,000 biodiversity observations across the southern hemisphere in 2021.
Data Analysis Multiple classifications are obtained for each photo and video sequence, and a subset of the data is classified by subject experts. These two groups of classifications can be aggregated into consensus classifications with confidence levels  139. Timestamps from the camera trap observations allow profiling the daily and seasonal temporal patterns of various species. Analyses of the spatial data have improved understanding of the diversity and distribution of wild mammals, revealing temporal patterns in animal behavior, and may aid future analyses and estimations of population structure through occupancy modelling  140. The dataset is also designed to train machine learning algorithms for automated wildlife recognition  141.
Results and Insights Data from Spipoll have led to new scientific knowledge on the effects of urbanization on community composition  118, 119  , contrasted affinities of pollinators with different land use  147  , and the role of domestic gardens as favorable pollinator habitats  148. Datasets are available under open access licenses. Spipoll's online communication spaces for participants contribute largely to the constitution of a friendly learning community, help retain them in the long-term as well as help improve data quality  97. As such, Spipoll illustrates the key role of such online interaction and participant support tools to achieve contributory citizen science's multiple goals.
Results and Insights The project led to the first Participatory Guide of Marine Biodiversity in the Barcelona Metropolitan area  152  and helped to provide a baseline dataset on the unknown extent of marine biodiversity in urban coastal waters of Barcelona. The Barcelona City Council has included part of the results as a new marine component, a fish species layer, in its Atlas of Barcelona Biodiversity. The project provides a successful example of the quintuple helix innovation model applied in citizen science with participation of academia, industry, government, and civil society  153. The engagement of different actors, and of volunteering participants in particular, has facilitated a new societal perception of the marine biodiversity in the urban environment which may affect future policies of coastal management in the city.
Data Gathering and Quality Control WRDM was launched on iNaturalist because of its accessibility and usability features, allowing any user to export data, the stability and usability of the mobile application, the built-in support for community agreement as quality control for species identifications, and the robust existing user community. iNaturalist users contribute to the project by sharing observations that include photos for identifying the tree species, answers to custom questions, and GPS coordinates. These coordinates were applied to collect additional environmental data, such as climate data using the ClimateNA tool  177  and soil data from the SSURGO database 178.
Engagement Strategy and Ethics The Forest Health Watch program recruited participants through presentations and retained interest by hosting monthly research updates to add transparency, brainstorm project improvements, and discuss data and updates about the project's progress. Many participants were first time users on iNaturalist, joining the platform in interest of accelerating research about the dieback of western redcedar. Some participants were recruited directly through iNaturalist by commenting on relevant observations outside of the project. Overall, the recruitment and retention activities were time intensive, and the dedication needed to engage participants should not be underestimated.
It is also good practice to involve participants when designing the data management plan, such as in deciding how attribution is given. FIG. 3  illustrates the process for publishing outputs from a citizen science project. Fig. 3 : Best-practice steps for publishing outputs from a citizen science project The depicted steps fall within the Describing and Preserving Data step in Stage 5 of a citizen science project plan (see FIG. 1 ): gathering outputs, pre-publishing preparation, organizing and formatting outputs, and publishing in long-term repositories. Input from participants should be sought when creating and implementing the plan. Equally important are software and hardware outputs. Reproducible code best practices should be followed, as demonstrated by The Zooniverse and iNaturalist, which publish the complete source code of their servers and mobile applications on GitHub 189.
The process is similar for hardware designs  190. All outputs should be published in a dedicated data repository. For environmental and ecological sciences, this could be the GBIF or Dryad. Multidisciplinary repositories such as OSF or Zenodo are also appropriate. These repositories provide a Digital Object Identifier (DOI), which allows a data set to have a permanent citable reference. The Registry of Research Data Repositories provides a list of additional data repositories  187. While software and hardware design often occur on version control platforms such as GitLab or GitHub, copies should be deposited in these repositories. The Cos4Cloud project hosts various online services to aid citizen science data interoperability and reproducibility for uptake into the European Open Science Cloud.
Integrating Data integration is about combining data from various citizen science projects or combining citizen science data with other sources of data for addressing complex research questions and issues  43. For example, the Global Earth Challenge Marine Litter Data Integration Platform harmonizes and publishes citizen science data on beach and shoreline litter collected through three citizen science initiatives, the National Oceanic and Atmospheric Administration's (NOAA) Marine Debris Monitoring and Assessment Project's Accumulation Data, the European Environment Agency's Marine Litter Watch, and the Ocean Conservancy's International Coastal Cleanup (ICC) Trash Information and Data for Education and Solutions (TIDES) database  192. Picture Pile, a web-based and mobile citizen science application for ingesting imagery from satellites, orthophotos, unmanned aerial vehicles or geotagged photographs that can be rapidly classified by participants, combines Earth Observation and citizen science data for environmental monitoring  193.
Acknowledgements The work of C. B. H. was supported by a  Brandeis University Provost Research  grant. The work of D. F. was supported by the  European Union 's  Horizon 2020 Research And Innovation Programme EU-Citizen. Science project  (under grant agreement number  824580 ). The work of F. D. was supported by the  European Union 's  Horizon 2020 Research And Innovation Programme INTAROS, CAPARDUS and FRAMEwork projects  (under grant agreement numbers  727890 ,  869673  and  862731 ), and the  Danish Agency for Science and Higher Education  through the  UArctic Thematic Network on Collaborative Resource Management. The work of G. H. was supported by the  European Union 's  Horizon 2020 research and innovation programme EU-Citizen. Science and FRAMEwork projects  (under grant agreement numbers  824580  and  862731 ).
The work of J. M. H. was supported by the  AFRI Postdoctoral Fellowship  grant  2020-67034-31766  from the  USDA National Institute of Food and Agriculture. The work of J. P. was supported by the  European Union 's  H2020 Research And Innovation Programme Cos4Cloud project  (under grant agreement number  863463 ); J. P. also acknowledges the ' Severo Ochoa Centre of Excellence ' accreditation ( CEX2019-000928-S ). The work of M. H. was supported by the  European Union 's  ERC Advanced Grant project 'European Citizen Science: Analysis and Visualisation'  (under grant agreement number  694767 ); and the  European Union 's  Horizon 2020 Research And Innovation Programme EU-Citizen. Science and TIME4CS projects  (under grant agreement numbers  824580  and  101006201 ). The work of M. T. was supported by the  European Union 's  Horizon 2020 Research And Innovation Programme MINKE project  (under grant agreement number  101008724 ).
ApplicationThe application of citizen science as a practice in the natural sciences dates to the beginnings of scientific inquiry itself and today spreads across the globe, leading, amongst others, to some of the longest-running time-series datasets in phenology, ornithology, and meteorology 128, 129. In this section, we illustrate the diversity of applications of contributory citizen science with examples from biodiversity research, earth observation and geography, and climate change research, where citizen science has an established focus and can be considered a well-tested method3. We complement these examples with applications in the environmental domain but outside the box of contributory projects from the Global South, highlighting the potential and intricacies of COMMUNITY-LED CITIZEN SCIENCE (BOX 1), as well as citizen science at the interface of education and environmental activism (BOX 2).
The more the e_value deviates from 0 the more likely the chromosome is to present an anomaly, as showed in Supplementary Fig. 2. The calculation of the e_value is provided as an additional module of the NIPT analysis tool NiPTUNE. Decision tree approach to identify the minimal thresholds of ff and sd needed to achieve reliable NIPT Aneuploid samples from the two cohorts and synthetic aneuploid samples generated with GenomeMixer were used to calculate the minimal thresholds for sd, ff and e_value to obtain a reliable NIPT. We used a decision tree approach using the R package caret, specifically the function rpart. Briefly, we used WisecondorX to calculate the Z-score of synthetic samples and Seqff and Defrag_a to assess their ff. The sd and the e_values were calculated using the modules despina.
Characterization of tissue architecture promises to deliver insights into development, cell communication and disease. In silico spatial domain retrieval methods have been developed for spatial transcriptomics (ST) data assuming transcriptional similarity of neighboring barcodes. However, domain retrieval approaches with this assumption cannot work in complex tissues composed of multiple cell types. This task becomes especially challenging in cellular resolution ST methods. We developed Vesalius to decipher tissue anatomy from ST data by applying image processing technology. Vesalius uniquely detected territories composed of multiple cell types and successfully recovered tissue structures in high-resolution ST data including in mouse brain, embryo, liver, and colon. Utilizing this tissue architecture, Vesalius identified tissue morphology specific gene expression and regional specific gene expression changes for astrocytes, interneuron, oligodendrocytes, and entorhinal cells in the mouse brain.
Image-based ST approaches including seqFISH  3, 4  or merFISH  5  provide sub-cellular resolution ST. Image-based techniques rely on the pre-selection of target mRNA species and -due to the challenge of distinguishing overlapping fluorescent signals -are limited to a smaller number of genes sampled at a time  6. In contrast to image-based ST, sequencing based-ST techniques such as 10X Visium  7  , Seq-Scope  8  or Slide-seq  9, 10  provide a non-biased and genome wide quantification of mRNA species. Technological advances have made it possible to obtain sequencing based-ST data on cellular and even sub-cellular resolutions. Tissue territory detection in high-resolution ST data will strengthen our understanding of tissue architecture and its associated marker genes providing further opportunities to study transcriptomic changes due to local environment and tissue morphology.
A number of tools including Seurat  11  , BayesSpace  12  and Giotto  13  have been developed to understand tissue architecture from ST data. Seurat leverages refence single cell data sets to map barcode identities to their respective location in ST data. While this approach demonstrates the cellular heterogeneity of tissues, the task of recovering and extracting anatomical regions is still challenging due to their cellular complexity. On the other hand, BayesSpace and Giotto provide distinct models both utilizing Hidden Markov Random Fields. Their respective methods attempt to cluster barcodes together under the assumption that neighboring barcodes are likely part of the same cluster if transcriptionally similar. These methods have performed well in low resolution Visium 10X data  12, 13.
However, this assumption will not necessarily hold in complex tissue containing multiple cell types as neighboring barcodes are just as likely to represent a different cell type in high resolution ST data. The recovery of anatomical structures containing multiple cell types becomes extremely arduous and generally has relied on manual isolation. One solution to this challenge is to link anatomical territories from companion hematoxylin and eosin (H&E) staining images to their spatial transcriptomic assay  [14] [15] [16] [17] [18]. However, techniques such as Slide-Seq  9, 10  do not provide these companion images. Furthermore, the segmentation of anatomical territories in images still remains challenging without manual annotation  [19] [20] [21]. To address these limitations, we developed Vesalius -an R package -designed to perform in silico anatomization and isolation of tissue territories without the use of H&E companion images.
Vesalius converts the transcriptome into an RGB color code that is then embedded into an image. By leveraging a variety of image analysis techniques, Vesalius is able to recover complex tissue territories in the mouse brain, mouse embryo, diseased liver and colon. Comprehensive tests using simulated data indicate that Vesalius can identify tissue territories even when the tissue is composed of multiple cell types while other competitors developed for low-resolution ST data often identify numerous unwanted patches. Identified territories are used to discover tissue morphology-specific gene expression as well as changes in the transcriptome of astrocytes, interneuron, oligodendrocytes, and entorhinal cells in the mouse brain depending on the tissue territory.
Vesalius embeds the transcriptome in the RGB color space The core concept of the Vesalius algorithm is to represent the transcriptome of a barcode as a color in the RGB color space and build images upon which image analysis techniques can be applied (Fig. 1a-b ). To embed the transcriptome into images, Vesalius first pre-processes sequencing based spatial transcriptomic data by log normalizing and scaling counts values, extracting highly variable features, and reduces dimensionality via principal component analysis (PCA). Next, Vesalius uses Uniform Manifold Approximation and Projection (UMAP) to project PCs into three dimensions and embed the latent space into the RGB color space (See Methods -Fig. S1a ). Alternatively, Vesalius can also embed PC loading values into the RGB color space for a more targeted view of data variance (See methods -Fig.