dataesr/scientific-paragraphs-categorization huggingface dataset:
Studies that create their own databases tend to use smaller numbers of patients. Sometimes it is not easy to have the opportunity and the permission of the ethics committee to work with a large number of patients. The most widely used databases of renal tumors are KiTS2019, ImageNet, KiTS21 and TCIA. As mentioned above, more than half of the databases are private. Also, there are limited number of datasets with the proper labels to train an effective model. The increase of public databases and open data in general will benefit future research, although this is progressing slowly. As for the number of patients analysed in each study, there are certain studies (in this case 5) that do not mention in their papers the number of patients utilized, but simply mention the number of images analysed.
Conclusions In this work, we have carried out a systematic review of the literature on renal tumor detection with deep learning in images. It is well known that the application of artificial intelligence, specifically deep learning, in the field of health is increasing by leaps and bounds. However, as our review shows, the application in renal tumors is limited and insufficiently validated. The number of papers analysed is not very large (18 in total) because the first paper where deep learning techniques are applied to renal tumors was in 2019. This fact rather than a limitation is a challenge and an opportunity for much more research in the area, not only in this field of renal tumors but also in others. As has been observed, a large number of studies use private databases, created by the hospitals themselves or departments based on their own patients.
Collecting and publicly sharing datasets that are related to renal tumors is considered of utmost importance in order to increase research in the field. There are currently very few public image-based data sets of renal tumors. During the research, these databases should be somehow made available for researchers and for computing experts, which means that they fulfill the ethical and legal issues related to data sharing. This topic needs to be addressed with documents to protect knowledge. We face serious problems with these issues and in the future, it may be more problematic. At the end of the investigations of the group, will be mandatory to publish the database or repositories in an open access format to reach reproducibility and accessibility of the data to the scientific community. We will use dedicated repositories for sharing, we will sign ethical documents to protect our data.
Another example is the Yellow River, a major drinking water source in China and the second-longest in Asia, that has been suffering from extensive contamination and is now on the verge of becoming unfit for even industrial or agricultural use  (Dwivedi et al. , 2022a). Existing CZOs do not operate at these scales, so it is important to consider the representativeness of individual CZ sites for larger systems and consider opportunities for cross-site synthesis to grasp the impact of spatial variability on downstream conditions. Further, while the scale and complexity of these water quality impacts is daunting, large investments have already been made in developing these CZO sites, which constitute collecting phenomenally diverse and distributed watershed datasets, including many associated with autonomous sensing systems.
It is worthwhile to acknowledge that comparable measurements exist at CZOs at national and international scales. Together, these datasets share many common attributes, but differ by important aspects such as geophysical attributes, climatic conditions, plant functional types, biodiversity, inherent complexity (e. g. , natural/built environment), disturbance types (e. g. , fire, heat wave, flooding, mining) and time since disturbance (e. g. , logging, insect infestation). An international network of watersheds and CZOs can serve as a vehicle for knowledge exchange, integration, and scientific discovery. Strengths of such a network include its ability to detect emergent scale properties of watershed and CZ function at local to regional and global scales, and provide an in-depth understanding of the spatially heterogeneous impact of disturbances on watershed function.
g. , DOE Watersheds, CZCN, OZCAR, TERENO) that were designed with specific strategic goals; however, there are many sites (including those not listed on this map) that are well established (e. g. , long-term, interdisciplinary, indigenous community-led) but not formally part of a funded network and therefore lacking in aspects that promote data sharing/cross-site comparisons. There is room to advocate for both individual and larger-scale CZO network development and network-of-networks. For example, there is significant underrepresentation of the intertropical belt among the established CZOs (Fig. 1 ), although this latitudinal range harbors over half of the world population, two-thirds of the terrestrial plant biomass  (Chapin et al. , 2002) , and may face some of the most dramatic impacts of ongoing global changes  (Mamalakis et al.
In the same section, we also highlight community debates regarding the need to integrate human and social perspectives in CZ science (Section 5. 2). We identify-three areas of greatest need in order to achieve higher rates of data sharing and reuse under a network-of-networks framework: open and standardized metadata guidelines, data harmonization, and a new class of CZ information scientists (Section 6. 1). We suggest specific guidance for addressing these needs and describe the principles for enhancing cross-site and cross-network collaborations in the CZ (Section 6. 2). The proposed network-of-networks model is expected to promote synthesis/integration activities across CZ networks, develop transferable tools, data and workflows, train next generation of CZ scientists, open new sources of funding, build personal connections and human-tohuman interaction, as well as engage CZ site managers and relevant stakeholders.
Further, there have been calls for an integrated earth observatory (e. g. ,  Kulmala, 2018)  as the best means to address global problems such as climate change. However, it is important to recognize that such an effort requires detailed ground data including, but not limited to, soil properties, nutrient stocks and transformations, carbon pools and transformations, and microbial functioning, to constrain processes such as greenhouse gas fluxes, nutrient transformations, and hydrological processes  (Arora et al. , 2016; Vicca et al. , 2018; Jansson and Hofmockel, 2020)  as well as assessing the response to disturbance  (Graham et al. , 2021; Grant et al. , 2019). Providing such data will require a global coordinated effort and is likely to require working across networks of CZOs. Integrating and collaborating across these observatories and networks also becomes increasingly indispensable for understanding how CZ might behave under conditions substantially different from the present ones.
For example, in a microbial context, an outstanding science question is to understand to what degree the response of a microbial community to a disturbance is ecosystem-limited, and can the response be generalized as a functional trait that could then be used to make predictions and management decisions regarding future events. If microbial datasets are collected in standardized ways across the global CZ, these data types can be used to reveal generalizable patterns, rules, concepts, and theories tied to a broad range of microbial properties related to ecology (e. g. , large-scale diversity gradients), evolution (e. g. , processes governing strain variation), and function (e. g. , microbial food webs structured by metabolite exchange). Taking a coordinated approach spanning CZOs will dramatically accelerate the pursuit of generalizable or transferable knowledge, which is essential to develop predictive models (e.
g. , CHOSEN, WHONDRS)  (Zhang et al. , 2021; Stegen and Goldman, 2018). A recent study by  Ward et al. (2022)  further demonstrates the power of prodding such extensive datasets using machine learning approaches. Focused on river corridor science, their study uncovered relationships that would not have been possible through traditional, deductive approaches to science. However, we must recognize that cross-site initiatives and data gathering efforts that cover a large spatial domain, but do not provide a holistic, interdisciplinary view of CZ processes may not be enough to advance CZ science. For instance, the NSF has taken a step to address this challenge by reorganizing their CZO program to develop 10 new Critical Zone Collaborative Networks (CZCNs) that all but one focus on science/hypothesis driven research across multiple, national CZ sites.
Other examples of outstanding CZ-related challenges Although not explicitly discussed during the 2021 CUAHSI cyberseminar series (or described in Table  1 ), there are opportunities for improved understanding of CZ processes in other landscapes undergoing rapid and drastic change, for example tropical forested landscapes, urban areas (especially those along coastlines), intermittent river and variably inundated settings. Below, we take the example of urban landscapes to illustrate how we could benefit from a concerted effort to generate and test hypotheses through synthesizing and analyzing information across a variety of sites and networks. As shown in Fig. 1 , urban landscapes can be classified as 'Developed -High' land cover according to National Land Cover Database (Dewitz and U. S. Geological Survey, 2021), which includes 4 sites (Eel River CZO, Central Arizona -Phoenix LTER, Baltimore Ecosystem Study, and Plum Island Ecosystem LTER) and as 'Developed -Low' that includes 17 additional sites.
shinyapps. io/macrosheds/). The latter also includes tools for training models with hosted datasets. Another example is the Datastream portal hosted by the Gordon Foundation (htt ps://gordonfoundation. ca/initiatives/datastream/), which makes it easy to share and access water quality data. A more extensive listing of existing regional and international data portals is provided in Table  2. We recognize that the highly distributed nature of CZ data repositoriesas is evident from Table  2  -can require substantial efforts in ensuring data access and discovery. In section 6. 1. 1. , we make recommendations on how to make these data easily and automatically accessible and discoverable through a global catalog of CZ data stores across networks. However, efforts to institute a data collection or storage standard will likely have the effect of stifling innovation in CZ science, while also becoming unwieldy.
Rather than standardize data collection or storage, we argue for the development of shared metadata template(s). These must be developed as a community effort and would enable more efficient but flexible data storage, collection, and discovery (see section 6. 1. 1 for further details). Online infrastructures integrating models and data analyses are emerging as well, from large platforms such as the IDEAS-Watersheds Software Ecosystem, https://ideas-productivity. org/ideas-watersheds/) which comprises workflow tools, interface libraries and a variety of codes for reactive transport, hydrological or land surface modeling, all the way to specific toolboxes such as BridgET (https://github. com/KIT-HYD/bridget) for comparing and scaling evapotranspiration estimates. Beyond existing portals, synthesis activities have been focused on curating specific CZ data from a variety of sites.
Successful examples of such activities include the Soil Water Infiltration Global (SWIG) database, which includes more than 5000 infiltration curves covering all continents with an acute focus on developing, evaluating and validating infiltration processes across a range of models  (Rahmati et al. , 2018). In the United States, the CHOSEN (Comprehensive Hydrologic Observatory Sensor Network) is a database of streamflow, soil moisture, and other hydroclimatic and hydrologic variables, a comparative analysis of which highlighted complex patterns in hydrological extremes across different US regions thereby advocating for long-term observatories  (Zhang et al. , 2021). Other efforts focused on global datasets of high-dimensional, high-resolution microbial properties and processes across diverse CZ environments are also emerging.
State-of-the-art tools and techniques for reducing complexity Beyond data integration portals and frameworks, conducting synthesis crucially relies on translating complex CZ information into a compelling scientific narrative. One approach to reducing complexity in studied systems -even more so across a large array of observation sites-is to use dimensionless numbers. This is because dimensionless numbers have the potential to collapse the scatter in data, highlight scale invariance, express the competition between processes, and allow for comparing datasets, model outputs, and/or locations with different characteristic ranges. Classical examples include the Reynolds number in fluid dynamics  (Abraham, 1970) , the combination of the dryness and evaporative indices (both dimensionless) in the Budyko curve in hydroclimatology (e.
The need for global metadata for CZ science Ensuring cross-site cross-network CZ collaboration will require a number of technical innovations that have already begun, but require significant additional developments to help bring about open and networked science. Specifically, workflows are needed that enable data to be discovered, accessed, and harmonized. Data discovery refers to the ability to locate and understand data sets that exist, while access refers to the ability to obtain these datasets. In Section 4. 1, we enumerated many CZ networks that made data available, and each one enables discovery across its own network. More and more of these networks structure their workflow and data life cycle requirements to make used data FAIR (findable, accessible, interoperable, reusable). The existing data portals work quite well when seeking to access data within one network.
However, when integrating data across multiple networks, the existing approach is quite cumbersome, as one must learn the terminology, interface, and other aspects of every individual network. Moreover, data integration is particularly challenging in the highly multidisciplinary field of CZ science, due to the inherent diversity of the data that may be combined in a single portal, in both type  (climate, ecology, geochemistry, genomics, etc. )  and associated spatio-temporal scales (see, for example Table  2 ). Given this background, it is obvious that some kind of global catalog of networks is needed, and for this to be developed, some agreement on a harmonized metadata template is needed. This calls for having extensive metadata associated with the databases, ideally built in from the start in a robust data management plan, to avoid unforeseen discrepancies as the database grows.
In practice, this implies making metadata generation and upload easy and user-friendly for data uploaders, and FAIR and open for the targeted users. Though not explicitly included in FAIR principles, the use of digital object identifiers (DOIs) for datasets and published algorithms has been an oft-mentioned need as well  (CUAHSI, 2021a (CUAHSI, , 2021b)). DOIs allow resources such as data or code to be unambiguously identified and cited, enabling much more transparency in research within and across networks.
The need for semantics to power data harmonization Data discovery is simply a first step of working across networks. A much more difficult issue, and one that arguably has not been addressed as well as discovery, is that of harmonization. Harmonization refers to taking data sets from a number of different sources and having them conform to a particular schema for a particular purpose. Barriers to CZ data harmonization were discussed in detail in the second CUAHSI cyberseminar series  (CUAHSI, 2021b). Todd-Brown et al. (2022)  reported on an interview study with eight research group leaders who had constructed harmonized soil carbon datasets from pre-existing data. They found that while discovery tools were quite useful and available, there were virtually none dedicated to data harmonization.
Harmonization was usually accomplished in a manual, ad-hoc manner, which proved to be quite labor intensive, error prone, and constituted no data provenance (detailed explanation of how the harmonized data was sourced from primary measurements). The data model that each group settled on for harmonized data tended to be dependent on the question they asked. This suggests that it is unrealistic to have a single data template or schema even for soil carbon work, let alone for CZ research. These results suggest the need for more research into data harmonization in CZ science. A generalized approach to data harmonization proved to be quite useful as shown in the SoDaH project  (Wieder et al. 2021) , where raw soil carbon data were annotated with a generalized metadata template. Such a generalized template allowed data to be mapped from whatever format they were collected into, to a format useful for a specific aggregated analysis, avoiding the need for a universal data storage schema.
However, the specific templates employed in SoDaH were focused on soil carbon. Different questions and different source data may require a revisit to the templates used, should an approach similar to SoDaH be implemented more broadly. These lessons learned in the soils field are likely to apply to the wider field of CZ science. If international CZ networks become linked together, we will need to develop ways to harmonize data across multiple schemas. For instance, the Theia/OZCAR network has opted to use a specific database schema for data across its network  (Braud et al. , 2020). It is quite possible that should a researcher wish to integrate OZCAR data with any other network's data, they would encounter harmonization difficulties similar to those encountered by Todd-Brown's (2022) interviewees.
As a solution to this 'tower of babel' problem when working across disciplines, researchers have advocated for the use of formal ontologies (e. g. ,  Sieber et al. , 2011). Formal ontologies encode the domain knowledge of a community into a set of logical statements using classes, properties, and instances  (Uschold and Gruninger, 1996). Importantly, formal ontologies are machine-processable and can be used for discovery and harmonization. Sieber et al. (2011)  show how formal ontologies can be used for data discovery (and harmonization to some extent) across multiple databases of Chinese history. Wellen and Sieber (2013)  question the use of formal ontologies of earth features due to significant natural language differences of those features. However, in a more restrictive context of sharing and harmonizing data across CZ networks, formal ontologies may be a useful tool.
Nascent examples of such an ontology exist. For instance, the Open Biological and Biomedical Ontology (OBO) Foundry has an environment ontology (ENVO,  Buttigieg et al. , 2016)  but it was not created to help scientists collaborate and is likely too broad for the purposes of CZ synthesis/integration activities. NASA's Jet Propulsion Laboratory has created an ontology of earth science concepts called SWEET (Semantic Web for Earth and Environment Technology; DiGiuseppe et al. , 2014) that might be a promising start to a community ontology to enable data sharing and harmonization across CZ networks. Future research is needed to examine whether formal ontologies are appropriate underpinnings for data harmonization tools, or whether a schema driven approach such as the SoDaH project might be more appropriate, or whether other avenues may be needed.
Towards a new class of CZ information scientists Given that data harmonization and integration were identified as a bottleneck for any CZ synthesis effort, and the highly multidisciplinary field of CZ science, the cyberseminar series (CUAHSI, 2021b) clearly highlighted the need for a new class of CZ information scientists. This meant involving scientist-users "in a hands-on way" in the design process of the data portals, working hand in hand with the database professionals to make sure that technical proficiency meets the users' needs. Community feedback from the survey further implied looking beyond researchers and actively engaging database users and creators, such as data scientists, managers and state agencies. Such synergies are expected to better connect long-term data portals with short-term and/or project-based data collection and may even incentivize data rescue, i.
Consequently, a network-based approach is expected to enhance interpersonal interactions and establish career-spanning, collegial relationships and friendships. The power of such a network lies in its ability to mobilize people and further empower CZ students, early careers and scientists to pursue the pathbreaking questions that address the most complex as well as socially-relevant problems of our time. One approach to facilitating a global network-of-networks framework is through the use of ICON science principles. These principles focus on the intentional design of research efforts to be "Integrated" across disciplines and scales, "Coordinated" through the use of consistent methods, "Open" throughout the research lifecycle (including publication of FAIR metadata and data), and "Networked" with a broad range of stakeholders to understand and respond to collective needs, priorities, perspectives, and risks  (Goldman et al.
, 2022). For example, the Coordinated component of ICON is focused on intentional a priori planning and implementation of strategies to generate FAIR metadata and data that are generated in a standardized format as well as consistently structured upon publication. Further, these consistent protocols are expected to be Openly shared and framed based on multidisciplinary (i. e. , Integrated) feedback/consensus (i. e. , Networked). Using the ICON principles together is therefore meant to enable development of knowledge, data, and models that are generalizable or transferable across diverse settings. Additionally, ICON is meant to enable research outcomes that are mutually beneficial across stakeholders, ranging from core research teams to land owners/managers to the general public. Producing research outcomes that are transferable and mutually beneficial does not happen by chance.
Secondly, new techniques and technologies are providing observations that were previously not possible, such as eddy covariance-based measurements of N 2 O and other trace gasses and fiber optic-based measurements of soil temperature, chemical and biological properties that can be useful indicators of global climate change  (Baldocchi, 2014; Hubbard et al. , 2020). Additionally, if these unique and novel watershed observations across networks/sites are to be analyzed through AI/MLbased approaches, such approaches hold the potential to transform our understanding, prediction and management of CZ/watershed behavior through the rapid identification of system tipping point precursors; the assimilation of diverse, multi-scale data into models for near-real time prediction and water management; and the ability for models to inform real-time optimization of autonomous sensing systems from local to regional to global scales.