# Preparing for the Next Generation of Material Environmental Health and Safety (EHS) Concerns: Guidelines for Future Data Curation Collaborations

Jaleesia D Amos, Anastasios G Papadiamantis, Camille de Garidel-Thoron, Zhao Zhang, Yuan Tian, Gregory V Lowry, Iseult Lynch, Jérôme Rose, Armand Masion, Lee A Walker, et al.

► To cite this version:

Jaleesia D Amos, Anastasios G Papadiamantis, Camille de Garidel-Thoron, Zhao Zhang, Yuan Tian, et al. Preparing for the Next Generation of Material Environmental Health and Safety (EHS) Concerns: Guidelines for Future Data Curation Collaborations. Environmental Science and Technology Letters, 2025, 10.1021/acs.estlett.5c00311. [https://doi.org/10.1021/acs.estlett.5c00311](https://doi.org/10.1021/acs.estlett.5c00311)

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# Guidelines for Future Data Curation Based Collaborations

2 Jaleesia D. Amos¹,², Anastasios G. Papadiamantis³,⁴, Camille de Garidel-Thoron¹,⁵, Zhao Zhang¹,²,

3 ‡, Yuan Tian¹,², Gregory V. Lowry¹,⁶, Iseult Lynch³, Jérôme Rose¹,²,⁵, Armand Masion¹,⁵, Lee A.

4 Walker⁷, Mélanie Auffan¹,²,⁵, Claus Svendsen⁸, Christine Ogilvie Hendren¹,², §, Mark R.

5 Wiesner¹,²,†

7 ¹Center for the Environmental Implications of Nano Technology (CEINT)

8 ²Civil & Environmental Engineering, Duke University, Durham, North Carolina 27708, United

10 States

11 ³School of Geography, Earth and Environmental Sciences (GEES), University of Birmingham

12 (UoB), Edgbaston, Birmingham, United Kingdom

13 ⁴NovaMechanics Ltd, Nicosia, Cyprus

14 ⁵Aix Marseille University, CNRS, IRD, INRAE, Coll France, CEREGE, 13545 Aix-en-Provence,

15 France

16 Civil & Environmental Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213,

17 United States

18 ⁶Civil & Environmental Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania

19 15213, United States

20 ⁷UK Centre for Ecology and Hydrology, Lancaster Environment Centre, Lancaster, UK

21 ⁸UK Centre for Ecology and Hydrology, Wallingford, Oxfordshire, UK

22 ⁹Civil & Environmental Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania

23 15213, United States

24 ‡Present address: Lucideon M+P, Morrisville, NC 27560, USA

25 ⁵Present address: Department of Geological and Environmental Sciences, Appalachian State

26 University, Boone, NC, USA

27 †Corresponding author; Phone: (919) 660-5292; Email: wiesner@duke.edu; Address: 120

28 Hudson Hall, Durham, NC 27708-0287

29 Abstract

30 Communal development of processes to standardize data is a recognized necessity of risk

31 assessors and regulators in the nano-environmental health and safety field. Emergence of new

32 nano-scaled contaminants and nanotechnology's current transition into advanced materials

33 have broadened the boundaries of the field. These field expanding topics exemplify the core

34 role of collaboration in creating 'good' and trustable data for future analyses. We describe an

35 international collaboration focused at an early stage in the informatics process, the curation

36 level. Our experiences are described in nine guidelines that can be adopted by future

37 collaborations. These guidelines were written to be interpretable and are structured within

38 scalable phases. Guidance is also provided on necessary personnel roles that should be

39 incorporated into funding plans for developing and using advanced materials. Current shifts in

40 the field demand community consensus to define data that are qualified as 'good' and

41 'trustable' which require collaboration at the early stages of the informatics and data curation

42 processes.
43 **Introduction**

44 In the context of innovation, environmental sustainability, and energy transition, it is essential
to leverage data on exposure and environmental hazards associated not only with materials
45 already in use, but also next-generation materials to tackle emerging scientific and industrial
46 challenges. A robust assessment of inadvertent risks from the use and release of nano-scaled
47 materials or particulate contaminants (i.e. nano- and microplastics, ultrafine air pollution from
48 climate related fires) into environmental and human systems requires multi-scaled cooperation
49 and collaborative transparency¹. Thus, international consortia, university centers, and
50 government agencies have been funded and created to collaboratively design and perform
51 complex (i.e. micro- and mesocosms studies) and multi-phased experiments, share resources
52 needed for materials and risk characterization, and serve as intellectual hubs for exchanging
53 ideas². Our current data science era developing machine learning (ML) and artificial intelligence
54 (AI) algorithms has highlighted the value of data to discover novel property-structure
55 relationships and answer new scientific questions. The call to preserve experimental data
56 generated from such organizations has also been promoted with funding agencies requiring
57 data management plans³, ⁴. It naturally follows that an inter-consortia collaboration centered
58 around risk assessment data of nano-scaled materials and nano-enabled products would
59 emerge.
60

61 New abilities to reuse data sets and funding requirements for data management have created
62 communities dedicated towards making individual datasets interoperable and comparable by
63 essentially, FAIR-ifying⁵ existing and new datasets. Individual consortium have published their
64 process required for curating nano-data from one international project, but few have written
65 about the process of coordinating nano-data curation processes between transatlantic projects.
66 Efforts to merge datasets from separate projects typically occurs after one project has ended,
67 when new data stewards retrofit a dataset or database from a previous project.
68
69
70
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<figure><img src="image_2.png" /><figcaption>**Figure 1** Phases of a data curation collaboration. A flow chart which shows three phases of a data curation based collaboration. The first phase – define data collaboration goals – is about understanding data profiles of each partner and understanding the type of collaboration that should proceed based on the goals described. The second phase – build team and develop process – is about determining who is required to successfully complete previously stated goals and developing a scalable process. Phase three – onboard data generators – is about introducing data preservation plans to a larger audience, usually other members of a project, within a given time frame. Although described as three distinct phases, these phases often overlap in reality. The guidelines listed under each phase are meant to provide future transatlantic or transcontinental collaborations guidance on how-to conduct a more efficient curation-based collaboration in the future.</figcaption></figure>

In this perspective, we outline the guidelines learned from an international nano-data curation effort between a European Union based international consortium Nanomaterial Fate and Speciation in the Environment (NanoFASE), a French-based laboratory Research and Teaching Centre of Environmental Geosciences (CEREGE), and a US-based consortium Center for the Environmental Implications of NanoTechnology (CEINT). These three groups coordinated nano-dataset curation while projects were still ongoing and in some cases, generating data.

The data coordination effort between the three entities was not envisioned when each center was funded; however, CEREGE was a partner within the CEINT consortium, and a leveraged, self-funded data curation effort between CEINT and NanoFASE was established using CEINT's NanoInformatics Knowledge Commons (CEINT-NIKC)⁶ as a focal point. We mean here that CEINT's curation efforts were not funded by NanoFASE, and NanoFASE's curation efforts were not funded by CEINT. This voluntary collaboration was born from a common goal to fortify and diversify individual datasets through data sharing, combined with established trust between the researchers sufficient to motivate self-funded pursuit of the common goal. It is worth noting that the collaboration required significant investment of time and resources that fell almost entirely outside of funded activities. This is in part because it was exploratory and in part because, until now, such efforts have often been unrecognized invisible labor. Future teams should explicitly plan for, and fund these data curation activities to make their data useful for future sharing and exploration. Each partner was willing to dedicate positions and time to this collaboration because there was a common vision for the potential benefits of deeper data integration.

The purpose of this paper is 1) to describe the coordinated data curation effort between international collaborators and the unique challenges that arise in data sharing amongst
transatlantic collaborators, 2) to describe the process of organizing data curation for a large
international consortium, 3) and to provide guidance for future data collaborations from the
successes and setbacks of our joint experiences. Reflecting on the entire collaboration process,
we have identified nine guidelines for a data curation-based collaboration which has been
divided into three phases, with three guidelines per phase: 1) define data collaboration goals, 2)
build data curation teams and co-develop the curation process, 3) and onboard data
generators. These guidelines and phases have been organized into a flow chart to better enable
success for future collaborations focusing on data curation (figure 1).

119 PHASE ONE: Define data collaboration goals

120 The first phase sets the framework for the collaboration by exploring the data landscape,
121 determining the collaboration format, and exploring the collective needs to define collaborative
goals; therefore, it is arguably the most important phase.
122

<figure><img src="image_3.png" /></figure>

123
124 **Figure 2 Profile of partner data and interests.** This diagram shows the compatibility of partner interests while also profiling individual datasets. The differences in the individual datasets allow the group to diversify their data, while the commonalities in each dataset (in bold) provide proficient data overlap to allow expansion of existing data.
125
126

127 Guideline 1: Outline data commonality and diversity

128 Our first step was to examine the data that each group was generating or curating to
129 understand the differences and similarities of the datasets and ontologies. Each group was
130 interested in data that looked at environmental exposures to nanomaterials and how such
131 exposures led to nanomaterial transport through environmental compartments and biological
132 systems, and their ultimate environmental and organismal fate and impacts. Within this
133 common exposure-to-fate process were individual data subsets around understanding
134 mechanisms of bio-uptake and toxicity, and preventing or mitigating exposures through safe(r)-
135 by-design concepts⁷,⁸. A more complete picture of the individual datasets and collective
interests can be found in figure 2. It is imperative to note that one of the reasons why a collaboration was possible was because there was enough commonality between the types of data among each group to create significant overlap; and yet, the differences in the individual datasets made collaboration desirable as it meant that each group would be diversifying and increasing the magnitude of their dataset upon sharing.

## Guideline 2: Define focal point of data collaboration

Diversifying datasets through data sharing is not a new concept; however, a common hurdle that often arises is data compatibility⁹. Although groups may want to share data on similar topics to augment individual datasets, if there are stark differences in recorded metadata those datasets may not be compatible for combining or combined to produce a dataset with gaps of missing data. For example, if two separate researchers are recording the same phenomenon and time is a phenomenon-relevant metadata, but one researcher records the phenomenon unfolding on a scale of hours and the other on the scale of days, then combining and analyzing these datasets may be difficult. Data curation was chosen as the collaborative focal point because it is early enough in the informatics process such that the ‘what’ and ‘how’ of creating a dataset with interoperable fields could be agreed upon. The significant role data curation contributes toward mitigating challenges in data integration has been previously suggested⁹,¹⁰. It was also chosen because of where each partner was in their curation journey.

Initial talks to discuss the potential for linking datasets began when the NanoFASE project was in its early phase, CEINT was entering its final years but still in the early phases of curation, and though CEREGE is a permanent institution unbound by project timelines, CEREGE was in the process of curating its data as well. Therefore, the timing was optimal for each partner to engage in a curation focused collaboration.

## Guideline 3: Distinguish partner goals from group goals

Each partner was motivated towards data curation for individual reasons, including fulfilling grant requirements or as inputs for modeling and risk assessment for regulatory purposes. The CEINT team was curating a specific dataset (figure 2) to model nano-release from consumer products. CEREGE was invested in integrating their mesocosm data. NanoFASE was determining their data management plan to preserve the data that was being generated from the project's work packages. It was important to identify primary and secondary goals of the joint collaboration. Achieving individual goals became the mutually accepted priority, while achieving those goals in a manner to enable future data sharing was the secondary shared goal motivating the effort.

The aim was not to curate everyone's data into one database, but to coordinate our individual curation efforts so that datasets would be interoperable for sharing and combining later. Each group had access to an individual database which was either inherited from previous projects, accessed through other existing partnerships, or one that had been built by the group for their specific needs. It became clear that interoperability would be best achieved by using one common data format. After reviewing each partner's data needs it was decided that the NIKC would be used as a common curation format as it was adaptable to each group's data.
177 **PHASE TWO: Build team and develop process**

178 The second phase develops the mechanics for collaboration on individual and collective scales
179 by choosing the right people to create and adapt the curation process through iterative
180 communication.

181 **Guideline 4: Appoint individuals to necessary roles**

182 After discussing the broader collaborative ideals, the next step was to figure out how to bring
183 three tandem curation endeavors in sync with each other. This required understanding who
184 was already doing this work on each team, and understanding their role(s) and responsibilities.
185 Developing a collaborative process around data curation that emphasizes the needs of each
186 partner is an onerous task that can only be achieved by designating individuals who are
187 committed to moving the collaboration forward, also known as data shepherds¹¹. This initial
188 group was defined by one CEREGE member, one NanoFASE member, and two CEINT members,
189 which changed through the timeline of this work. Each member was regarded as persons with
190 full understanding of their groups' scientific work, needs, and with the knowledge needed to
191 move the work forward.

192 **Guideline 5: Create and refine the data collaboration process**

193 The CEINT team needed to teach the NIKC curation method within the data shepherd group,
194 which occurred mostly in one-on-one sessions both in-person and via teleconference. "Instance
195 mapping" was a curation method developed by CEINT based on the NIKC's unique structure¹².
196 Instance mapping as a curation method became the basis for teaching NIKC curation in addition
197 to a curation manual. Whether in-person or virtual, the teaching process had the same five
198 steps: 1) relay a conceptual understanding of the database's structure and organization, 2)
199 instructions on how to make an instance map, 3) assistance in translating the map into the
200 database template, 4) independent practice with a familiar dataset, 5) and reviewing the results
201 and resolving remaining questions. After training, it is possible to adapt and refine the curation
202 process according to the specific needs of collaborating partners.

203 **Guideline 6: Designate time for regular communication**

204 The data shepherd team met on a weekly basis via video calls. These meetings were designated
205 curation discussions on topics such as standardizing the curation process, giving updates on
206 individual curation efforts, and training sessions. These weekly meetings were essential, to
207 proceed beyond phase one. To appropriately illustrate the importance of these meetings in
208 syncing independent curation efforts, we provide an example of a real discussion for
209 standardizing curation formatting of the medium. While the NIKC database categorized data in
210 five categories, in this scenario, only two categories are discussed: the medium and property. A
211 NIKC-defined medium is the environment surrounding the nanomaterial (i.e. biological,
212 experimental settings), and the property may be used to describe the medium (i.e.
213 temperature, soil composition). The CEINT team was in the process of curating their dataset
214 when an issue of granularity arose – how much metadata is appropriate in describing the
215 medium, and what metadata should be recorded?
216 The example study used to center the discussion was a cell-line toxicity exposure study to nano-silver. The CEINT team originally proposed curating the cell-line used (A549 cells)¹³ as one NIKC-medium and the culture medium used to grow the cells as another NIKC-medium with the temperature listed as a property of the culture medium; thereby, describing the nano-silver's environment. During independent CEINT discussions, it was suggested to rethink curation of the medium from the perspective of a database query. How would someone querying the database find this dataset? Thus, it was decided a broader level be added to the medium for query purposes - a parameter termed the class (i.e. plant or mammal).

224 This suggestion was later proposed in a data-shepherd meeting: in the case given, the medium would be curated as 'mammal', and the cell-line (A549 cells) would be curated as a property of the medium. While discussing the change, it became clear that another category was needed – the name of the organism. Upon further discussion of how to curate the name of the organism, it was decided that the Latin name or scientific name of the organism should be used.
225 Linguistically, this was most accessible as 'common' names of animals are not universal across 226 languages. It was finally decided in the cell-line exposure study, the NIKC-medium would be 227 curated as mammal with three descriptive properties – the cell-line (A549 cells), the Latin name 228 (Homo Sapiens), and the type of assay (In Vitro). This became the agreed upon standard for 229 curating the medium in biological studies henceforth.
230

234 Although video calls allowed for the data shepherds to connect, in-person meetings were still 235 necessary. The exchange of ideas and discussions that occurred during a 2-3 day in-person 236 meeting were equivalent to weeks of virtual meetings. It is necessary to have a team of 237 individuals who are dedicated to discussing the detailed aspects of curation. Building the 238 curation team was essential to further developing the teaching process. The small-group 239 discussions refining the curation process were essential before introducing the process to data 240 generators.
241

## PHASE THREE: Onboard data generators

242 As previously stated in phase one, the individual goals of CEREGE and NanoFASE were to curate 243 data that had been generated across the center/project; therefore, the next phase was to 244 onboard the data generators.
245

## Guideline 7: Introduce data plans to data generators early

246 Projects requiring digital storage of data need to inform participants that curation of their work 247 is required as early as possible. Data curation is a lengthy process requiring participants to 248 understand the curation format, understand what data and meta-data will need to be 249 preserved, and practice curating their work. Time also needs to be allotted for data quality 250 checks and revisions. Mobilizing potentially dozens of individual researchers within a center to 251 complete these processes therefore needs to be built into projected timelines coincided with 252 the work itself, not an afterthought saved for the end of the project.
253

254 With the NanoFASE project, a member of the CEINT data integration team was invited to attend 255 project meetings. During such meetings, project leadership would introduce (or remind 256 members of) the required data management plan and its non-negotiability. The NanoFASE data 257 shepherd would (re)introduce the database chosen for data management (the NIKC) and the
257 CEINT delegate. The CEINT delegate would later run curation workshops which were built into
258 the project meeting schedule. The CEREGE onboarding process was handled independently by
259 the CEREGE delegate, which provided in-person hands-on training. These sessions provided
260 training on NIKC-curation and broader data integration training for tools within the broader
261 Safe(r) Ecodesign Research and Education applied to NAnomaterial DEvelopment (SERENADE)
262 consortia¹⁴, ¹⁵.

263 The training used amongst the data shepherds was further refined to meet the needs of a
264 larger group and NanoFASE and CEREGE specific needs. The CEREGE and NanoFASE data
265 shepherds were in the unique possession of data curation training, understanding of their
266 project data, and trust with their center researchers. They were therefore best able to lead the
267 data onboarding process for their center/project, while each data shepherd provided assistance
268 and advice as needed.

269 **Guideline 8: Data shepherds are needed to help data generators through the curation process**

270 Both NanoFASE and CEREGE were preserving diverse datasets from separate yet interlinked
271 fields into a single format. Data shepherds alleviated some of the data friction¹⁶, ¹⁷ that
272 persisted in individual follow-up sessions. Data friction was originally defined by Edwards as a
273 "force that resists and impedes...[occurring when] data moves between people, substrates,
274 organizations, or machines - from one lab to another, from one discipline to another, from a
275 sensor to a computer, or from one data format to another". Data shepherds were essential in
276 helping data generators curate their own data by being a source of personal guidance for
277 individual datasets. The person best suited to understand each project's specific needs was that
278 project's delegate. The NanoFASE project was spread across EU national borders and
279 institutions, as such, the NanoFASE delegate traveled across sites to ensure proper curation
280 with virtual assistance from the CEINT delegate. While curation at CEREGE is on-going, the
281 NanoFASE curation timeline (from first introduction to curation completion) was approximately
282 three years.

283 **Guideline 9: Even data require visas to move**

284 External CEINT curation was done in Excel templates, and an important issue that needed to be
285 addressed centered around data storage. The transatlantic aspects of the collaboration
286 presented unique legal questions. Although data sharing is common practice and done with
287 little issue, long-term data storage presented some legal uncertainty regarding EU and US laws
288 on data and whether the laws applied equally to scientific data as personal data. Add to the
289 complexity that while the collaboration was self-funded, the funding of the original work and
290 labor to generate and collect data prior to integration was provided by the US government for
291 CEINT and the EU for NanoFASE and France for CEREGE. During the course of the collaboration
292 CEREGE had developed the MESOCOSM¹⁸ database which incorporated the most fitting aspects
293 of the NIKC for their needs, and also settled questions around storage. It was decided that
294 CEINT would transfer the NIKC infrastructure to the NanoFASE team, so that EU-funded data
295 could be stored in the EU. A potential solution to the politics of data storage can be adopted
296 from more mature informatics fields such as the Protein Data Bank (PDB) in bioinformatics,
297 which has data centers spread across the world. Users are directed to different servers based
298 on their continental origin with a master server allowing users to access data from each server
regardless of their location¹⁹. Replicating this scale of technical support and organization for
globally interconnected servers requires a more permanent team and funding sources.

## 301 An Evolving Technological Landscape

Our active data curation collaboration ended in 2020, partially due to the profound disrupting
events of the year and to the ending of partner project timelines. In the years since, the
technological landscape has dramatically shifted with the release of natural language
processing (NLP) AI tools. Segments of the medical field are already exploring methods for
incorporating AI in the curation and metadata extraction processes involved in specimen²⁰
imaging. Ayadi et al. proposed an ontology-based NLP information extraction to enrich
nanomaterial environmental exposure database²¹ and Chen et al. has reviewed potential
opportunities to merge (ML) methods for curation within the nanomedicine arena²².

In each exploration of merging AI/ML into the data curation process, a 'human-in-the-loop'²³, ²⁴ approach is adopted. Indicating that some amount of data shepherding is still necessary. Future data curation efforts that explore the inclusion of AI/ML into the process, should proceed with the knowledge that collaboration and communication are still essential to ensuring that researchers are recording the same system characteristics and measurements with comparable methods and protocols.

## 316 Concluding Remarks

It is important to note that while we were successful in achieving individual goals, which were
our first priority, our secondary goal of creating datasets compatible for combining was only
partially completed when evaluated compared to our initial shared vision that motivated the
collaborative efforts described here. While the efforts of the data curation team successfully
created individual datasets compatible for combining, the process of combining datasets was
not initiated for several reasons, including the questions of legality discussed under guideline 9.
It also became clear that fully combining the datasets into a fully integrated merged dataset
would require a longer timeline with a dedicated team expanding beyond the data shepherds
to include a more permanent technical team. That this collaborative trio was voluntary and self-
funded highlights how the future combining of disparate and particularly multi-national
datasets will require dedicated funding.

It should be noted that while our secondary goal remains incomplete, the impact of our work
and collaboration has still rippled through and contributed to successes within multiple
projects. Including the NanoCommons project which is an exemplary case of successfully
combining datasets from multiple EU projects into one infrastructure, including the NanoFASE
NIKC-curated dataset²⁵. A dataset originally curated during our joint collaboration, and Furxhi et
al., illustrated how the ASINA project successfully implemented their data management plan
which included using data shepherds to curate project data into NIKC Excel templates²⁶. Other
impacts can be seen in the broader adoption of shared language and concepts developed
through our collaborative work. 'Data Shepherd' in its contextual role in data curation was
defined through the work of our initial collaboration, and further explored in the National
Cancer Institute NanoTechnology Working Group (NCI Nano WG)¹¹ and other nanoinformatics
communities²⁷. Instance Mapping became a method for collaborative data curation while
340 during the data generator onboarding process; however, it was later developed into an
341 informatic tool for expanded uses²⁸.

342 While much has been written on FAIR-ifying advanced materials and nano data, one avenue
343 that warrants further exploration is collaboration at the curation level at the early stages of a
344 collaborative project. We have presented guidelines gained from a voluntary transatlantic and
345 international curation focused data collaboration in the hopes that any future similar
346 collaborations may apply the guidelines from our experiences to a successful outcome.

347
348 **Funding Sources**
349 This material is based upon work supported by the National Science Foundation (NSF) and the
350 Environmental Protection Agency (EPA) under NSF Cooperative Agreement EF-0830093 and
351 DBI-1266252, Center for the Environmental Implications of NanoTechnology (CEINT). This
352 material is based upon work funded in part by the French National Research Agency (ANR)
353 under the project ANR-11-LABX-0064 (Labex SERENADE), through the AMIDEX project (No.
354 ANR11-IDEX-0001-02). The authors also acknowledge the CNRS-INSU for the funding of the IRP
355 iNOVE. This material is based upon work supported by the European Union's Horizon 2020
356 research and innovation programme under grant agreement 646002. Jaleesia Amos is a Sloan
357 Scholar supported by the Alfred P. Sloan Foundation's Minority Ph.D. (MPHD) Program,
358 awarded in 2020-21.

359
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Phase 1

Defining Data Collaboration Goals

**Guideline 1:** Outline data commonality and diversity

**Guideline 2:** Define focal point of data collaboration

**Guideline 3:** Distinguish partner goals from group goals

Phase 2

Building Team and Develop Process

**Guideline 4:** Appoint individuals to necessary roles

**Guideline 5:** Create and refine the data collaboration process

**Guideline 6:** Designate time for regular communication

Phase 3

Onboarding Data Generators

**Guideline 7:** Introduce data plans to data generators early

**Guideline 8:** Data shephards are needed to help data generators through the curation process

**Guideline 9:** Even data require visas to move
CEREGE - Dataset

Materials

• Metal

• Metal oxides

• Nanoclay

• Imogolite

Study

• In vitro

• In vivo

• Aging

• Mesocosms

Data Topic

• Exposure

• Fate

• Toxicity

• Risk Assessment

• Safe(r)-by-Design

• Bio-uptake

Materials

• Silver

• Carbon nanotubes

Study

• In vitro

• In vivo

• Mesocosms

CEINT - Dataset

Materials

• Silver

• Metal Oxides

• Nano-mixtures

• Doped-nanomaterials

• Hydroxylapatite

Study

• In vitro

• In vivo

• In situ

NanoFASE - Dataset