dataesr/scientific-paragraphs-categorization huggingface dataset:
enabled crowdsourcing capabilities, which allow users to suggest edits to any entry and permits researchers to upload new findings ranging from human and environmental exposure data to complete life cycle assessments. There are inherent limitations to this type of database, but these modifications to the inventory addressed the majority of criticisms raised in published literature and in surveys of nanotechnology stakeholders and experts. The development of standardized methods and metrics for nanomaterial characterization and labelling in consumer products can lead to greater understanding between the key stakeholders in nanotechnology, especially consumers, researchers, regulators, and industry.
In 2010, the CPI listed 1012 products from 409 companies in 24 countries. Even though it did not go through substantial updates in the period between 2010 and 2013, it continued being heavily cited in government reports  [2]  and the scientific literature -the website http://www. nanotechproject. org has been cited over 2,580 times in articles according to Google Scholar -and became a popular indicator of the prevalence of nanotechnology in everyday life and the need to further study its potential social, economical, and environmental impacts  [3] [4] [5] [6]. The CPI has also been criticized due to its lack of science-based data to support manufacturer claims. Other longstanding suggestions for improvement included: more frequent updates, indications when products were no longer available for purchase by consumers, and the inclusion of more product categories to improve the searchability of the CPI database  [7].
Since the creation of the CPI, other nanotechnology-related inventories have been developed around the world. In 2006, a German company launched a freely accessible internet database of nanotechnology products  [8]. The website associated with this database was not accessible at the time of this writing and its last available record is from May 2014, when 586 products were listed. In 2007, Japan's National Institute of Advanced Industrial Science and Technology created an inventory of "nanotechnology-claimed consumer products" available in Japan  [2]. This inventory is freely accessible online and it acknowledges the CPI in its website. At the time of this writing, the inventory listed 541 product lines and 1241 products; its last update occurred in 2010  [9]. In 2009, two European consumer organizations, the European Consumers Organization (BEUC) and the European Consumer Voice in Standardization (ANEC), joined efforts to develop an inventory of "consumer products with nano-claims" available to consumers in Europe  [10].
A new inventory was generated annually from 2009 to 2012, but the 2011 and 2012 versions focused exclusively on products containing silver nanoparticles (nanosilver); the latest version in 2012 listed 141 nanosilver products. This inventory does not provide a searchable online database, but it can be downloaded for free as an Excel spreadsheet. In 2012, the Danish Consumer Council and Ecological Council and the Technical University of Denmark's Department of Environmental Engineering launched "The Nanodatabase", an inventory of products available for purchase that are claimed to contain nanomaterials and are available in the European consumer market  [11]. This inventory has been continually updated and it currently lists 1423 products. These worldwide efforts to understand the transition of nanotechnology from the laboratory bench to the commercial marketplace substantiate the need for applying the concept of nanoinformatics to a nanotechnology-enabled consumer products database, which is to determine the most relevant and useful information needed by a variety of stakeholders and to develop tools for its most effective use  [12].
Databases such as the CPI offer information useful and relevant to a variety of stakeholders who are interested in a) understanding which consumer products incorporate nanotechnology and b) developing strategies, tools, and policies that may be needed to ensure safe and responsible use of those products. Nanomaterials are regulated without specific provisions in the U. S. as hazardous chemical substances and pesticides, under the EPA's Toxic Substances Control Act (TSCA)  [13]  and the Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA)  [14]. When used as food additives, drugs, or cosmetics, nanomaterials are regulated under the Federal Food, Drug, and Cosmetic Act (FFDCA). a The CPI now has crowdsourcing capabilities, so these numbers are a snapshot in time and will not represent the CPI at the time of reading.
The objective of this work was to modify the CPI to improve its functionality, reliability, and utility to the diverse group of stakeholders who have come to depend on it as a critical resource for current information on nano-enabled consumer products. Specific objectives were  (1)  to update the CPI data to gain an insight into the penetration of nanotechnology in the consumer products market over the past decade; (2) to determine and implement improvements to the CPI based on the scientific literature and a survey of nanotechnology experts and CPI users; and to (3) develop a sustainable model to facilitate future CPI maintenance using crowdsourcing tools. Below, we present a brief history of this inventory over a decade of existence. We also describe the specific changes made in the inventory during this project (referred here as CPI 2.
New nanomaterial descriptors Eight new product descriptors were introduced to facilitate the use of this database by a variety of stakeholders (namely industry and the scientific and regulatory communities): 1. main nanomaterial composition or type, 2. nanomaterial shape and size, 3. nanomaterial coating or stabilizing agent, 4. nanomaterial location within the product, 5. nanomaterial function in the product, 6. potential exposure pathways, 7. "how much we know", 8. "researchers say". The experimental section of this paper describes all new product descriptors. The results of the five new quantitative descriptors are presented and discussed below. Since the "nanomaterial shape and size", "coating and stabilizing agent", and the "researchers say" categories are text-entry data fields, thus qualitative information at this point, we have not included their analysis in this paper.
0 are somewhat in agreement with those of the Danish Nanodatabase. The Nanodatabase also lists a high fraction of products with unknown nanomaterial composition (944 products or 66%) and, among known compositions, silver is also the most frequently advertised nanomaterial component, with 207 products or 14. 5%  [11]. Silver nanoparticles are popular consumer product additives due to their well-documented antimicrobial properties  [22]. Figure  3  shows how the availability of these major nanomaterial composition groups changed over time. Since the start of the CPI 2. 0 project (2012), products with unknown (not advertised) nanomaterial compositions have decreased by 12%, which is partially due to these products being archived and of their composition being identified and added to the inventory.
Berube et al. [7]  offered a critique of the original CPI in 2010, which focused primarily on the lack of data pertinent to the dosages of nanomaterials to which consumers might be exposed through CPI-listed products. This is a valid criticism given that information used to populate the CPI is based primarily on marketing claims made by manufacturers. However, the most recent modifications of the CPI offer a potential remedy for data gaps through the contributions of third-party research teams. These modifications are especially timely as there is a growing number of published studies assessing consumer exposure to nanomaterials released during the use of nanotechnologyenhanced consumer products  [27] , such as cosmetic powders  [28] , sprays  [29, 30] , general household products  [31] , and products for children  [32, 33].
Crowdsourcing Since October 29, 2013, when the modified inventory (CPI 2. 0) was released, 557 new user accounts have been requested. Of these, only approximately 10 users who were not directly or indirectly involved in the research team performing the CPI upgrade and maintenance suggested updates or edits to CPI entries. These edits have all been suggested by users from industry and academia. Future work is needed to better educate users on their role as curators of CPI 2. 0 and the importance of the data they contribute. Providing the supporting technical data required to verify the nature and quantity of nanomaterial components in CPI-listed products is a massive undertaking, and no single laboratory can accomplish it on its own or within a short amount of time. A long-term solution is to promote the importance of crowd-sourcing data collection and implementing standard data collection and reporting best practices that can help reliably populate the CPI with much needed supporting data.
Results convey a general belief or hope that the CPI will become more useful after the modifications reported in this publication. When asked the following open-ended questions: "How did you use the CPI in your work?" and "To what end do you think you might use the CPI in the future?", answers could be easily grouped into three main categories: (1) for raising awareness, teaching, or for urging the need for regulation, (2) to justify the need for research in research proposals or papers, and (3) to use the inventory data for research (Figure  9 ). Half the respondents (51%) have used the CPI in the past to gather data for research (e. g. , searching for consumer products of a certain nanomaterial composition to understand their potential applications or consumer exposure) while 74% believe they will use the CPI for that purpose in the future.
Conclusion The modified version of the Wilson Center's nanotechnology consumer products inventory (CPI 2. 0) was released in October 2013. We improved the searchability and utility of the inventory by including new descriptors for both the consumer products and the nanomaterial components of those products (e. g. , size, concentration, and potential exposure routes). The updated CPI 2. 0 now links listed products to published scientific information, where available, and includes a metric to assess the reli-ability of the data associated with each entry. Finally, the CPI 2. 0 has enabled crowdsourcing capabilities, which allow registered users to upload new findings such as basic product composition information, human and environmental exposure data, and complete life cycle assessments. There are inherent limitations to this type of database, but recent improvements address the majority of issues raised in published literature and in a survey of nanotechnology experts.
Improvements to the CPI were motivated, in part, by the recognition that it represents and will continue to represent an important information resource for a broad range of stakeholders, especially consumers and the academic and regulatory communities. The CPI is a useful interactive database for educating consumers and legislators on the real-world applications of nanotechnology. Michaelson stated that the CPI transformed "the face of nanotechnology away from innovations in the realm of science fiction to the iconic images of everyday consumer products"  [2]. The academic community can continue to make use of this inventory to help prioritize, for example, which types of products or nanomaterial components to evaluate in human exposure or toxicity studies, life cycle assessments, and nanomaterial release studies.
Nanomaterial shape and size Because there are many different ways in which manufacturers can measure and describe the shape and size of nanomaterials in consumer products (i. e. , units of nanometers or micrometers, thickness of nanofilms, diameter or length of fibers or tubes, diameter or radius of nanoparticles, maximum, median, average, or minimum size), this descriptor was added as a text entry field in the database, which allows for any form of data entry but makes data analysis cumbersome.
Acknowledgements Funding for this work was provided by the  Institute for Critical Technology and Applied Science (ICTAS)  at  Virginia Tech  and the  Virginia Tech Center for Sustainable Nanotechnology (VTSuN). We acknowledge the important help of  J. Rousso ,  E. Bruning ,  S. Guldin ,  J. Wang ,  D. Yang ,  X. Zhou ,  L. Marr , the VTSuN graduate students in updating inventory entries, and the  Laboratory for Interdisciplinary Statistical Analysis at Virginia Tech. We also acknowledge the  Center for the Environmental Implications of Nanotechnology , funded under  NSF  Cooperative Agreement  EF-0830093 , for helping to inform our understanding of the broad world of manufactured nanomaterials. Co-author M. Hull acknowledges helpful discussions with  A. Maynard  of the  Arizona State University Risk Innovation Lab  that provided important motivation for this work.
This paper presents the SPOT-4 (Take 5) experiment, aimed at providing time series of optical images simulating the repetitivity, the resolution and the large swath of Sentinel-2 images. The aim was to help users set up and test their applications and methods, before Sentinel-2 mission data become available. In 2016, when both Sentinel-2 satellites are operational, and for at least fifteen years, users will have access to high resolution time series of images systematically acquired every five days, over the whole Earth land surfaces. Thanks to Sentinel-2's high revisit frequency, a given surface should be observed without clouds at least once a month, except in the most cloudy periods and regions. In 2013, the Centre National d'Etudes Spatiales (CNES) lowered the orbit altitude of SPOT-4, to place it on a five-day repeat cycle orbit for a duration of five months.
5 pixels in the large majority of cases. Despite the lack of a blue band on the SPOT-4 satellite, the cloud and shadow detection yielded good results, while the aerosol optical thickness was measured with a root mean square error better than 0. 06. The surface reflectances after atmospheric correction were compared with in situ data and other satellite data showing little bias and the standard deviation of surface reflectance errors in the range (0. 01-0. 02). The Take 5 experiment is being repeated in 2015 with the SPOT-5 satellite with an enhanced resolution.
Usually, when the development of a new satellite mission starts, space agencies set up a preparatory program  [2, 3]  to provide simulated data to future users. Thanks to that, users can get ready to use the mission data as soon as they are available, after the satellite and ground segment commissioning phases. This is often done using aerial acquisitions that provide simulations of the future images, but in the case of ESA's Sentinel-2 program, this task is complex, because the main new feature of the mission is its capacity to acquire time series with a frequent revisit. Such a characteristic is not easily obtained with aerial acquisitions. Hence, before the SPOT-4 (Take 5) experiment, the existing datasets only fulfilled two of the four main features described above: • ESA provided simulated data resulting from aerial acquisitions, with the 13 spectral bands at the proper resolutions, but with a very small coverage and no repetitivity; • CNES and Centre d'Etudes Spatiales de la Biosphere (CESBIO) provided Formosat-2 satellite time series with the appropriate repetitivity at a constant viewing angle and 8-m resolution, but with a small coverage (24 × 24 km 2 ) and only four bands (and no SWIR)  [4] ; • Landsat data with adequate coverage and good spectral richness could also be used; however, the repetitivity is much lower  (16 days) , and the resolution is only 30 m; • SPOT and RapidEye imagery have the necessary resolution and may cover large sites, but do not provide repetitivity with constant angles and only have four or five bands, respectively; To cope with this problem, the SPOT-4 (Take 5) experiment was proposed by the CESBIO and implemented by CNES.
During this period, each of the 45 selected sites (see the next section for a description of the sites) was observed every fifth day, a total of 28 times. Finally, SPOT-4 was de-orbited and switched off after the end of the experiment, in June 2013. The dataset is aimed at helping users to learn to process the information brought by the unique set of Sentinel-2 features, among which the most unusual is the availability of repetitive observations under constant viewing angles and at a high spatial resolution. Development of new techniques, methods and applications is needed to take full advantage of Sentinel-2 time series, and SPOT-4 (Take 5) data can be used to test these new ideas. Two products were delivered to the users, for which we used the nomenclature defined for Sentinel-2  [1] : a Level 1C product providing ortho-rectified images expressed in top of atmosphere reflectances and a Level 2A product providing the same ortho-rectified images, but expressed in surface reflectance after atmospheric correction and provided with cloud, cloud shadow, water and snow masks.
Validation of Cloud Masks For the SPOT-4 (Take 5) experiment, we did not have the manpower to set up an independent dataset to provide a quantitative validation of cloud masks and cloud shadow masks, as done by Zhu et al. in  [18]. Our validation of the clouds and cloud shadow masks relied on a visual verification of quick-looks, as shown in Figure  5. In spite of some imperfections, even very thin clouds and their shadows are usually detected. Although the cloud masks are generally accurate and provide performances similar to those observed when applied to the Landsat 5 and 7 or Formosat-2 satellites in  [8] , a few defects have been observed, which will require some enhancements in our future works: • Some cloud classification errors have been observed when the assumption on a slow variation of surface reflectance is wrong, for instance when a wet bare soil dries up and becomes brighter and whiter.
The weather in spring 2013 was very cloudy in France, where a large number of sites were located. Some places in the east of France had 30% less sunshine duration than average, and most of the country was at least 10% below. On a few sites (Alsace, Ardeche, Bretagne, Belgium and also China (1) or equatorial sites), only 2-4 cloud-free observations were obtained out of 28 acquisitions from February-mid-June. We also noticed that the number of large images (110 × 110 km 2 ) with no cloud is very low. This fact should push users to develop methods robust to the presence of data gaps due to clouds, otherwise, if only cloud-free images are used, a large proportion of the clear observations at the pixel level will be lost. For instance, on the Belgium site, as shown in Figure  7 , only three almost cloud-free images are available, while an average of six cloud-free observations is available at the pixel level.
These sites were Arcachon, Carpentras, Seysses, Le Fauga, Palaiseau, Paris and Kyiv in Europe, Saada, Ouarzazate and Ben Salem in Africa, Wallops and Cart Site in America and Gwangjiu in Asia. The validation results are provided in Figure  9. The standard deviation of AOT estimates in stable cases is 0. 06, which is a good result considering the absence of a blue band. However, errors of 0. 1 were observed on sites for which the weather was very bad, with less than one clear image per month available and, therefore, an insufficient repetitivity. This is the case for the Ben Salem site in Tunisia, to which the dots with the largest overestimations pertain. In the case of Sentinel-2, the availability of a blue band will enable one to combine a spectral criterion to the multi-temporal one, which should increase the robustness of estimates when long data gaps are observed due to cloud cover.
The data are processed by an operational software, which calibrates the photometer, estimates the atmospheric optical properties and computes the bidirectional reflectance distribution function of the site. This is done by fitting a directional model  [22] , which allows computing the surface reflectance for any observation geometry. It is therefore possible to use the ROSAS station not only to verify the absolute calibration of sensors, but also to validate the surface reflectance. ). The blue circles correspond to ROSAS in situ measurements, while the red squares correspond to SPOT-4 (Take 5). Three of them, observed after rain events, are marked by red diamonds. Surface reflectance validation results using ROSAS are shown in Figure  10. The agreement between retrieved and in situ reflectances is better than 5% for the green band (550 nm), the red band (670 nm) and the SWIR (1650 nm), and the reflectances observed are as smooth as those observed in situ.
Cross-Comparison with MODIS Surface Reflectances An additional evaluation experiment was led at NASA and University of Maryland. It is based on the cross-comparison of SPOT-4 (Take-5) surface reflectances with the MODIS surface reflectances, considered as the reference dataset and derived from the MOD09CMG product (MODIS product nÂř9 sampled at 0. 05 degrees). The cross-comparison is structured in two processing steps: (i) averaging the SPOT-4 SR data to 0. 05 degrees; and (ii) bidirectional reflectance distribution function (BRDF) adjustments of the MODIS surface reflectance to the corresponding SPOT-4 Sun and view geometry. As MODIS and SPOT4 spectral response are not identical, an adjustment of the spectral responses would have been justified, but was not done in this study. The BRDF adjustment methodology is the same as the one used by  [23]  to cross-compare Landsat and MODIS data and relying on the BRDF approach introduced by  [24].
On the scientific side, five months of data are often not enough to allow one to obtain publishable results, but nonetheless, several articles have been published so far. A subset of the applications of the experiment is shown on this Special Issue in MDPI remote sensing: "Lessons Learned from the SPOT-4 (Take 5): Experiment in Preparation for Sentinel-2". Before this Special Issue, a few articles using time series of SPOT-4 (Take 5) images were already published regarding the estimation of water needs (  [25]  and  [26] ) or forestry  [27]. To allow the distribution of data, a new ground segment was set up at the Theia land data center to produce and distribute Level 1C and Level 2A data. The processing of Level 1C with a sub-pixel multi-temporal registration accuracy in a fully automatic way was quite a challenge, as the initial location error of SPOT-4 has a standard deviation above 20 pixels, and peak values of 75 pixels were observed; moreover, our will to process all of the images for which a few km 2 of cloud-free land were available did not ease the task.
From 2016 on, the MACCS processor will be applied to Sentinel-2 data at the Theia Land Data Center at the European scale to produce Level 2A products. The SPOT-4 (Take 5) dataset is still open to the scientific community and also to private companies, with a very open license. The data may be downloaded from https://spot-take5. org. Thanks to the success of the SPOT-4 (Take 5) experiment, CNES and ESA accepted renewing the Take 5 experiment with the SPOT-5 satellite, from April-August 2015. This new experiment is based on 150 sites and provides images with the same spatial resolution as Sentinel-2. Although data are acquired in all continents and all sorts of biomes, the time period is perfectly suited to monitor vegetation cover and, particularly, summer crops in the Northern Hemisphere. As for SPOT-4 (Take 5), L1C and L2A products will be freely available for users.