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[Methods] Dr Carlos Palacin-Lizarbe made the datasets. For further details of the methods see the parent publication: Palacin-Lizarbe, C., Camarero, L., Hallin, S., Jones, C. M. & Catalan, J. Denitrification rates in lake sediments of mountains affected by high atmospheric nitrogen deposition. Sci Rep10, 3003, doi:10.1038/s41598-020-59759-w (2020). Further details on the denitrification rate measurement method are provided in the publication: Palacin-Lizarbe, C., Camarero, L. & Catalan, J. Estimating sediment denitrification rates using cores and N2O microsensors. J Vis Exp, e58553, doi:doi:10.3791/58553 (2018). Further details on the sediment molecular descriptors (DNA, 16S, nirS, nirK, nosZ1, and nosZ2) are provided in the publication: Palacin-Lizarbe, C. et al. The DNRA-denitrification dichotomy differentiates nitrogen transformation pathways in mountain lake benthic habitats. Front Microbiol 10, 1229, doi:10.3389/fmicb.2019.01229 (2019)., [Usage Notes] Dryad repository - https://doi.org/10.5061/dryad.j6q573n95 This document describes four datasets (tab-separated text format tables) related to measurements of denitrification rates in mountain lake sediments of the Pyrenees. Dr Carlos Palacin-Lizarbe made the datasets, anyone that uses these data should reference this DRYAD repository and the parent publication: Palacin-Lizarbe, C., Camarero, L., Hallin, S., Jones, C. M. & Catalan, J. Denitrification rates in lake sediments of mountains affected by high atmospheric nitrogen deposition. Sci Rep10, 3003, doi:10.1038/s41598-020-59759-w (2020). See this publication for further details on the methods used. Dataset 1: Sediment_denitrification_rates_DRYAD.txt Dataset containing all measured denitrification rates and incubation conditions. The dataset contains the following variables: id_rate, id_core, yymmdd_lake_sample, denitrification_rate, nitrate_treatment, temperature_insitu, temperature_incubation, nitrate_actual_plus_added, glucose_added, habitat, site. Dataset 2: table_background_DRYAD_def.txt Dataset containing all measured denitrification rates and the background descriptors (landscape, water, sediment). This dataset contain missing values. The dataset contains the following variables: id_core, yymmdd_lake_sample, actual_denitrification_rate, nitrate_add_7_denitrification_rate, nitrate_add_14_denitrification_rate, nitrate_add_28_denitrification_rate, high_nitrate_add_denitrification_rate, site, latitude, longitude, altitude, lake_area, catchment_area, renewal_time, habitat, temperature_insitu, nitrate, nitrite, ammonium, sulphate, DOC, water_column_depth, sediment_layer_depth, organic_matter, nitrmicroogen, d15N, carbon, d13C, carbon_nitrogen_ratio, dry_weight_wet_weight_ratio, sediment_density, sediment_grain_size, DNA, X16S, nirS, nirK, nosZ1, nosZ2. Dataset 3: table_actual_denitrification_rates_modeled_DRYAD.txt Dataset containing the actual denitrification rates modelled in Palacin-Lizarbe et al. 2020 Sci Rep and the background descriptors (landscape, water, sediment). The dataset contains the following variables: id_rate, id_core, yymmdd_lake_sample, actual_denitrification_rate, site, latitude, longitude, altitude, lake_area, catchment_area, renewal_time, habitat, temperature_insitu, nitrate, nitrite, ammonium, sulphate, DOC, water_column_depth, sediment_layer_depth, organic_matter, nitrogen, d15N, carbon, d13C, carbon_nitrogen_ratio, dry_weight_wet_weight_ratio, sediment_density, sediment_grain_size, DNA, X16S, nirS, nirK, nosZ1, nosZ2. In the dataset the variables are not transformed. In Palacin-Lizarbe et al. 2020 Sci Rep when developing the models of actual denitrification rates, we use the variables scaled; before being scaled some variables were square root (actual_denitrification_rate, DOC, DNA, X16S, nirS, and nirK) or log10 (lake_area, catchment_area, renewal_time, nitrite, ammonium, sulphate, dry_weight_wet_weight_ratio, nosZ1, and nosZ2) transformed to reduce the influence of extreme values. Dataset 4: table_potential_denitrification_rates_modeled_DRYAD.txt Dataset containing the potential (28 microM nitrate added) denitrification rates modelled in Palacin-Lizarbe et al. 2020 Sci Rep and the background descriptors (landscape, water, sediment). The dataset contains the following variables: id_rate, id_core, yymmdd_lake_sample, nitrate_add_28_denitrification_rate, site, latitude, longitude, altitude, lake_area, catchment_area, renewal_time, habitat, temperature_insitu, nitrate, nitrite, ammonium, sulphate, DOC, water_column_depth, sediment_layer_depth, organic_matter, nitrogen, d15N, carbon, d13C, carbon_nitrogen_ratio, dry_weight_wet_weight_ratio, sediment_density, sediment_grain_size, DNA, X16S, nirS, nirK, nosZ1, nosZ2. In the dataset the variables are not transformed. In Palacin-Lizarbe et al. 2020 Sci Rep when developing the models of potential denitrification rates, we use the variables scaled; before being scaled some variables were square root (nitrate_add_28_denitrification_rate, DOC, DNA, X16S, and nirS) or log10 (lake_area, catchment_area, renewal_time, nitrite, ammonium, sulphate, dry_weight_wet_weight_ratio, nirK, nosZ1, and nosZ2) transformed to reduce the influence of extreme values. Variables contained in the datasets: id_rate: identification code for each denitrification rate measurement is useful as a link between datasets. id_core: identification code for each sediment core is useful as a link between datasets. yymmdd_lake_sample: The datasets are sorted by this variable. Identification code for each sediment core is useful as a link between datasets. Each code corresponds to the date sampled (year, month, and day), and the Lake, and the number of the sediment core. E.g. 130701Llo3 correspond the 3rd core sampled in Llong Lake on 2013, July 1 (13 07 01). The following are the sampled Lakes, with its abbreviation in brackets: Bergus (Be), Bassa de les Granotes (Bgra), Contraix (Co), Gelat de Bergus (GBe), Llebreta (Lle), Llong (Llo), Plan (Pl), Podo (Po), Redon (Re), Redo Aiguestortes (ReAT), and Redon de Vilamos (ReVil). This variable is useful as a link between datasets. denitrification_rate (micromols N2O m-2 h-1): denitrification rate without identifying the nitrate treatment. actual_denitrification_rate (micromols N2O m-2 h-1): denitrification rate measured without any substrate addition. nitrate_add_7_denitrification_rate (micromols N2O m-2 h-1): denitrification rate measured adding 7 microM nitrate and 1.5 g/L of glucose. nitrate_add_14_denitrification_rate (micromols N2O m-2 h-1): denitrification rate measured adding 14 microM nitrate and 1.5 g/L of glucose. nitrate_add_28_denitrification_rate (micromols N2O m-2 h-1): denitrification rate measured adding 28 microM nitrate and 1.5 g/L of glucose. high_nitrate_add_denitrification_rate (micromols N2O m-2 h-1): denitrification rate measured adding a high concentration of nitrate (>300 microM) and 1.5 g/L of glucose. nitrate_treatment: treatment of nitrate addition in the denitrification measurements. Treatments: actual (without any nitrate added), Add_7 (7 microM nitrate added), Add_14 (14 microM nitrate added), Add_28 (28 microM nitrate added), High_add (>300 microM nitrate added). temperature_incubation (Celsius degrees): constant temperature during the incubation of the denitrification measurement. nitrate_actual_plus_added (microM): actual (in situ) plus added nitrate concentration in the water overlying the sediment core. glucose_added (mg * L-1): added when nitrate was also added. site: Lake (code): Bergus (B), Contraix (C), Bassa de les Granotes (G), Gelat de Bergus (GB), Llebreta (Le), Llong (Lo), Plan (P), Podo (Po), Redon (R), Redo Aiguestortes (RA), and Redon de Vilamos (RV). latitude (N). longitude (E). altitude (m a.s.l.). lake_area (ha). catchment_area (ha). renewal_time (months): mean time that water spends in the lake, retention time and residence time are synonyms of renewal time. habitat: sediment type (code): littoral sediments from rocky areas (R), helophyte Carex rostrata belts (C), beds of isoetid (I) and elodeid (E) macrophytes, and non-vegetated deep (D) sediments. temperature_insitu (Celsius degrees): temperature measured in the water overlying the sediment core just after the sediment core was retrieved. nitrate (microM): nitrate concentration measured in the water overlying the sediment core after less than 4 h of the sediment core retrieval and before the denitrification incubations. nitrite (microM): nitrite concentration measured in the water overlying the sediment core after less than 4 h of the sediment core retrieval and before the denitrification incubations. ammonium (microM): ammonium concentration measured in the water overlying the sediment core after less than 4 h of the sediment core retrieval and before the denitrification incubations. sulphate (microM): sulphate concentration measured in the water overlying the sediment core after less than 4 h of the sediment core retrieval and before the denitrification incubations. DOC (mg*L-1): dissolved organic carbon concentration measured in the water overlying the sediment core after less than 4 h of the sediment core retrieval and before the denitrification incubations. water_column_depth (m): depth of the water column in the sediment core sampling point. Coded as 0.5 for littoral habitats. sediment_layer_depth (cm): depth of the sediment layer described by molecular and abiotic features. organic_matter (%): sediment organic matter content determined by Lost on Ignition. nitrogen (%): sediment nitrogen content. d15N (parts per thousand): sediment delta15N. carbon (%): sediment carbon content. d13C (parts per thousand): sediment delta13C. carbon_nitrogen_ratio (a/a): sediment atomic ratio of the two elements. dry_weight_wet_weight_ratio: sediment dry weight to wet weight ratio. sediment_density (g * cm-3). sediment_grain_size (um): sediment, volume diameter of the mean-sized spherical particle (vol_weighted_mean parameter provided by Mastersizer 2000, Malvern Instruments Ltd, UK). DNA (ng * m-2): sediment DNA content, ng of DNA per m2 in the sediment layer (0-0.5 cm). X16S (copies * m-2): 16S rRNA gene copies per m2 in the sediment layer (0-0.5 cm). nirS (copies * m-2) : nirS gene copies per m2 in the sediment layer (0-0.5 cm). nirK (copies * m-2) : nirK gene copies per m2 in the sediment layer (0-0.5 cm). nosZ1 (copies * m-2) : nosZ1 gene copies per m2 in the sediment layer (0-0.5 cm). nosZ2 (copies * m-2) : nosZ2 gene copies per m2 in the sediment layer (0-0.5 cm)., During the last decades, atmospheric nitrogen loading in mountain ranges of the Northern Hemisphere has increased substantially, resulting in high nitrate concentrations in many lakes. Yet, how increased nitrogen has affected denitrification, a key process for nitrogen removal, is poorly understood. We measured actual and potential (nitrate and carbon amended) denitrification rates in sediments of several lake types and habitats in the Pyrenees during the ice-free season. Actual denitrification rates ranged from 0 to 9 μmol N2O m−2 h−1 (mean, 1.5 ± 1.6 SD), whereas potential rates were about 10-times higher. The highest actual rates occurred in warmer sediments with more nitrate available in the overlying water. Consequently, littoral habitats showed, on average, 3-fold higher rates than the deep zone. The highest denitrification potentials were found in more productive lakes located at relatively low altitude and small catchments, with warmer sediments, high relative abundance of denitrification nitrite reductase genes, and sulphate-rich waters. We conclude that increased nitrogen deposition has resulted in elevated denitrification rates, but not sufficiently to compensate for the atmospheric nitrogen loading in most of the highly oligotrophic lakes. However, there is potential for high rates, especially in the more productive lakes and landscape features largely govern this., Ministerio de Ciencia e Innovación, Gobierno de España, Award: CGL2010-19373. Ministerio de Economía, Industria y Competitividad, Gobierno de España, Award: CGL2016–80124-C2-1-P., Peer reviewed

[UPDATE to version 2 of this upload] Also the raw (no data cleaning applied to it) occurrence dataset as extracted from EFSA DWH is provided in csv format. This dataset is compliant with EFSA SSD model and contains two additional columns documenting issues identified in the cleaning process (column: issue) and the action taken (column: action) to address the issue (e.g. delete record or update values in specific fields). [Description - Version 1] Annex: Tables on GAs on occurrence data in food and feed, and dietary exposure assessment for humans Table A.1. Dietary surveys used for the estimation of acute dietary exposure to GA Table A.2. Number of results and samples per food category submitted to EFSA through the continuous call for data Table A.3. Analytical results excluded from the final dataset used to estimate dietary exposure and the criteria applied for exclusion Table A.4. Occurrence of alpha-chaconine and alpha-solanine (UB mg/kg) in the samples included in the final dataset (left censored results highlighted in yellow) Table A.5. European Starch Association data on feed and potatoes for starch Table A.6. Details acute assessment across surveys (consumption days only) Table A.7. Comparison of exposure summary results obtained using the uniform vs the normal distribution for reduction factors, Peer reviewed

Contains the raw (no data cleaning applied to it) occurrence dataset on nickel as extracted from EFSA DWH on 7 February 2020 in food samples presented in the opinion as described in its section 3.2.1. The data is provided in csv format. This dataset is compliant with EFSA SSD model and contains two additional columns documenting issues identified in the cleaning process (column: issue) and the action taken (column: action) to address the issue (e.g. delete record or update values in specific fields)., Peer reviewed

Annex A – Benchmark dose analysis The Annex is provided as a separate pdf file containing the detailed results of the benchmark dose analyses from which no reference point was selected. Annex B – Dietary surveys per country and age group available in the EFSA Comprehensive Database, considered in the exposure assessment The Annex is provided as a separate Excel file containing the dietary surveys per country and age group. Annex C – Occurrence data on nickel in food and drinking water The Annex is provided as a separate Excel file containing summary statistics on occurrence data on nickel. Annex D – Chronic and acute dietary exposure to nickel and the contribution of different food groups to the dietary exposure The Annex is provided as a separate Excel file containing the chronic and acute dietary exposure to nickel per survey and the contribution of different food groups to the dietary exposure., Peer reviewed

Annex: Summary statistics on occurrence and consumption data and exposure assessment results Table 1 Number of analytical results excluded from the initial dataset during data cleaning, and justification for exclusion Table 2 Occurrence values of OTA (µg/kg) in food as reported in the cleaned database Table 3 Occurrence values of OTA (µg/kg) in food as used for the exposure assessment Table 4 Dietary surveys per country and age group available in the EFSA Comprehensive Database, considered in the exposure assessment Table 5 Results of chronic dietary exposure assessment on OTA (ng/kg bw per day) Table 6 Main contributing food categories to the mean LB exposure assessments to OTA across European dietary surveys and population groups Table 7 Distribution of LOQ values among the different food categories Table 8 All contributing food categories to the mean LB and UB exposure to OTA across European dietary surveys and population groups FormattedDATA_OchratoxinA.zip: Occurrence data on OTA Contains the occurrence data of OTA on 73,891 food samples presented in the opinion as described in its section 4.7 Occurrence data., Peer reviewed

Annex_III_occurrence data.xlsx This Annex is an excel file presenting tables on occurrence data on nitrate and nitrite in feed. Nitrate Nitrite_OCC_ZENODO.CSV Contains the raw (no data cleaning applied to it) occurrence dataset on nitrate and nitrite as extracted from EFSA DWH on 03 December 2019 in feed samples presented in the opinion as described in its section 3.2.2. The data is provided in csv format. This dataset is compliant with EFSA SSD model and contains two additional columns documenting issues identified in the cleaning process (column: issue) and the action taken (column: action) to address the issue (e.g. delete record or update values in specific fields)., Peer reviewed

The annexes A to E to the Scientific Opinion on Aflatoxins in Food included in the upload are excel files as follows: Annex A: Dietary surveys per country and age group available in the EFSA Comprehensive Database, considered in the exposure assessment Annex B: Occurrence data on aflatoxins Annex C: Proportion of left-censored data and the mean concentrations of the quantified analytical results of AFB1 for pistachios, hazelnuts, peanuts, other nuts and dried figs Annex D: AFB1 and AFM1 concentrations reported for organic farming and conventional farming in selected food categories Annex E: Mean and high chronic dietary exposure to aflatoxins per survey and the contribution of different food groups to the dietary exposure, Peer reviewed

This Annex is an excel file which presents tables on chlorinated paraffins on occurrence data in food and dietary exposure assessment for humans., Peer reviewed

Annexes to the Risk to human health related to the presence of perfluoroalkyl substances in food - available at: https://www.efsa.europa.eu/en/efsajournal/pub/6223 Annex A - Occurrence and exposure data Annex B - Distribution of analytical results Annex C - Comparison of PFOA and PFOS occurrence and exposure data with previous assessment (EFSA CONTAM Panel, 2018). Also the raw (no data cleaning applied to it) occurrence dataset as extracted from EFSA DWH is provided in csv format. This dataset is compliant with EFSA SSD model and contains two additional columns documenting issues identified in the cleaning process (column: issue) and the action taken (column: action) to address the issue (e.g. delete record or update values in specific fields)., Peer reviewed

El Observatorio Limnológico de los Pirineos (LOOP) tiene entre sus objetivos hacer difusión de los resultados de la investigación en lagos de montaña de sus miembros. Dentro de esta actividad está el servicio a bases de datos internacionales. Esta contribución a GBIF incluye información sobre especies de diatomeas encontradas a lo largo de una secuencia sedimentaria en el lago Redon, que cubre todo el Holoceno. Más detalles sobre la naturaleza del registro se pueden encontrar en Catalan, J., S. Pla, J. García and L. Camarero. 2009. Climate and CO2 saturation in an alpine lake throughout the Holocene. Limnology and Oceanography, 54(6, part 2): 2542–2552. Note: this dataset was previously orphaned. It has been rescued by ① extracting it from the GBIF.org index (see GBIF Download in External Data) and ② republishing it on this IPT data hosting centre as version 1.0.