This climate change impact data (future scenarios on temperature-induced GDP losses) and climate change mitigation cost data (REMIND model scenarios) is published under doi: 10.5281/zenodo.3541809 and used in this paper:

Ueckerdt F, Frieler K, Lange S, Wenz L, Luderer G, Levermann A (2018) The economically optimal warming limit of the planet. Earth System Dynamics. https://doi.org/10.5194/esd-10-741-2019

Below the individual file contents are explained. For further questions feel free to write to Falko Ueckerdt (ueckerdt@pik-potsdam.de).

Climate change impact data

File 1: Data_rel-GDPpercapita-changes_withCC_per-country_all-RCP_all-SSP_4GCM.csv

Content: Data of relative change in absolute GDP/CAP levels (compared to the baseline path of the respective SSP in the SSP database) for each country, RCP (and a zero-emissions scenario), SSP and 4 GCMs (spanning a broad range of climate sensitivity). Negative (positive) values indicate losses (gains) due to climate change. For figure 1a of the paper, this data was aggregated for all countries.

File 2: Data_rel-GDPpercapita-changes_withCC_per-country_all-SSP_4GCM_interpolated-for-REMIND-scenarios.csv

Content: Data of relative change in absolute GDP/CAP levels (compared to the baseline path of the respective SSP in the SSP database) for each country, SSP and 4 GCMs (spanning a broad range of climate sensitivity). The RCP (and a zero-emissions scenario) are interpolated to the temperature pathways of the ten REMIND model scenarios used for climate change mitigation costs. Hereby the set of scenarios for climate impacts and climate change mitigation are consistent and can be combined to total costs of climate change (for a broad range of mitigation action).

File 3: Data_rel-GDPpercapita-changes_withCC_per-country_SSP2_12GCM_interpolated-for-REMIND-scenarios.csv

Content: Same as file 2, but only for the SSP2 (chosen default scenario for the study) and for all 12 GCMs. Data of relative change in absolute GDP/CAP levels (compared to the baseline path of the respective SSP in the SSP database) for each country, SSP-2 and 12 GCMs (spanning a broad range of climate sensitivity). The RCP (and a zero-emissions scenario) are interpolated to the temperature pathways of the ten REMIND model scenarios used for climate change mitigation costs. Hereby the set of scenarios for climate impacts and climate change mitigation are consistent and can be combined to total costs of climate change (for a broad range of mitigation action).

In addition, reference GDP and population data (without climate change) for each country until 2100 was downloaded from the SSP database, release Version 1.0 (March 2013, https://tntcat.iiasa.ac.at/SspDb/, last accessed 15Nov 2019).

Climate change mitigation cost data

The scenario design and runs used in this paper have first been conducted in [1] and later also used in [2].

File 4: REMIND_scenario_results_economic_data.csv

File 5: REMIND_scenarios_climate_data.csv

Content: A broad range of climate change mitigation scenarios of the REMIND model. File 4 contains the economic data of e.g. GDP and macro-economic consumption for each of the countries and world regions, as well as GHG emissions from various economic sectors. File 5 contains the global climate-related data, e.g. forcing, concentration, temperature.

In the scenario description “FFrunxxx” (column 2), the code “xxx” specifies the scenario as follows. See [1] for a detailed discussion of the scenarios.

The first dimension specifies the climate policy regime (delayed action, baseline scenarios):

1xx: climate action from 2010
5xx: climate action from 2015
2xx climate action from 2020 (used in this study)
3xx climate action from 2030
4x1 weak policy baseline (before Paris agreement)

The second dimension specifies the technology portfolio and assumptions:

x1x Full technology portfolio (used in this study)
x2x noCCS: unavailability of CCS
x3x lowEI: lower energy intensity, with final energy demand per economic output decreasing faster than historically observed
x4x NucPO: phase out of investments into nuclear energy
x5x Limited SW: penetration of solar and wind power limited
x6x Limited Bio: reduced bioenergy potential p.a. (100 EJ compared to 300 EJ in all other cases)
x6x noBECCS: unavailability of CCS in combination with bioenergy

The third dimension specifies the climate change mitigation ambition level, i.e. the height of a global CO2 tax in 2020 (which increases with 5% p.a.).

xx1 0$/tCO2 (baseline)
xx2 10$/tCO2
xx3 30$/tCO2
xx4 50$/tCO2
xx5 100$/tCO2
xx6 200$/tCO2
xx7 500$/tCO2
xx8 40$/tCO2
xx9 20$/tCO2
xx0 5$/tCO2

For figure 1b of the paper, this data was aggregated for all countries and regions. Relative changes of GDP are calculated relative to the baseline (4x1 with zero carbon price).

[1] Luderer, G., Pietzcker, R. C., Bertram, C., Kriegler, E., Meinshausen, M. and Edenhofer, O.: Economic mitigation challenges: how further delay closes the door for achieving climate targets, Environmental Research Letters, 8(3), 034033, doi:10.1088/1748-9326/8/3/034033, 2013a.

[2] Rogelj, J., Luderer, G., Pietzcker, R. C., Kriegler, E., Schaeffer, M., Krey, V. and Riahi, K.: Energy system transformations for limiting end-of-century warming to below 1.5 °C, Nature Climate Change, 5(6), 519–527, doi:10.1038/nclimate2572, 2015.

This preliminary dataset contains the application/vnd.zenodo.v1+json JSON records of Zenodo deposits as retrieved on 2019-09-16.

Files

• zenodo-records-json-2019-09-16.tar.xz Zenodo JSON records
  XZ-compressed tar archive of individual JSON records as retrieved from Zenodo. Filenames reflects record, e.g. 1310621.json was retrieved from https://zenodo.org/api/records/1310621 using content-negotiation for application/vnd.zenodo.v1+json
• zenodo-records-json-2019-09-16-filtered.jsonseq.xz Concatinated Zenodo JSON records
  XZ-compressed RFC7464 JSON Sequence stream, readable by jq. Concatination of Zenodo JSON records. Order not significant.
• zenodo-records.sh Retrieve Zenodo JSON records
  A retrospectively created Bash shell script that shows the commands used to retrieve JSON files and concationate to jsonseq.
• ro-crate-metadata.jsonld RO-Crate 0.2 structured metadata
• ro-crate-preview.html Browser rendering of RO-Crate structured metadata
• README.md This dataset description

License

This dataset is provided under the license Apache License, version 2.0:

Copyright 2019 The University of Manchester

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

CC0 for Zenodo metadata

The Zenodo metadata in zenodo-records-json-2019-09-16.tar.xz is reused under the terms of https://creativecommons.org/publicdomain/zero/1.0/

Reproducibility

To retrieve the Zenodo JSON it was deemed necessary to use the undocumented parts of Zenodo API.

From the Zenodo source code it was identified that the REST template https://zenodo.org/api/records/{pid_value} could be used with pid_value as the numeric part from the OAI-PMH identifier, e.g. for oai:zenodo.org:1310621 the Zenodo JSON can be retrieved at https://zenodo.org/api/records/1310621.

The JSON API supports content negotiation, the content-types supported as of 2019-09-20 include:

• application/vnd.zenodo.v1+json giving the Zenodo record in Zenodo's internal JSON schema (v1)
• application/ld+json giving JSON-LD Linked Data using the http://schema.org/ vocabulary
• application/x-datacite-v41+xml giving DataCite v4 XML
• application/marcxml+xml giving MARC 21 XML

Using these (currently) undocumented parts of the Zenodo API thus avoids the need for HTML scraping while also giving individual complete records that are suitable to redistribute as records in a filtered dataset.

This preliminary exploration will be adapted into the reproducible CWL workflow, for now included as a Bash script zenodo-records.sh

Execution time was about 3 days from a server at the University of Manchester network on a single 1 GBps network link. The script does:

• Retrieve each of the first 3.5 million Zenodo records
  as Zenodo JSON by iterating over possible numeric IDs (the maximum ID 3450000 was estimated from "Recent uploads")
• Filter list to exclude records that are not found, moved or deleted. The presence of the key conceptrecid is used as marker.
• Use jq to ensure the JSON is on a single line
• Join the JSON files using the ASCII Record Separator (RS, 0x1e) to make a application/json-seq JSON text sequence stream
• Save the JSON stream as a single compressed file using xz

This data set includes global climate change mitigation scenarios as summarized by Riahi et al., 2021. The scenarios are developed as part of the ENGAGE project and were assessed in terms of the their investment implications (Bertram et al., 2021), their land-use dynamics (Hasegawa et al., 2021) as we all as with respect to their costs and benefits (Drouret et al., 2021). The scenarios include a current national policies scenario and an NDC scenario that depict relevant near-term GHG emission tends and targets. In the long-term, two types of CO₂ emission budgets are implemented, so called “net-zero budgets” and “end-of-century” budgets. The “net-zero-budget” scenarios assume climate policies that limit the remaining cumulative CO₂ emissions until net zero CO₂ emissions are reached. These scenarios limit the temperature overshoot and do not rely on global net-negative CO₂ emissions to keep warming below the intended temperature limit. In contrast, the “end-of-century budget” scenarios assume long-term climate policies that limit cumulative CO₂ emissions over the full course of the 21^st century. Depending on the availability of carbon dioxide removal options, these scenarios may comprise high temperature overshoot and global net negative CO₂ emissions in the second half of the century. The near-term dimension of current national policies until 2020 or NDCs until 2030 is then combined with reaching the net-zero and full-century CO₂ emissions budgets. To cover a relevant range of temperature outcomes (which in addition to the budgets themselves also determined by mitigation of non-CO₂ GHG and aerosol emissions), the budgets are varied between 200 and 3000 GtCO₂ in steps of 50 – 500 GtCO₂.

The data is available for download at the ENGAGE Scenario Explorer. The license permits use of the scenario ensemble for scientific research and science communication, but restricts redistribution of substantial parts of the data. Please refer to the FAQ and legal code for more information.

This dataset contains the analytical results of pesticide residues measured in the food products analysed by the national competent authorities. Pesticide residues resulting from the use of plant protection products on crops that are used for food or feed production may pose a risk factor for public health. For this reason, a comprehensive legislative framework has been established in the European Union (EU), which defines rules for the approval of active substances used in plant protection products, the use of plant protection products and for pesticide residues in food. In order to ensure a high level of consumer protection, legal limits, so called “maximum residue levels” or briefly “MRLs”, are established in Regulation (EC) No 396/2005. EU-harmonised MRLs are set for all pesticides covering all types of food products. A default MRL of 0.01 mg/kg is applicable for pesticides not explicitly mentioned in the MRL legislation. Regulation (EC) No 396/2005 imposes on Member States the obligation to carry out controls to ensure that food placed on the market is compliant with the legal limits.

A sample is considered free of quantifiable residues if the analytes were not present in concentrations at or above the limit of quantification (LOQ). The LOQ is the smallest concentration of an analyte that can be quantified with the analytical method used to analyse the sample. It is commonly defined as the minimum concentration of the analyte in the test sample that can be determined with acceptable precision and accuracy.

If a sample contains quantifiable residues but within the legally permitted limit (maximum residue level, MRL), it is described as a sample with quantified residue levels within the legal limits (below or at the MRL)

A sample is considered non-compliant with the legal limit (MRL), if the measured residue concentrations clearly exceed the legal limits, taking into account the measurement uncertainty. It is current practice that the uncertainty of the analytical measurement is taken into account before legal or administrative sanctions are imposed on food business operators for infringement of the MRL legislation.

REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION:

MOPER_2022 - Netherlands Food and Consumer Product Safety Authority

MOPER_2021 - Netherlands Food and Consumer Product Safety Authority

MOPER_2020 - Netherlands Food and Consumer Product Safety Authority

MOPER_2019 - Netherlands Food and Consumer Product Safety Authority

MOPER_2018 - Netherlands Food and Consumer Product Safety Authority

MOPER_2017 - Netherlands Food and Consumer Product Safety Authority

MOPER_2016 - Netherlands Food and Consumer Product Safety Authority

MOPER_2015 - Netherlands Food and Consumer Product Safety Authority

MOPER_2014 - Netherlands Food and Consumer Product Safety Authority

MOPER_2013 - Netherlands Food and Consumer Product Safety Authority

MOPER_2012 - Netherlands Food and Consumer Product Safety Authority

MOPER_2011 - Netherlands Food and Consumer Product Safety Authority

We are seeking feedback on our open data please complete the survey at the link below:https://ec.europa.eu/eusurvey/runner/9344dfa0-f384-cb72-65f6-6c187a6d0f14

This dataset contains speech from Finnish parliament 2008-2020 plenary sessions, segmented and aligned for speech recognition training. In total, the training set has:

• 1.4 million samples
• 3100 hours of audio
• 460 speakers
• over 19 million word tokens

Additionally, the upload contains 5h long development and 5h long evaluation sets described in publication 10.21437/Interspeech.2017-1115. Due to the size of the training set (~300 GB) and Zenodo upload limit (50 GB), only the development and evaluation sets are published on Zenodo. Rest of the data is available at: http://urn.fi/urn:nbn:fi:lb-2021051903

The training set comes in two parts:

1. 2008-2016 set which is originally described in publication 10.21437/Interspeech.2017-1115. This set includes a list of samples from sessions in 2008-2014 that can be combined with the 2015-2020 set to form the 3100 hour training set.
2. A new 2015-2020 dataset.

All audio samples are single-channel, 16 kHz and 16-bit wav files. Each wav file has corresponding transcript in a .trn text file. The data is machine-extracted so there still remains small inaccuracies in the training set transcripts and possibly few Swedish samples. Development and evaluation sets have been corrected by hand.

The licenses can be viewed at:

• http://urn.fi/urn:nbn:fi:lb-2019112822 (audio)
• http://urn.fi/urn:nbn:fi:lb-2019112823 (text)

The code used in extraction is available at:

• https://github.com/aalto-speech/finnish-parliament-scripts (2008-2014, dev and eval sets)
• https://github.com/aalto-speech/fi-parliament-tools (2015-2020 set)

Quick Summary:

The ASCII files herein are a dataset of spacecraft electric potential for the Wind spacecraft between January 1, 2005 and January 1, 2022. The data is thoroughly described in the publication "Spacecraft floating potential measurements for the Wind spacecraft," The Astrophysical Journal Supplement Series.

Wind Spacecraft:

The Wind spacecraft (https://wind.nasa.gov and https://doi.org/10.1029/2020RG000714) was launched on November 1, 1994 and currently is in a halo orbit about the first Sun-Earth Lagrange point. It holds a suite of instruments from gamma ray detectors to quasi-static magnetic field instruments, Bo. The instruments used in this study and these datasets are the fluxgate magnetometer (MFI), the radio receivers (WAVES), ion Faraday cups (SWE), and the electron and ion electrostatic analyzers (3DP). The MFI measures 3-vector Bo at ~11 samples per second (sps); the SWE measures reduced velocity distribution functions (VDFs) of the thermal proton and alpha-particle populations from which velocity moments are derived and used herein; WAVES observes electromagnetic radiation from ~4 kHz to >12 MHz which provides an observation of the upper hybrid line (also called the plasma line) used to define the total electron density; and 3DP observes full 4π steradian VDFs of electrons and ions from a few eV to ~30 keV which provide both ion velocity moments and the electron VDFs modeled herein.

Brief Method Description:

The spacecraft potential, (\phi_{sc}), was found using four methods. Three of these methods return a range of values while the fourth returns a single value. The methods rely on examining the shape of the electron energy distribution function (EDF), f(E) versus energy, E, for three different pitch-angles (parallel, perpendicular, and anti-parallel with respect to the quasi-static magnetic field, Bo). The instrument has a physical lower energy threshold, Emin, below which no data are measured. We impose an upper energy threshold, Emax, allowed when searching for (\phi_{sc}) based on empirical evidence. The methods are as follows:

Method 1: find the range of energies where d2f/dE2 > 0, also referred to as the positive curvature region; Method 2: find the range of energies where df/dE transitions from negative to positive, i.e., the local minimum point of f(E); Method 3: find the range of energies bounding the minimum and maximum values of d2f/dE2, i.e., region of minimum to maximum curvature; and Method 4: find the local minimum between Emin and Emax

There are some additional constraints imposed in the software, available at https://github.com/lynnbwilsoniii/wind_3dp_pros (https://doi.org/10.5281/zenodo.6141586). We found four basic shapes for the EDFs (see paper for example figures), two (i.e., Types A and B) of which satisfy Emin < (\phi_{sc}) and thus are good. The other two shapes (i.e., Types C1 and C2) satisfy Emin > (\phi_{sc}), and thus we cannot determine (\phi_{sc}) from the EDF. We can only know that it has an upper bound of Emin. All Type A EDFs are given a quality flag (QF) of 4 (i.e., the best), all Type Bs are given a QF of 2 (i.e., still okay and useable), and all Type Cs are given a QF of 0 (i.e., do not use these).

ASCII File Description:

Each ASCII file contains one year of data. There is summary information contained in the header of each file. The first two columns are the start and end times (UTC) of the EDF (format 'YYYY-MM-DD/hh:mm:ss.xxx'). After the times, Methods 1-3 have six columns and Method 4 has three columns. The first(second) three columns for Methods 1-3 correspond to the lower(upper) bound on the range of (\phi_{sc}) [eV] solutions. Method 4 only has one set of three-column solutions. Each three-column set corresponds to the parallel, perpendicular, and anti-parallel pitch-angle solutions. All of these in total comprise 21 columns. The (\phi_{sc}) solutions are followed by a column for Emin [eV] and Emax [eV]. The last two columns are the EDF label or type (i.e., A, B, C1, or C2) and the quality flag (i.e., 4, 2, or 0).

Note that NaNs have been replaced with -1030 fill values

Usage Notes:
This is the updated LCSPP dataset (v3.2), generated using the LCREF-AVHRR record from 1982–2023. Due to Zenodo’s size constraints, LCSPP-AVHRR is divided into two separate repositories. Previously referred to as "LCSIF," the dataset was renamed to emphasize its role as a SIF-informed long-term photosynthesis proxy derived from surface reflectance and to avoid confusion with directly measured SIF signals.

Key updates in version 3.2 include:

• Improved Calibration: Enhanced consistency in calibration methods, addressing technical limitations in version 3.1 including applying more stringent quality filtering and snow masks.
• Quality Flags: New quality flag layer enables users to identify whether a pixel is derived from observed surface reflectance (QA=0), high-quality gap-filled values (QA=1), lower-quality gap-filled based on the mean seasonal cycle (QA=2), or missing entirely (QA=3). We advice the user to rely only on observed and high-quality gap-filled values for their analyses.
• Extension to include observations from the year of 2023.

Other LCSPP repositories can be accessed via the following links:

• LCSPP-AVHRR v3.2 (1982-2000): 10.5281/zenodo.7916850
• LCSPP-MODIS v3.2(2001-2023): 10.5281/zenodo.11658088

The user can choose between LCSPP-AVHRR and LCSPP-MODIS for the overlapping period from 2001-2023. The two datasets are generally consistent during this overlapping period, although LCSPP-MODIS shows a stronger greening trend between 2001-2023. For studies exploring the long-term vegetation dynamics, the user can either use only LCSPP-AVHRR or use a blend dataset of LCSPP-AVHRR and LCSPP-MODIS as a sensitivity test.

In addition, the updated long-term continuous reflectance datasets (LCREF), used for the production of LCSPP, can be accessed using the following links:

• LCREF-AVHRR v3.2 (1982-2023): 10.5281/zenodo.11905959
• LCREF-MODIS v3.2 (2001-2023): 10.5281/zenodo.11657458

A manuscript describing the technical details is available at https://arxiv.org/abs/2311.14987, while detailed the uses and limitations of the dataset. In particular, we note that LCSPP is a reconstruction of SIF-informed photosynthesis proxy and should not be treated as SIF measurements. Although LCSPP has demonstrated skill in tracking the dynamics of GPP and PAR absorbed by canopy chlorophyll (APARchl), it is not suitable for estimating fluorescence quantum yield.

All data outputs from this study are available at 0.05° spatial resolution and biweekly temporal resolution in NetCDF format. Each month is divided into two files, with the first file “a” representative of the 1^st day to the 15^th day of a month, and the second file “b” representative of the 16^th day to the last day of a month.

Abstract:

Satellite-observed solar-induced chlorophyll fluorescence (SIF) is a powerful proxy for the photosynthetic characteristics of terrestrial ecosystems. Direct SIF observations are primarily limited to the recent decade, impeding their application in detecting long-term dynamics of ecosystem function. In this study, we leverage two surface reflectance bands available both from Advanced Very High-Resolution Radiometer (AVHRR, 1982-2023) and MODerate-resolution Imaging Spectroradiometer (MODIS, 2001-2023). Importantly, we calibrate and orbit-correct the AVHRR bands against their MODIS counterparts during their overlapping period. Using the long-term bias-corrected reflectance data from AVHRR and MODIS, a neural network is trained to produce a Long-term Continuous SIF-informed Photosynthesis Proxy (LCSPP) by emulating Orbiting Carbon Observatory-2 SIF, mapping it globally over the 1982-2023 period. Compared with previous SIF-informed photosynthesis proxies, LCSPP has similar skill but can be advantageously extended to the AVHRR period. Further comparison with three widely used vegetation indices (NDVI, kNDVI, NIRv) shows a higher or comparable correlation of LCSPP with satellite SIF and site-level GPP estimates across vegetation types, ensuring a greater capacity for representing long-term photosynthetic activity.

Dataset accompanying the paper submitted to F1000 entitled The Varying Openness of Digital Open Science Tools.

Abstract of paper

Digital tools that support Open Science practices play a key role in the seamless accumulation, archiving and dissemination of scholarly data, outcomes and conclusions. Despite their integration into Open Science practices, the providence and design of these digital tools are rarely explicitly scrutinized. This means that influential factors, such as the funding models of the parent organizations, their geographic location, and the dependency on digital infrastructures are rarely considered. Suggestions from literature and anecdotal evidence already draw attention to the impact of these factors, and raise the question of whether the Open Science ecosystem can realise the aspiration to become a truly “unlimited digital commons” in its current structure.

In an online research approach, we compiled and analysed the geolocation, terms and conditions as well as funding models of 242 digital tools increasingly being used by researchers in various disciplines. Our findings indicate that design decisions and restrictions are biased towards researchers in North American and European scholarly communities. In order to make the future Open Science ecosystem inclusive and operable for researchers in all world regions including Africa, Latin America, Asia and Oceania, those should be actively included in design decision processes.

Digital Open Science Tools carry the promise of enabling collaboration across disciplines, world regions and language groups through responsive design. We therefore encourage long term funding mechanisms and ethnically as well as culturally inclusive approaches serving local prerequisites and conditions to tool design and construction allowing a globally connected digital research infrastructure to evolve in a regionally balanced manner.

Additional data for the gene zgc::64022, which did not fit the file size limit of the main data set located at the following address: https://doi.org/10.5281/zenodo.5268683

Data suppoting the information and figures presented in the paper "Stretching the limits of refractometric sensing in water by Whispering-gallery-modes resonators"

The SeasFire Cube is a scientific datacube for seasonal fire forecasting around the globe. Apart from seasonal fire forecasting, which is the aim of the SeasFire project, the datacube can be used for several other tasks. For example, it can be used to model teleconnections and memory effects in the earth system. Additionally, it can be used to model emissions from wildfires and the evolution of wildfire regimes.

It has been created in the context of the SeasFire project, which deals with "Earth System Deep Learning for Seasonal Fire Forecasting" and is funded by the European Space Agency (ESA) in the context of ESA Future EO-1 Science for Society Call.

It contains 21 years of data (2001-2021) in an 8-days time resolution and 0.25 degrees grid resolution. It has a diverse range of seasonal fire drivers. It expands from atmospheric and climatological ones to vegetation variables, socioeconomic and the target variables related to wildfires such as burned areas, fire radiative power, and wildfire-related CO2 emissions.

Datacube properties

Feature

Value

Spatial Coverage

Global

Temporal Coverage

2001 to 2021

Spatial Resolution

0.25 deg x 0.25 deg

Temporal Resolution

8 days

Number of Variables

54

Tutorial Link

https://github.com/SeasFire/seasfire-datacube

    Full name
    DataArray name
    Unit
    Contact *




    Dataset: ERA5 Meteo Reanalysis Data





    Mean sea level pressure
    mslp
    Pa
    NOA


    Total precipitation
    tp
    m
    MPI


    Relative humidity
    rel_hum
    %
    MPI


    Vapor Pressure Deficit
    vpd
    hPa
    MPI


    Sea Surface Temperature
    sst
    K
    MPI


    Skin temperature
    skt
    K
    MPI


    Wind speed at 10 meters
    ws10
    m*s-2
    MPI


    Temperature at 2 meters - Mean
    t2m_mean
    K
    MPI


    Temperature at 2 meters - Min
    t2m_min
    K
    MPI


    Temperature at 2 meters - Max
    t2m_max
    K
    MPI


    Surface net solar radiation
    ssr
    MJ m-2
    MPI


    Surface solar radiation downwards
    ssrd
    MJ m-2
    MPI


    Volumetric soil water level 1
    swvl1
    m3/m3
    MPI







          Volumetric soil water level 2




    swvl2
    m3/m3
    MPI


    Volumetric soil water level 3
    swvl3
    m3/m3
    MPI


    Volumetric soil water level 4
    swvl4
    m3/m3
    MPI


    Land-Sea mask
    lsm
    0-1
    NOA


    Dataset: Copernicus

CEMS

    Drought Code Maximum
    drought_code_max
    unitless
    NOA


    Drought Code Average
    drought_code_mean
    unitless
    NOA


    Fire Weather Index Maximum
    fwi_max
    unitless
    NOA


    Fire Weather Index Average
    fwi_mean
    unitless
    NOA


    Dataset: CAMS: Global Fire Assimilation System (GFAS)





    Carbon dioxide emissions from wildfires
    cams_co2fire
    kg/m²
    NOA


    Fire radiative power
    cams_frpfire
    W/m²
    NOA


    Dataset: FireCCI - European Space Agency’s Climate Change Initiative





    Burned Areas from Fire Climate Change Initiative (FCCI)
    fcci_ba
    ha
    NOA


    Valid mask of FCCI burned areas
    fcci_ba_valid_mask
    0-1
    NOA



    Fraction of burnable area
    fcci_fraction_of_burnable_area
    %
    NOA


    Number of patches
    fcci_number_of_patches
    N
    NOA


    Fraction of observed area
    fcci_fraction_of_observed_area
    %
    NOA


    Dataset: Nasa MODIS MOD11C1, MOD13C1, MCD15A2





    Land Surface temperature at day
    lst_day
    K
    MPI


    Leaf Area Index
    lai
    m²/m²
    MPI


    Normalized Difference Vegetation Index
    ndvi
    unitless
    MPI


    Dataset: Nasa SEDAC Gridded Population of the World (GPW), v4





    Population density
    pop_dens
    persons per square kilometers
    NOA


    Dataset: Global Fire Emissions Database (GFED)





    Burned Areas from GFED (large fires only)
    gfed_ba
    hectares (ha)
    MPI


    Valid mask of GFED burned areas
    gfed_ba_valid_mask
    0-1
    NOA


    GFED basis regions
    gfed_region
    N
    NOA


    Dataset: Global Wildfire Information System (GWIS)





    Burned Areas from GWIS
    gwis_ba
    ha
    NOA


    Valid mask of GWIS burned areas
    gwis_ba_valid_mask
    0-1
    NOA


    Dataset: NOAA Climate Indices





    Arctic Oscillation Index
    oci_ao
    unitless
    NOA


    Western Pacific Index
    oci_wp
    unitless
    NOA


    Pacific North American Index
    oci_pna
    unitless
    NOA


    North Atlantic Oscillation
    oci_nao
    unitless
    NOA


    Southern Oscillation Index
    oci_soi
    unitless
    NOA


    Global Mean Land/Ocean Temperature
    oci_gmsst
    unitless
    NOA


    Pacific Decadal Oscillation
    oci_pdo
    unitless
    NOA


    Eastern Asia/Western Russia
    oci_ea
    unitless
    NOA


    East Pacific/North Pacific Oscillation
    oci_epo
    unitless
    NOA


    Nino 3.4 Anomaly
    oci_nino_34_anom
    unitless
    NOA


    Bivariate ENSO Timeseries
    oci_censo
    unitless
    NOA


    Dataset: ESA CCI





    Land Cover Class 0 - No data
    lccs_class_0
    %
    NOA


    Land Cover Class 1 - Agriculture
    lccs_class_1
    %
    NOA


    Land Cover Class 2 - Forest
    lccs_class_2
    %
    NOA


    Land Cover Class 3 - Grassland
    lccs_class_3
    %
    NOA


    Land Cover Class 4 - Wetlands
    lccs_class_4
    %
    NOA


    Land Cover Class 5 - Settlement
    lccs_class_5
    %
    NOA


    Land Cover Class 6 - Shrubland
    lccs_class_6
    %
    NOA


    Land Cover Class 7 - Sparse vegetation, bare areas, permanent snow and ice
    lccs_class_7
    %
    NOA


    Land Cover Class 8 - Water Bodies
    lccs_class_8
    %
    NOA


    Dataset: Biomes





    Dataset: Calculated





    Grid Area in square meters
    area
    m²
    NOA

*The datacube specifications (temporal, spatial resolution, chunk size) have been set up by the Max Planck Institut (MPI) team. For the variables that the contact is MPI, Lazaro Alonso (lalonso bgc-jena.mpg.de) has led the efforts to collect and process them. For the variables that the contact is NOA, Ilektra Karasante (ile.karasante noa.gr) has led the efforts to collect and process them.

They are some Matpower limit loading cases considered in some references. They have been obtained as follows: the injected active and reactive power of load buses along the injected active power of generation buses have been increased in steps of 0.0001 pu until the standard NR diverged from a flat start. For instance, in the case1354pegase, the limit load is 1.3139 pu (1.3140 pu gives rise to divergence).

NASA LIS output with water budget variables at 0.7 km^2 resolution over the Po river basin (Italy) for 2015-2023. Netcdf files for 8 + 2 experiments, described in De Lannoy et al. (2024, JAMES). Because of storage limitations, this upload contains 7 of the 8 experiments with ERA5. The baseline OL experiment without irrigation is on a separate zenodo link (see below).

8 experiments forced with ERA5 meteorology

po_ol_hymap_noirr: (OL) open loop simulation, no irrigation modeling --> 10.5281/zenodo.13768739po_ol_hymap_irr: (OL*) open loop simulation, with irrigation modeling

po_da_hymap_gamma_noirr: (DAg) data assimilation of Sentinel-1 backscatter (VV) for soil moisture updating, no irrigation modelingpo_da_hymap_gamma_irr: (DAg*) data assimilation of Sentinel-1 backscatter (VV) for soil moisture updating, with irrigation modeling

po_da_hymap_snd_noirr: (DAs) data assimilation of Sentinel-1 snow depth retrievals, no irrigation modelingpo_da_hymap_snd_irr: (DAs*) data assimilation of Sentinel-1 snow depth retrievals, with irrigation modeling

po_da_hymap_gamma_snd_noirr: (DAgs) data assimilation of Sentinel-1 backscatter (VV) for soil moisture updating and assimilation of snow depth retrievals, no irrigation modelingpo_da_hymap_gamma_snd_irr: (DAgs*) data assimilation of Sentinel-1 backscatter (VV) for soil moisture updating and assimilation of snow depth retrievals, with irrigation modeling

2 experiments forced with MERRA2 meteorology

--> These are not provided on Zenodo, because we hit the maximum storage limit. Feel free to reach out to the authors and ask for these data.

po_ol_hymap_noirr_M2: open loop simulation, no irrigation modelingpo_ol_hymap_irr_M2: open loop simulation, with irrigation modeling

Ten Ti-6Al-4V samples were mounted on a multi-sample stage for EBSD on a Thermo Fisher Apreo SEM equipped with an Oxford Instruments' Symmetry 2 detector at the University of Manchester.

In project multi-sample_1, AZtec reported a saving error when scanning the fifth sample and stopped with 5646 frames saved (.oip~4GB). It is able to montage and export the maps, but any edit on the .oip file cannot be saved.

In project multi-sample_2, we restarted the scan on the rest of the samples and completed with 5601 frames. The .oip is 3.97GB, which almost reaches the size limit. No error was reported during the scanning, and the .oip file is still editable.

These dataset contains the source data and the correposnding code for the paper explained above.

Data and code to make figures.

EFSA is regularly evaluating pesticide occurrence data in food generated under the official monitoring programs of Member States with respect to consumer exposure and risk assessment. Most of these data refer to raw commodities (RAC) because maximum residue levels established under European legislation reflect pesticide residues only in the RAC. However, food processing operations can have decisive effects on pesticide residue levels and therefore consumer exposure. This database has been developed to compile validated processing factors for pesticide residues in food in line with the EFSA food classification and description system (FoodEx2).

This update fixes a problem that caused some median processing factors to display a "<" qualifier when it was not necessary. The calculated values for the processing factors were not affected by this error and remain unchanged from the previous release

The database is complemented by the following publications:

• Background Document on the EU Database of Processing Factors for Pesticide Residues
• Compendium of Representative Processing Techniques Investigated in Regulatory Studies for Pesticides
• Linking Processed Foods and Processing Techniques to the FoodEx2 Coding System

Input structures for a manuscript, along with selected output data and structures. This directory structure contains a cut-down copy of the directories used to generate the simulation data and the analysis. In order to make this fit into the 50GB Zenodo limit, it was constructed with the following tar command: `tar -zcvf ligninsolvationstudy.tar.gz --exclude="*BAK" --exclude="*#" --exclude="*xtc" --exclude="*gro" --exclude="*log" --exclude="*[0-9].out" --exclude="*npz" --exclude="*pkl" --exclude="*npy" --exclude="*png" --exclude="*bmim*" --exclude="*old" --exclude="*dcd" --exclude="*tmp" --exclude="*xst" --exclude="*edr" --exclude="*txt" --exclude="*state_prev.cpt" LigninSolvation`, which intentionally excludes large files. The full dataset is available upon request.

Directory Descriptions

BuildSolventBoxes contains the scripts and inputs needed to make the solvent boxes suitable for use with the VMD solvate plugin.
BuildSystems assembles the lignin polymers and solvates them into a complete simulation system. Depends on the outputs from [LigninBuilder](https://github.com/jvermaas/LigninBuilder).
Equilibrium has all the equilibrium trajectories and the scripts needed to set them up.
FEP has the free energy perturbation calculation key outputs (the fepout files) and the scripts needed to set up the calculation and analyze them.

The scripts are mostly python scripts, but some are also in tcl, and have the appropriate file endings. GROMACS run input files (.tpr) and namd configuration files (.namd) may also be of general interest.

Synthetic dataset used in "The maximum weighted submatrix coverage problem: A CP approach".

Includes both the generated datasets as a zip archive and the python script used to generate them.

Each instance is composed of two files in the form

• XxY_K_O_0xN_AxB_Smatrix.tsv being the matrix to use. Each row on a separate line, with tab-separated cells.
• XxY_K_O_0xN_AxB_Ssolution.txt giving the implanted solution. One submatrix per line. Then two JSON arrays follow, separated by a tabulation. The first is the list of rows selected in the submatrix, the second the columns.

With:

• X and Y the size of the matrix
• K the number of submatrices in the implanted solution
• O the (minimum) overlap percentage of each submatrix
• N the sigma used for the background noise
• A and B the size of the implanted submatrices (subject to noise)

The dataset from the Alpha SETup (ASET) for publication in preparation: 'New upper limits for beta-delayed fission probabilities of 230,232Fr and 230,232,234Ac'

Includes the data analysed for results given in the publication from the LOI216 experimental campaign at ISOLDE (CERN).