Timmer, M. P., Erumban, A. A., Los, B., Stehrer, R., & De Vries, G. J. (2014). Slicing up global value chains. Journal of economic perspectives, 28(2), 99-118, DOI: 10.1257/jep.28.2.99 Related website

1. Contents

Available variables in KNMI-LENTIS

request-overview-CMIP-historical-including-EC-EARTH-AOGCM-preferences.txt

Where is the data deposited on the ECWMF's tape storage (section 4)

LENTIS_on_ECFS.zip

Data of all variables for 1 year for 1 ensemble member (section 5)

tree_of_files_one_member_all_data.txt

{AERmon,Amon,Emon,LImon,Lmon,Ofx,Omon,SImon,fx,Eday,Oday,day,CFday,3hr,6hrPlev,6hrPlevPt}.zip

1. Description of this Zenodo dataset

This Zenodo dataset pertains to the full KNMI-LENTIS dataset: a large ensemble of simulations with the Global Climate Model EC-Earth3. The periods are for the present-day period (2000-2009) and a future +2K period (2075-2084 following SSP2-4.5). KNMI-LENTIS has 1600 simulated years for both the two climates. This level of sampled climate variability allows for robust and in-depth research into extreme events. The available variables are listed in the file request-overview-CMIP-historical-including-EC-EARTH-AOGCM-preferences.txt. All variables are cmorised following CMIP6 data format convention. Further details on the variables and their output dimensions is available via the following search tool. The total size of KNMI-LENTIS is 128 TB. KNMI-LENTIS is stored at the high performance storage system of the ECMWF (ECFS).

The Global Climate Model that is used for generating this Large Ensemble is EC-Earth3 - VAREX project branch https://svn.ec-earth.org/ecearth3/branches/projects/varex (access restricted to ECMWF members).

The goal of this Zenodo dataset is :

to provide an accurate description and example of how the KNMI-LENTIS dataset is organised.

to describe in which servers the data are deposited and how to gain access to the data for future users

to provide links to related git repositories and other content relating to the KNMI-LENTIS production

1. How KNMI-LENTIS is organised

KNMI-LENTIS consists of 2 times 160 runs of 10 years. All simulations have a unique ensemble member label that reflects the forcing, and how the initial conditions are generated. The initial conditions have two aspects: the parent simulation from which the run is branched (macro perturbation, there are 16), and the seed relating to a particular micro-perturbation in the initial three-dimensional atmosphere temperature field (there are 10). The ensemble member label thus is a combination of:

forcing (h for present-day/historical and s for +2K/SSP2-4.5)

parent ID (number between 1 and 16)

micro perturbation ID (number between 0 and 9)

In this Zenodo dataset we publish 1 year from 1 member to give insight into the type of data and metadata that is representative of the full KNMI-LENTIS dataset. The published data is year 2000 from member h010. See Section 4

Further, all KNMI-LENTIS simulations are labeled per the CMIP6 convention of variant labelling. A variant label is made from four components: the realization index r, the initialization index i, the physics index p and the forcing index f. Further details on CMIP6 variant labelling be found in The CMIP6 Participation Guidance for Modelers. In the KNMI-LENTIS data set, the forcing is reflected in the first digit of the realization index r of the variant label. For the historical simulations, the one thousands (r1000-r1999) have been reserved. For the SSP2-4.5 the five thousands (r5000-r5999) have been reserved. The parent is reflected in the second and third digit of the realization index r of the variant label (r?01?-r?16?). The seed is reflected in the fourth digit of the realization index r: (r???0-r???9). The seed is also reflected in the initialization index i of the variant label (i0-i9), so this is double information. The physics index p5 has been reserved for the ECE3p5 version: all KNMI-LENTIS simulations have the p5 label. The forcing index f of the variant label is kept at 1 for all KNMI-LENTIS simulations. As an example, variant label r5119i9p5f1 refers to: the 2K time slice with parent 11 and randomizing seed number 9. The physics index is 5, meaning the run is done with the ECE3p5 version of EC-Earth3.

1. Where is the data deposited on the ECWMF's tape storage

In this Zenodo folder, there are several text files and several netcdf files. The text files provide

Data from KNMI-LENTIS is deposited in the ECMWF ECFS tape storage system. Data can be freely downloaded by to those who have access to the ECMWF ECFS. Else, the data can be made available by the authors upon request.

The way the dataset is organised is detailed in LENTIS_on_ECFS.zip. This contains details on all available KNMI-LENTIS files, in particular details for how these are filed in ECFS. The files on ECFS are tar zipped per ensemble member & variable: these contain 10 years of ensemble member data (10 separate netcdf files). The location on ECFS of the tar-zipped files that are listed in the various text files in this Zenodo dataset is

ec:/nklm/LENTIS/ec-earth/cmorised_by_var/

!/bin/bash

-------------------

script to write out LENTIS details on ECFS

-------------------

for freq in AERmon Amon Emon LImon Lmon Ofx Omon SImon fx Eday Oday day CFday 3hr 6hrPlev 6hrPlevPt; do for scen in hxxx sxxx; do els -l ec:/nklm/LENTIS/ec-earth/cmorised_by_var/${scen}/${freq}/* >> LENTIS_on_ECFS_${scen}_${freq}.txt done done

Further, part of the data will be made publicly available from the Earth System Grid Federation (ESGF) data portal. We aim to upload most of the monthly variables for the full ensemble. As search terms use EC-Earth for model and p5 for physical index to locate the KNMI-LENTIS data.

1. Data of all variables for 1 year for 1 ensemble member

The netcdf files of the data of 1 year from 1 member h010 are published here to give insight into the type of data and metadata that is representative of the full KNMI-LENTIS dataset. The data are in zipped folders per output frequencies: AERmon, Amon, Emon, LImon, Lmon, Ofx, Omon, SImon, fx, Eday, Oday, day, CFday, 3hr, 6hrPlev, 6hrPlevPt. The text file request-overview-CMIP-historical-including-EC-EARTH-AOGCM-preferences.txt gives an overview of variables available per output frequency. the text files tree_of_files_one_member_all_data.txt gives an overview of the files in the zipped folders.

1. Related links

The production of the KNMI-LENTIS ensemble was funded by the KNMI (Royal Dutch Meteorological Institute) multi-year strategic research fund KNMI MSO Climate Variability And Extremes (VAREX)

GitHub repository corresponding to this Zenodo dataset: https://github.com/lmuntjewerf/KNMI-LENTIS_dataset_description.git

Github repository for KNMI-LENTIS production code: https://github.com/lmuntjewerf/KNMI-LENTIS_production_script_train.git

In this data collection is:Data S1: A folder with all the fasta files, representing the 42 strains (41 Micromonosporaceae, 1 Streptomycetaceae).Data S2: A folder with all the .gbk files for the BGC regions predicted by antiSMASH v5.1.1. These files were used as inputs for BiG-SCAPE and BiG-SLiCE.Data S3: A folder with all the .gbk files for the BGC regions predicted by antiSMASH v6.1.0.Data S4: A folder containing all the Quast outputs for the 42 strains.Data S5: A folder containing all the BUSCO outputs for the 42 strains. Example scripts are provided for scraping relevant information from the individual BUSCO outputs.Data S6: A folder containing GTDB (Genome Taxonomy Database) classification results, and species-level grouping results using FastANI (95% cutoff).Data S7: A folder containing an Interactive Tree of Life (iTOL)-compatible bar chart annotation using antiSMASH v5.1.1 BGC region information.Data S8: A folder containing a word document that describes the parameters used with Ubuntu WSL (Windows Subsystem for Linux) on the command line for programs antiSMASH v6.1.2, BiG-SCAPE v1.1.2, and BiG-SLiCE v1.1.1. Also included are parameters for MDSC in python. An example script is also provided for batch queries of BGCs against BiG-SLiCE v1.1.1’s pre-processed dataset of ~1.2 million BGCs.Data S9: A folder containing the BiG-SCAPE visualization of the 38 Micromonosporaceae (post-QC filtering, excluding WMMA1363, WMMB482, WMMB486, and WMMC500) in Cytoscape.Data S10: A folder containing:The pre-processed dataset of 1.2 million BGCs from BiG-SLiCE.All report folders generated by BiG-SLiCE for the 779 Micromonosporaceae BGCs queried against the 1.2 million BGCs.The results data.db and associated folders for the pre-processed dataset of 1.2 million BGCs.Data S11: A folder containing the scripts necessary to regenerate the figures and perform independent analyses, and the relevant data used for the analyses.Data S12: A folder containing the results of the nucleotide blast of WMMA1947.region12's siderophore contig against WMMD1120.region14's siderophore contig.

Normalized differential cross-section of the Lund jet plane. The first systematic uncertainty is detector systematics, the second is background systematic...

Machine learning models fit complex algorithms to arbitrarily large datasets. These algorithms are well known to be high on performance and low on interpretability. We use interactive visualization of slices of predictor space to address the interpretability deficit; in effect opening up the black-box of machine learning algorithms, for the purpose of interrogating, explaining, validating and comparing model fits. Slices are specified directly through interaction, or using various touring algorithms designed to visit high-occupancy sections, or regions where the model fits have interesting properties. The methods presented here are implemented in the R package condvis2. Supplementary files for this article are available online.

5G

This dataset is about: Volume per slice of sediment core PS70/035-3. Please consult parent dataset @ https://doi.org/10.1594/PANGAEA.867451 for more information.

This dataset is about: Volume per slice of sediment core POS325/2_472. Please consult parent dataset @ https://doi.org/10.1594/PANGAEA.867451 for more information.

Project Y2 30 images of successive depth slices through the tomography volume. Each slice is 12.5 km apart.

Patient information and pressure data at baseline and follow-up when available (Scan time intervals were about 18 months; L: left carotid artery; R: right carotid artery).

Due to the nature of fMRI acquisition protocols, slices cannot be acquired simultaneously, and as a result, are temporally misaligned from each other. To correct from this misalignment, preprocessing pipelines often incorporate slice timing correction (STC). However, evaluating the benefits of STC is challenging because it (1) is dependent on slice acquisition parameters, (2) interacts with head movement in a non-linear fashion, and (3) significantly changes with other preprocessing steps, fMRI experimental design, and fMRI acquisition parameters. Presently, the interaction of STC with various scan conditions has not been extensively examined. Here, we examine the effect of STC when it is applied with various other preprocessing steps such as motion correction (MC), motion parameter residualization (MPR), and spatial smoothing. Using 180 simulated and 30 real fMRI data, we quantitatively demonstrate that the optimal order in which STC should be applied depends on interleave parameters and motion level. We also demonstrate the benefit STC on sub-second-TR scans and for functional connectivity analysis. We conclude that STC is a critical part of the preprocessing pipeline that can be extremely beneficial for fMRI processing. However, its effectiveness interacts with other preprocessing steps and with other scan parameters and conditions which may obscure its significant importance in the fMRI processing pipeline.

This paper describes new user controls for examining high-dimensional data using low-dimensional linear projections and slices. A user can interactively change the contribution of a given variable to a low-dimensional projection, which is useful for exploring the sensitivity of structure to particular variables. The user can also interactively shift the center of a slice, for example, to explore how structure changes in local subspaces. The Mathematica package as well as example notebooks are provided, which contain functions enabling the user to experiment with these new manual controls, with one specifically for exploring regions and boundaries produced by classification models. The advantage of Mathematica is its linear algebra capabilities and interactive cursor location controls. Some limited implementation has also been made available in the R package tourr.