cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25582/data.2021-09.2316640/2100178
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2316151/2100179
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2317321/2100180
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2317735/2100181
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2315602/2100182
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2315657/2100183
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2316240/2100184
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2318094/2100185
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2317932/2100186
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2315977/2100187
title: Data for EMSL Project 51241 from September 2021
DOI: 10.5519/qd.k4cseplu
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (821 records)
DOI: 10.25582/data.2021-09.2315946/2100188
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2315975/2100189
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2316615/2100190
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2317648/2100191
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2315592/2100192
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2316539/2100193
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2318084/2100194
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2315624/2100195
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2315866/2100196
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2315640/2100197
title: Data for EMSL Project 51241 from September 2021
DOI: 10.5519/qd.wkyihef6
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (821 records)
DOI: 10.25582/data.2021-09.2316136/2100198
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2316294/2100199
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2317709/2100200
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2315762/2100201
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2316203/2100202
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2316131/2100203
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2316446/2100204
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2317630/2100205
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2317909/2100206
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2315880/2100207
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2317927/2100208
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2315781/2100209
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2316125/2100210
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2317871/2100211
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2316596/2100212
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2316464/2100213
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2316171/2100214
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2318013/2100215
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2317464/2100216
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2315995/2100217
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2316012/2100218
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2316225/2100219
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2316590/2100220
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2317432/2100221
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2315846/2100222
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2315859/2100223
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2316550/2100224
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2316163/2100225
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2317892/2100226
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2315889/2100227
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2316173/2100228
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2318129/2100229
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2318151/2100230
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2315921/2100231
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2318159/2100232
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2315840/2100233
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2317975/2100234
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2315928/2100235
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2317865/2100236
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2316035/2100237
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2316217/2100238
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2316303/2100239
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2315972/2100240
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2318059/2100241
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2315809/2100242
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2317861/2100243
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2317985/2100244
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2318144/2100245
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2316239/2100246
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2315911/2100247
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2316288/2100248
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2315831/2100249
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2316472/2100250
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2317356/2100251
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2315648/2100252
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2316386/2100253
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2315825/2100254
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2317857/2100255
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2318050/2100256
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2316617/2100257
title: Data for EMSL Project 51241 from September 2021
DOI: 10.5519/qd.xsryeri4
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (310 records)
DOI: 10.25582/data.2021-09.2315945/2100258
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2315976/2100259
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2318108/2100260
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2317798/2100261
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2316145/2100262
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2316470/2100263
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2316411/2100264
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2316042/2100265
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2316054/2100266
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2317881/2100267
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2316107/2100268
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2316118/2100269
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2317979/2100270
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2316564/2100271
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2317698/2100272
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2316361/2100273
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2316364/2100274
title: Data for EMSL Project 51241 from September 2021
DOI: 10.18712/nsd-nsd3135-v1
title: The Norwegian Integration Barometer 2022

description: The Norwegian Integration Barometer is a survey that deals with the population's attitudes towards immigration, integration and diversity in Norway. The Integration Barometer 2022 is the tenth round of the Directorate of Integration and Diversity's "Integration Barometer". The project was carried out from October to December 2021 and Kantar has carried out the data collection for the Institute for Social Research. The survey is aimed at the entire Norwegian population over the age of 15.
DOI: 10.5285/07877700-0e22-5fb5-e063-6c86abc03058
title: Northwest European Shelf daily and monthly physical-biogeochemical data (1990-2099) from NEMO-ERSEM coupled physics-biogeochemistry climate model forced by GFDL-ESM2G under scenario RCP8.5.

description: This dataset consists of daily averages of temperature, salinity, river nitrogen inputs, velocity vectors, nutrient, oxygen, and plankton related carbon and chlorophyll a data as well as monthly averages of carbonate chemistry, including air-sea and pelagic-benthos fluxes, and plankton related carbon, nitrogen, respiration, and primary production data. This is a gridded dataset, covering the Northwest European continental shelf from 40 to 60 degrees latitude and from -19 to 13 degrees longitude, with a horizontal resolution of 7 km and 52 sigma depth levels corresponding to the Atlantic Margin Model 7 km (AMM7) domain. The data cover the period from 1990 to 2099. These outputs are downscaled projections, obtained by running the Nucleus for European Modelling of the Ocean (NEMO) and European Regional Seas Ecosystem Model (ERSEM) coupled physics-biogeochemistry climate model, forced with lateral and atmospheric boundary conditions from the Geophysical Fluid Dynamics Laboratory, GFDL-ESM2G, Earth System Model from the Coupled Model Intercomparison Project collection, CMIP5, running under the RCP8.5 emission scenario. This dataset was generated by the Plymouth Marine Laboratory (PML) and the National Oceanography Centre (NOC), under Natural Environment Research Council (NERC) grant Resolving Climate Impacts on shelf and CoastaL sea Ecosystems, ReCICLE (grant numbers NE/M004120/1 and NE/M003477/2). The simulations were run on the ARCHER supercomputer managed by the University of Edinburgh.
DOI: 10.5281/zenodo.10004903
title: Second CMRR cdr workshop: Layer-fMRI analysis across pipelines

description: This repository contain workshop material of cortical depth resolved (cdr) fMRI, aka layer-fMRI Oct 10th-11th 2023. Official website is: https://sites.google.com/umn.edu/2023-minnesota-workshop/training/depth-resolved-layer-fmri.
The goal of this hands-on workshop was to:

Talk about the stuff that is not in the paper!
Show you the "ugly" parts of layer fMRI
Provide enough knowledge to allow analyzing layer data from beginning to end
Highlight the cool features of some of the most commonly used software
The workshop featured 4 independent pipelines of layer-fMRI analysis:

Yulia Lazarova: BrainVoyager
Anna Blazejewska: Freesurfer
Renzo Huber: LayNii AFNI
Cheryl Olman: Distrortion correction and registration
DOI: 10.5281/zenodo.10004904
title: Second CMRR cdr workshop: Layer-fMRI analysis across pipelines

description: This repository contain workshop material of cortical depth resolved (cdr) fMRI, aka layer-fMRI Oct 10th-11th 2023. Official website is: https://sites.google.com/umn.edu/2023-minnesota-workshop/training/depth-resolved-layer-fmri.
The goal of this hands-on workshop was to:

Talk about the stuff that is not in the paper!
Show you the "ugly" parts of layer fMRI
Provide enough knowledge to allow analyzing layer data from beginning to end
Highlight the cool features of some of the most commonly used software
The workshop featured 4 independent pipelines of layer-fMRI analysis:

Yulia Lazarova: BrainVoyager
Anna Blazejewska: Freesurfer
Renzo Huber: LayNii AFNI
Cheryl Olman: Distrortion correction and registration
DOI: 10.5281/zenodo.8373650
title: High frequency moonquake catalog

description: Catalog csv file for the Apollo 14, 15, and 16 stations
== File names ==

S14_New_MQ_SP.csv
S15_New_MQ_SP.csv
S16_New_MQ_SP.csv
== Format ==

1st column: Event ID
2nd column: Year
3rd column: Day of Year (DOY)
4th column: Station (14, 15, or 16)
5th column: Start time of a window used in coherence analysis
6th column: End time of a window used in coherence analysis
7th column: Event Type (see the main text)
== Note ==
The detailed information on new shallow moonquakes is summarized in the main text (Table A1 in Appendix A). You may refer to it for a particular analysis of shallow moonquakes.
DOI: 10.18462/iir.icr.2023.0093
title: Decarbonisation pathways for fossil fuel-based district heating networks using heat pumps.
DOI: 10.5285/0786d770-ba54-0e57-e063-6c86abc09fdf
title: Northwest European Shelf daily and monthly physical-biogeochemical data (1990-2099) from NEMO-ERSEM coupled physics-biogeochemistry climate model forced by IPSL-CM5A-MR under scenario RCP8.5.

description: This dataset consists of daily averages of temperature, salinity, river nitrogen inputs, velocity vectors, nutrient, oxygen, and plankton related carbon and chlorophyll a data as well as monthly averages of carbonate chemistry, including air-sea and pelagic-benthos fluxes, and plankton related carbon, nitrogen, respiration, and primary production data. This is a gridded dataset, covering the Northwest European continental shelf from 40 to 60 degrees latitude and from -19 to 13 degrees longitude, with a horizontal resolution of 7 km and 52 sigma depth levels corresponding to the Atlantic Margin Model 7 km (AMM7) domain. The data cover the period from 1990 to 2099. These outputs are downscaled projections, obtained by running the Nucleus for European Modelling of the Ocean (NEMO) and European Regional Seas Ecosystem Model (ERSEM) coupled physics-biogeochemistry climate model, forced with lateral and atmospheric boundary conditions from the Institut Pierre-Simon Laplace Coupled Model with mid-resolution, IPSL-CM5A-MR, from the Coupled Model Intercomparison Project collection, CMIP5, running under the RCP8.5 emission scenario. This dataset was generated by the Plymouth Marine Laboratory (PML) and the National Oceanography Centre (NOC), under Natural Environment Research Council (NERC) grant Resolving Climate Impacts on shelf and CoastaL sea Ecosystems, ReCICLE (grant numbers NE/M004120/1 and NE/M003477/2). The simulations were run on the ARCHER supercomputer managed by the University of Edinburgh.
DOI: 10.5281/zenodo.10012518
title: Raw data: X-ray tomography of catalyst H101

description: XRay tomography data for a bed of spherical catalyst H101.
 
Funding:
ProHyGen
Bundesministerium für Wirtschaft und Klimaschutz
FKZ  03EI3074B-D
DOI: 10.5281/zenodo.10012517
title: Raw data: X-ray tomography of catalyst H101

description: XRay tomography data for a bed of spherical catalyst H101.
 
Funding:
ProHyGen
Bundesministerium für Wirtschaft und Klimaschutz
FKZ  03EI3074B-D
DOI: 10.5281/zenodo.10014342
title: Nomogram Built Based on Machine Learning to Predict Recurrence in Early-stage Hepatocellular Carcinoma Patients Treated with Ablation
DOI: 10.5281/zenodo.10014341
title: Nomogram Built Based on Machine Learning to Predict Recurrence in Early-stage Hepatocellular Carcinoma Patients Treated with Ablation
DOI: 10.5281/zenodo.10014198
title: ERA5-Land selected indicators daily aggregates for the Latin America region, 1953

description: This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land eight selected indicators, covering the Latin America region, for 1953.
Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.
For 2m dewpoint pressure, 10m u component of wind, 10m v component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean and minimum were used for aggregation.
Those files were created using the KrigR package.
DOI: 10.5281/zenodo.10014197
title: ERA5-Land selected indicators daily aggregates for the Latin America region, 1953

description: This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land eight selected indicators, covering the Latin America region, for 1953.
Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.
For 2m dewpoint pressure, 10m u component of wind, 10m v component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean and minimum were used for aggregation.
Those files were created using the KrigR package.
DOI: 10.14473/csda/hfzklt/car2om
title: TI_CSDA00305_00317.pdf

description: Technická informace 62. vlna - 74. vlna
DOI: 10.14473/csda/hfzklt/p3qgpz
title: D_CSDA00307.pdf

description: Dotazník 64. vlna
DOI: 10.14473/csda/hfzklt/07nlt9
title: 64. vlna - rijen 2020 TC_data._FINAL.tab

description: Data
DOI: 10.14473/csda/18ooac/nb3czw
title: TI_CSDA00305_00317.pdf

description: Technická informace 62. vlna - 74. vlna
DOI: 10.14473/csda/18ooac/aibowy
title: 65. vlna - listopad 2020 TC_data_FINAL.tab

description: Data
DOI: 10.14473/csda/18ooac/kgyxpb
title: D_CSDA00308.pdf

description: Dotazník 65. vlna
DOI: 10.5281/zenodo.7495024
title: Replication Package for: Streaming vs. Functions: A Cost Perspective on Cloud Event Processing

description: In cloud event processing, data generated at the edge is processed in real-time by cloud resources. Both distributed stream processing (DSP) and Function-as-a-Service (FaaS) have been proposed to implement such event processing applications. FaaS emphasizes fast development and easy operation, while DSP emphasizes efficient handling of large data volumes. Despite their architectural differences, both can be used to model and implement loosely-coupled job graphs. In this paper, we consider the selection of FaaS and DSP from a cost perspective. We implement stateless and stateful workflows from the Theodolite benchmarking suite using cloud FaaS and DSP. In an extensive evaluation, we show how application type, cloud service provider, and runtime environment can influence the cost of application deployments and derive decision guidelines for cloud engineers.
DOI: 10.5281/zenodo.7495025
title: Replication Package for: Streaming vs. Functions: A Cost Perspective on Cloud Event Processing

description: In cloud event processing, data generated at the edge is processed in real-time by cloud resources. Both distributed stream processing (DSP) and Function-as-a-Service (FaaS) have been proposed to implement such event processing applications. FaaS emphasizes fast development and easy operation, while DSP emphasizes efficient handling of large data volumes. Despite their architectural differences, both can be used to model and implement loosely-coupled job graphs. In this paper, we consider the selection of FaaS and DSP from a cost perspective. We implement stateless and stateful workflows from the Theodolite benchmarking suite using cloud FaaS and DSP. In an extensive evaluation, we show how application type, cloud service provider, and runtime environment can influence the cost of application deployments and derive decision guidelines for cloud engineers.
DOI: 10.5281/zenodo.5596983
title: Replication Package for: A Configurable Method for Benchmarking Scalability of Cloud-Native Applications

description: This repository contains a replication package and experimental results for our study A Configurable Method for Benchmarking Scalability of Cloud-Native Applications.


It provides benchmark execution files for repeating our experiments as well as the collected data from our experiments and Jupyter notebooks for reproducing our analysis.


Instructions for repeating our experiments and reproducing our analysis can be found in the Readme.md file.
DOI: 10.5281/zenodo.5596982
title: Replication Package for: A Configurable Method for Benchmarking Scalability of Cloud-Native Applications

description: This repository contains a replication package and experimental results for our study A Configurable Method for Benchmarking Scalability of Cloud-Native Applications.


It provides benchmark execution files for repeating our experiments as well as the collected data from our experiments and Jupyter notebooks for reproducing our analysis.


Instructions for repeating our experiments and reproducing our analysis can be found in the Readme.md file.
DOI: 10.5281/zenodo.10014380
title: agave_karwinskii_decontaminated_FSCR.fasta.gz

description: .
DOI: 10.5281/zenodo.10014379
title: agave_karwinskii_decontaminated_FSCR.fasta.gz

description: .
DOI: 10.14473/csda/ais714/qsrqvg
title: D_CSDA00309.pdf

description: Dotazník 66. vlna
DOI: 10.14473/csda/ais714/ox0qjp
title: TI_CSDA00305_00317.pdf

description: Technická informace 62. vlna - 74. vlna
DOI: 10.14473/csda/ais714/tic7qo
title: 66. vlna - leden 2021 TC_data_FINAL.tab

description: Data
DOI: 10.14473/csda/bmvfap/kvbq0b
title: TI_CSDA00305_00317.pdf

description: Technická informace 62. vlna - 74. vlna
DOI: 10.14473/csda/bmvfap/y9frrf
title: D_CSDA00310.pdf

description: Dotazník 67. vlna
DOI: 10.14473/csda/bmvfap/majrs4
title: 67. vlna - unor 2021 TC_data_FINAL.tab

description: Data
DOI: 10.6084/m9.figshare.24324700
title: Code underlying the publication: Resilience of manufacturing supply chain network under multiple disruption scenarios

description: This dataset supplements the paper of "Resilience of manufacturing supply chain network under multiple disruption scenarios" with the codes (in the format of .m) to duplicate the reported measurement figures.
DOI: 10.6084/m9.figshare.24324700.v1
title: Code underlying the publication: Resilience of manufacturing supply chain network under multiple disruption scenarios

description: This dataset supplements the paper of "Resilience of manufacturing supply chain network under multiple disruption scenarios" with the codes (in the format of .m) to duplicate the reported measurement figures.
DOI: 10.5281/zenodo.10014408
title: cinnycerthia_unirufa_decontaminated_FSCR.fasta.gz

description: cinnycerthia_unirufa_decontaminated_FSCR.fasta.gz