cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7483/opendata.cms.vcyw.iesq
title: /NMSSM_XToYHTo2B2WTo2B2Q1L1Nu_MX-3500_MY-300_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.9rc8.7ig7
title: /NMSSM_XToYHTo2B2WTo2B2Q1L1Nu_MX-3500_MY-350_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.93jn.sscw
title: /NMSSM_XToYHTo2B2WTo2B2Q1L1Nu_MX-3500_MY-350_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.olol.ya1u
title: /NMSSM_XToYHTo2B2WTo2B2Q1L1Nu_MX-3500_MY-400_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.vk1g.awqs
title: /NMSSM_XToYHTo2B2WTo2B2Q1L1Nu_MX-3500_MY-400_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.0z5y.wqy4
title: /NMSSM_XToYHTo2B2WTo2B2Q1L1Nu_MX-3500_MY-450_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.pw6y.ngk8
title: /NMSSM_XToYHTo2B2WTo2B2Q1L1Nu_MX-3500_MY-450_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.vne7.yzzn
title: /NMSSM_XToYHTo2B2WTo2B2Q1L1Nu_MX-3500_MY-500_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.uupa.4v9w
title: /NMSSM_XToYHTo2B2WTo2B2Q1L1Nu_MX-3500_MY-500_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.cx9z.jlvn
title: /NMSSM_XToYHTo2B2WTo2B2Q1L1Nu_MX-3500_MY-600_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.lxug.yxps
title: /NMSSM_XToYHTo2B2WTo2B2Q1L1Nu_MX-3500_MY-600_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.bvnf.kwv9
title: /NMSSM_XToYHTo2B2WTo2B2Q1L1Nu_MX-3500_MY-60_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.xb3g.25m2
title: /NMSSM_XToYHTo2B2WTo2B2Q1L1Nu_MX-3500_MY-60_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.sm1w.jbo2
title: /NMSSM_XToYHTo2B2WTo2B2Q1L1Nu_MX-3500_MY-70_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.9o5e.5vth
title: /NMSSM_XToYHTo2B2WTo2B2Q1L1Nu_MX-3500_MY-70_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.cnbd.fs04
title: /NMSSM_XToYHTo2B2WTo2B2Q1L1Nu_MX-3500_MY-800_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.vos4.6qrl
title: /NMSSM_XToYHTo2B2WTo2B2Q1L1Nu_MX-3500_MY-800_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.g243.5iuz
title: /NMSSM_XToYHTo2B2WTo2B2Q1L1Nu_MX-3500_MY-80_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.5wt6.l8i7
title: /NMSSM_XToYHTo2B2WTo2B2Q1L1Nu_MX-3500_MY-80_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.cana.cwjx
title: /NMSSM_XToYHTo2B2WTo2B2Q1L1Nu_MX-3500_MY-900_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.9i63.fbu7
title: /NMSSM_XToYHTo2B2WTo2B2Q1L1Nu_MX-3500_MY-900_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.0jq8.wpky
title: /NMSSM_XToYHTo2B2WTo2B2Q1L1Nu_MX-3500_MY-90_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.15468/dl.d3cnd4
title: Occurrence Download

description: A dataset containing 1659 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Hibiscus syriacus L."
 ]
}

 The dataset includes 1659 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0067752-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7483/opendata.cms.o4hr.j0ov
title: /NMSSM_XToYHTo2B2WTo2B2Q1L1Nu_MX-3500_MY-90_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.zyvn.6van
title: /NMSSM_XToYHTo2B2WTo2B2Q1L1Nu_MX-360_MY-125_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.ce37.c5t1
title: /NMSSM_XToYHTo2B2WTo2B2Q1L1Nu_MX-360_MY-125_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.15468/dl.bnudsw
title: Occurrence Download

description: A dataset containing 1104 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Month is one of (January, February)",
 "OccurrenceStatus is Present",
 "TaxonKey is Anser fabalis (Latham, 1787)",
 "Year 2010-2010"
 ]
}

 The dataset includes 1104 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0067754-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7483/opendata.cms.y8nc.zcod
title: /NMSSM_XToYHTo2B2WTo2B2Q1L1Nu_MX-360_MY-60_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.p1t2.1p2v
title: /NMSSM_XToYHTo2B2WTo2B2Q1L1Nu_MX-360_MY-60_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.y9ni.cpgv
title: /NMSSM_XToYHTo2B2WTo2B2Q1L1Nu_MX-360_MY-70_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.u3et.oenn
title: /NMSSM_XToYHTo2B2WTo2B2Q1L1Nu_MX-360_MY-70_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.15468/dl.rraphy
title: Occurrence Download

description: A dataset containing 9244 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Month is one of (March, April, May, June, July, August, September, October, November, December)",
 "OccurrenceStatus is Present",
 "TaxonKey is Anser fabalis (Latham, 1787)",
 "Year 2010-2010"
 ]
}

 The dataset includes 9244 records from 41 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0067753-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7483/opendata.cms.a9h0.cg3v
title: /NMSSM_XToYHTo2B2WTo2B2Q1L1Nu_MX-360_MY-80_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.7j45.m2w4
title: /NMSSM_XToYHTo2B2WTo2B2Q1L1Nu_MX-360_MY-80_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.5281/zenodo.13309164
title: Results of Improved SMAP Soil Moisture Retrieval Using a Deep Neural Network-based Replacement of Radiative Transfer and Roughness Model

description: This repository contains:



A deep neural network (DNN) based soil moisture (SM) estimates (NN) based on the SMAP TB (Descending, 6 AM) and SMAP SCA-V ancillary data as the input variables. (SMAP_NN_36km_20150331_20220326.nc)

Temporally averaged roughness parameter (hNN) and scattering albedo (omegaNN) which are retrieved by inversely tracking the DNN model. (SMAP_hNN_omegaNN_36km_temporal_average_201503_202103.nc)


Summary:

The DNN model has been developed by relating SMAP TB and SMAP SCA-V ancillary data with in-situ SM data from the international soil moisture network (ISMN) using DNN. To minimize scale mismatch between gridded SMAP data and point in-situ data, the triple collocation analysis was conducted.

The SM estimated from the DNN algorithm (NN) showed a good agreement with the ISMN data that was not used in the model training. Moreover, for a densely vegetated region located in the Amazon (Tambopata site) the NN showed less bias compared to available SM retrievals. 

Two parameters hNN and omegaNN are retrieved by ingesting NN to the modified dual channel algorithm. When the SM retrieval was conducted using the hNN and omegaNN, the result showed good agreement with the NN (DNN-based SM) with R of 0.986, ubRMSD of 0.015 m3/m3, and bias of -0.001 m3/m3.

The paper "Improved SMAP Soil Moisture Retrieval Using a Deep Neural Network-based Replacement of Radiative Transfer and Roughness Model" published in the Transactions on Geoscience and Remote Sensing.

For more details, please contact me (wotp12@unist.ac.kr)
DOI: 10.5281/zenodo.13309165
title: Results of Improved SMAP Soil Moisture Retrieval Using a Deep Neural Network-based Replacement of Radiative Transfer and Roughness Model

description: This repository contains:



A deep neural network (DNN) based soil moisture (SM) estimates (NN) based on the SMAP TB (Descending, 6 AM) and SMAP SCA-V ancillary data as the input variables. (SMAP_NN_36km_20150331_20220326.nc)

Temporally averaged roughness parameter (hNN) and scattering albedo (omegaNN) which are retrieved by inversely tracking the DNN model. (SMAP_hNN_omegaNN_36km_temporal_average_201503_202103.nc)


Summary:

The DNN model has been developed by relating SMAP TB and SMAP SCA-V ancillary data with in-situ SM data from the international soil moisture network (ISMN) using DNN. To minimize scale mismatch between gridded SMAP data and point in-situ data, the triple collocation analysis was conducted.

The SM estimated from the DNN algorithm (NN) showed a good agreement with the ISMN data that was not used in the model training. Moreover, for a densely vegetated region located in the Amazon (Tambopata site) the NN showed less bias compared to available SM retrievals. 

Two parameters hNN and omegaNN are retrieved by ingesting NN to the modified dual channel algorithm. When the SM retrieval was conducted using the hNN and omegaNN, the result showed good agreement with the NN (DNN-based SM) with R of 0.986, ubRMSD of 0.015 m3/m3, and bias of -0.001 m3/m3.

The paper "Improved SMAP Soil Moisture Retrieval Using a Deep Neural Network-based Replacement of Radiative Transfer and Roughness Model" published in the Transactions on Geoscience and Remote Sensing.

For more details, please contact me (wotp12@unist.ac.kr)
DOI: 10.7483/opendata.cms.91ph.5syz
title: /NMSSM_XToYHTo2B2WTo2B2Q1L1Nu_MX-4000_MY-1000_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.15468/dl.77py3u
title: Occurrence Download

description: A dataset containing 112 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Anopheles sacharovi Favre, 1903"
 ]
}

 The dataset includes 112 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0067755-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7483/opendata.cms.ssz6.zt3w
title: /NMSSM_XToYHTo2B2WTo2B2Q1L1Nu_MX-4000_MY-1000_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.faqz.knhb
title: /NMSSM_XToYHTo2B2WTo2B2Q1L1Nu_MX-4000_MY-100_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.qgpo.y8ve
title: /NMSSM_XToYHTo2B2WTo2B2Q1L1Nu_MX-4000_MY-100_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.de6j.i1qx
title: /NMSSM_XToYHTo2B2WTo2B2Q1L1Nu_MX-4000_MY-1100_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.gjyy.ziz4
title: /NMSSM_XToYHTo2B2WTo2B2Q1L1Nu_MX-4000_MY-1100_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.qc8g.mjjn
title: /NMSSM_XToYHTo2B2WTo2B2Q1L1Nu_MX-4000_MY-1200_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.2qmq.s8g5
title: /NMSSM_XToYHTo2B2WTo2B2Q1L1Nu_MX-4000_MY-1200_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.nbiv.7tk5
title: /NMSSM_XToYHTo2B2WTo2B2Q1L1Nu_MX-4000_MY-125_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.2y9s.6s4g
title: /NMSSM_XToYHTo2B2WTo2B2Q1L1Nu_MX-4000_MY-125_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.536i.n3u5
title: /NMSSM_XToYHTo2B2WTo2B2Q1L1Nu_MX-4000_MY-1400_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.vae0.ngbq
title: /NMSSM_XToYHTo2B2WTo2B2Q1L1Nu_MX-4000_MY-1400_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.523k.30av
title: /NMSSM_XToYHTo2B2WTo2B2Q1L1Nu_MX-4000_MY-150_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.nmej.szre
title: /NMSSM_XToYHTo2B2WTo2B2Q1L1Nu_MX-4000_MY-150_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.eo20.1gc4
title: /NMSSM_XToYHTo2B2WTo2B2Q1L1Nu_MX-4000_MY-1800_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.cgqb.c61c
title: /NMSSM_XToYHTo2B2WTo2B2Q1L1Nu_MX-4000_MY-1800_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.8wsd.extn
title: /NMSSM_XToYHTo2B2WTo2B2Q1L1Nu_MX-4000_MY-190_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.gt6g.5xlx
title: /NMSSM_XToYHTo2B2WTo2B2Q1L1Nu_MX-4000_MY-190_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.z9n6.uxl6
title: /NMSSM_XToYHTo2B2WTo2B2Q1L1Nu_MX-4000_MY-2000_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.5281/zenodo.14238931
title: Effector candidates in Austropuccinia psidii - raw and supplementary data
DOI: 10.5281/zenodo.14238932
title: Effector candidates in Austropuccinia psidii - raw and supplementary data
DOI: 10.15468/dl.kv59zu
title: Occurrence Download

description: A dataset containing 143 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Anopheles sergentii (Theobald, 1907)"
 ]
}

 The dataset includes 143 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0067758-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25345/c5xp6vg15
title: MassIVE MSV000096844 - Chagas Disease Tropism_Tolerance
title: DDA LC_MS/MS from heart (section A) tissue of mice with or without T. cruzi infection
DOI: 10.5281/zenodo.11226102
title: GARNET Database

description: This repository contains all data described in our recent manuscript:

Yekaterina Shulgina*, Marena I. Trinidad*, Conner J. Langeberg*, Hunter Nisonoff*, Seyone Chithrananda*, Petr Skopintsev*, Amos J. Nissley*, Jaymin Patel, Ron S. Boger, Honglue Shi, Peter H. Yoon, Erin E Doherty, Tara Pande, Aditya M. Iyer, Jennifer A. Doudna, Jamie H. D. Cate*. RNA language models predict mutations that improve RNA function. Nat Commun 15, 10627 (2024). https://doi.org/10.1038/s41467-024-54812-y

* Contributed equally

Descriptions of all files are available in the enclosed README.md. The associated code for the manuscript is located here: https://github.com/Doudna-lab/GARNET_DL
DOI: 10.5281/zenodo.11226103
title: GARNET Database

description: This repository contains all data described in our recent manuscript:

Yekaterina Shulgina*, Marena I. Trinidad*, Conner J. Langeberg*, Hunter Nisonoff*, Seyone Chithrananda*, Petr Skopintsev*, Amos J. Nissley*, Jaymin Patel, Ron S. Boger, Honglue Shi, Peter H. Yoon, Erin E Doherty, Tara Pande, Aditya M. Iyer, Jennifer A. Doudna, Jamie H. D. Cate*. RNA language models predict mutations that improve RNA function. bioRxiv 2024.04.05.588317; doi: https://doi.org/10.1101/2024.04.05.588317

* Contributed equally

Descriptions of all files are available in the enclosed README.md. Associated code for the manuscript is located here: https://github.com/Doudna-lab/GARNET_DL
DOI: 10.5281/zenodo.12541208
title: GARNET Database

description: This repository contains all data described in our recent manuscript:

Yekaterina Shulgina*, Marena I. Trinidad*, Conner J. Langeberg*, Hunter Nisonoff*, Seyone Chithrananda*, Petr Skopintsev*, Amos J. Nissley*, Jaymin Patel, Ron S. Boger, Honglue Shi, Peter H. Yoon, Erin E Doherty, Tara Pande, Aditya M. Iyer, Jennifer A. Doudna, Jamie H. D. Cate*. RNA language models predict mutations that improve RNA function. bioRxiv 2024.04.05.588317; doi: https://doi.org/10.1101/2024.04.05.588317

* Contributed equally

Descriptions of all files are available in the enclosed README.md. Associated code for the manuscript is located here: https://github.com/Doudna-lab/GARNET_DL
DOI: 10.5281/zenodo.14003346
title: GARNET Database

description: This repository contains all data described in our recent manuscript:

Yekaterina Shulgina*, Marena I. Trinidad*, Conner J. Langeberg*, Hunter Nisonoff*, Seyone Chithrananda*, Petr Skopintsev*, Amos J. Nissley*, Jaymin Patel, Ron S. Boger, Honglue Shi, Peter H. Yoon, Erin E Doherty, Tara Pande, Aditya M. Iyer, Jennifer A. Doudna, Jamie H. D. Cate*. RNA language models predict mutations that improve RNA function. bioRxiv 2024.04.05.588317; doi: https://doi.org/10.1101/2024.04.05.588317

* Contributed equally

Descriptions of all files are available in the enclosed README.md. Associated code for the manuscript is located here: https://github.com/Doudna-lab/GARNET_DL
DOI: 10.5281/zenodo.14642491
title: GARNET Database

description: This repository contains all data described in our recent manuscript:

Yekaterina Shulgina*, Marena I. Trinidad*, Conner J. Langeberg*, Hunter Nisonoff*, Seyone Chithrananda*, Petr Skopintsev*, Amos J. Nissley*, Jaymin Patel, Ron S. Boger, Honglue Shi, Peter H. Yoon, Erin E Doherty, Tara Pande, Aditya M. Iyer, Jennifer A. Doudna, Jamie H. D. Cate*. RNA language models predict mutations that improve RNA function. Nat Commun 15, 10627 (2024). https://doi.org/10.1038/s41467-024-54812-y

* Contributed equally

Descriptions of all files are available in the enclosed README.md. The associated code for the manuscript is located here: https://github.com/Doudna-lab/GARNET_DL
DOI: 10.5281/zenodo.14642546
title: GARNET Database

description: This repository contains all data described in our recent manuscript:

Yekaterina Shulgina*, Marena I. Trinidad*, Conner J. Langeberg*, Hunter Nisonoff*, Seyone Chithrananda*, Petr Skopintsev*, Amos J. Nissley*, Jaymin Patel, Ron S. Boger, Honglue Shi, Peter H. Yoon, Erin E Doherty, Tara Pande, Aditya M. Iyer, Jennifer A. Doudna, Jamie H. D. Cate*. RNA language models predict mutations that improve RNA function. Nat Commun 15, 10627 (2024). https://doi.org/10.1038/s41467-024-54812-y

* Contributed equally

Descriptions of all files are available in the enclosed README.md. The associated code for the manuscript is located here: https://github.com/Doudna-lab/GARNET_DL
DOI: 10.25345/c5t14v202
title: MassIVE MSV000096845 - GNPS - Drug metabolites 5-ASA RT matching
title: Drug metabolites of 5-ASA, Retention time matching
DOI: 10.15468/dl.jkzhak
title: Occurrence Download

description: A dataset containing 1392 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Anopheles stephensi Liston, 1901"
 ]
}

 The dataset includes 1392 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0067759-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57841/casdc.0004967
title: FIGURE 9 in Eight new species of the genus Clubiona Latreille, 1804 from Xishuangbanna Rainforest, southwestern China (Araneae, Clubionidae)
DOI: 10.57841/casdc.0004968
title: FIGURE 8 in Eight new species of the genus Clubiona Latreille, 1804 from Xishuangbanna Rainforest, southwestern China (Araneae, Clubionidae)
DOI: 10.57841/casdc.0004969
title: FIGURE 7 in Eight new species of the genus Clubiona Latreille, 1804 from Xishuangbanna Rainforest, southwestern China (Araneae, Clubionidae)
DOI: 10.15468/dl.v7hsz7
title: Occurrence Download

description: A dataset containing 51 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Goniothalamus velutinus Airy Shaw"
 ]
}

 The dataset includes 51 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017223-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.y0d5ij
title: Occurrence Download

description: A dataset containing 86 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Limnodriloides scandinavicus Erséus, 1982"
 ]
}

 The dataset includes 86 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018499-151016162008034/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.36vp2y
title: Occurrence Download

description: A dataset containing 82 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Gymnadenia lithopolitanica (Ravnik) Teppner & E.Klein"
 ]
}

 The dataset includes 82 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017241-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fupnhe
title: Occurrence Download

description: A dataset containing 43559 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is Japan",
 "OccurrenceStatus is Present",
 "Year 2023-2023"
 ]
}

 The dataset includes 43559 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007015-230918134249559/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.