cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.14473/csda/ngivbd/a7jd5c
title: PR010_V1511.pdf

description: Názory občanů na úroveň sociální zabezpečení v ČR a alokace veřejných prostředků – listopad 2015. Tisková zpráva CVVM.
DOI: 10.14473/csda/ngivbd/xzmwxz
title: PR04_V1511.pdf

description: Česká veřejnost o tzv. Islámském státu – listopad 2015.Tisková zpráva CVVM.
DOI: 10.22008/fk2/sd56cb/5vvdqs
title: SICEv2.3.2_Greenland_1000m_2023_07_15.nc

description: SICEv2.3.2 output: SICEv2.3.2_Greenland_1000m_2023_07_15.nc, date: 2023-07-15
DOI: 10.14473/csda/ngivbd/bymax9
title: PR012_V1511.pdf

description: Daně z pohledu veřejného mínění – listopad 2015
DOI: 10.14473/csda/ngivbd/4lkg1j
title: PR02_V1511.pdf

description: Důvěra ústavním institucím v listopadu 2015. Tisková zpráva CVVM.
DOI: 10.14473/csda/ngivbd/ijhyf6
title: PR011_V1511.pdf

description: Občané o Transatlantickém obchodním a investičním partnerství – listopad 2015. Tisková zpráva CVVM.
DOI: 10.22008/fk2/sd56cb/pytns5
title: SICEv2.3.2_Greenland_1000m_2023_07_16.nc

description: SICEv2.3.2 output: SICEv2.3.2_Greenland_1000m_2023_07_16.nc, date: 2023-07-16
DOI: 10.14473/csda/ngivbd/tyfwbi
title: PR01_V1511.pdf

description: Postoj české veřejnosti k přijímání uprchlíků říjen a listopad 2015. Tisková zpráva CVVM.
DOI: 10.14473/csda/ngivbd/ueysy9
title: PR08_V1511.pdf

description: Změny v důchodovém systému: hodnocení občanů - listopad 2015. Tisková zpráva CVVM.
DOI: 10.14473/csda/ngivbd
title: Naše společnost 2015 - listopad

description: Výzkum provedl výzkumný tým CVVM Sociologického ústavu AV ČR, v.v.i., v rámci výzkumu „Naše společnost 2015". Sběr dat provedla tazatelská síť CVVM.
DOI: 10.22008/fk2/sd56cb/epojel
title: SICEv2.3.2_Greenland_1000m_2023_07_17.nc

description: SICEv2.3.2 output: SICEv2.3.2_Greenland_1000m_2023_07_17.nc, date: 2023-07-17
DOI: 10.22008/fk2/sd56cb/qyu54k
title: SICEv2.3.2_Greenland_1000m_2023_07_18.nc

description: SICEv2.3.2 output: SICEv2.3.2_Greenland_1000m_2023_07_18.nc, date: 2023-07-18
DOI: 10.22008/fk2/sd56cb/chrv7f
title: SICEv2.3.2_Greenland_1000m_2023_07_19.nc

description: SICEv2.3.2 output: SICEv2.3.2_Greenland_1000m_2023_07_19.nc, date: 2023-07-19
DOI: 10.22008/fk2/sd56cb/lj3ube
title: SICEv2.3.2_Greenland_1000m_2023_07_20.nc

description: SICEv2.3.2 output: SICEv2.3.2_Greenland_1000m_2023_07_20.nc, date: 2023-07-20
DOI: 10.22008/fk2/sd56cb/chdm3m
title: SICEv2.3.2_Greenland_1000m_2023_07_21.nc

description: SICEv2.3.2 output: SICEv2.3.2_Greenland_1000m_2023_07_21.nc, date: 2023-07-21
DOI: 10.22008/fk2/sd56cb/sp9d2c
title: SICEv2.3.2_Greenland_1000m_2023_07_22.nc

description: SICEv2.3.2 output: SICEv2.3.2_Greenland_1000m_2023_07_22.nc, date: 2023-07-22
DOI: 10.22008/fk2/sd56cb/6eom28
title: SICEv2.3.2_Greenland_1000m_2023_07_23.nc

description: SICEv2.3.2 output: SICEv2.3.2_Greenland_1000m_2023_07_23.nc, date: 2023-07-23
DOI: 10.22008/fk2/sd56cb/k68g8f
title: SICEv2.3.2_Greenland_1000m_2023_07_24.nc

description: SICEv2.3.2 output: SICEv2.3.2_Greenland_1000m_2023_07_24.nc, date: 2023-07-24
DOI: 10.22008/fk2/sd56cb/lcvryb
title: SICEv2.3.2_Greenland_1000m_2023_07_25.nc

description: SICEv2.3.2 output: SICEv2.3.2_Greenland_1000m_2023_07_25.nc, date: 2023-07-25
DOI: 10.22008/fk2/sd56cb/xorxxh
title: SICEv2.3.2_Greenland_1000m_2023_07_26.nc

description: SICEv2.3.2 output: SICEv2.3.2_Greenland_1000m_2023_07_26.nc, date: 2023-07-26
DOI: 10.22008/fk2/sd56cb/ta0qq7
title: SICEv2.3.2_Greenland_1000m_2023_07_27.nc

description: SICEv2.3.2 output: SICEv2.3.2_Greenland_1000m_2023_07_27.nc, date: 2023-07-27
DOI: 10.22008/fk2/sd56cb/3wnlmv
title: SICEv2.3.2_Greenland_1000m_2023_07_28.nc

description: SICEv2.3.2 output: SICEv2.3.2_Greenland_1000m_2023_07_28.nc, date: 2023-07-28
DOI: 10.22008/fk2/sd56cb/hkre1v
title: SICEv2.3.2_Greenland_1000m_2023_07_29.nc

description: SICEv2.3.2 output: SICEv2.3.2_Greenland_1000m_2023_07_29.nc, date: 2023-07-29
DOI: 10.22008/fk2/sd56cb/inzuhz
title: SICEv2.3.2_Greenland_1000m_2023_07_30.nc

description: SICEv2.3.2 output: SICEv2.3.2_Greenland_1000m_2023_07_30.nc, date: 2023-07-30
DOI: 10.22008/fk2/sd56cb/jzxwgv
title: SICEv2.3.2_Greenland_1000m_2023_07_31.nc

description: SICEv2.3.2 output: SICEv2.3.2_Greenland_1000m_2023_07_31.nc, date: 2023-07-31
DOI: 10.22008/fk2/sd56cb/4tmkub
title: SICEv2.3.2_Greenland_1000m_2023_08_01.nc

description: SICEv2.3.2 output: SICEv2.3.2_Greenland_1000m_2023_08_01.nc, date: 2023-08-01
DOI: 10.22008/fk2/sd56cb/wov2qc
title: SICEv2.3.2_Greenland_1000m_2023_08_02.nc

description: SICEv2.3.2 output: SICEv2.3.2_Greenland_1000m_2023_08_02.nc, date: 2023-08-02
DOI: 10.22008/fk2/sd56cb/wrh2an
title: SICEv2.3.2_Greenland_1000m_2023_08_03.nc

description: SICEv2.3.2 output: SICEv2.3.2_Greenland_1000m_2023_08_03.nc, date: 2023-08-03
DOI: 10.22008/fk2/sd56cb/1q9bko
title: SICEv2.3.2_Greenland_1000m_2023_08_04.nc

description: SICEv2.3.2 output: SICEv2.3.2_Greenland_1000m_2023_08_04.nc, date: 2023-08-04
DOI: 10.22008/fk2/sd56cb/tt8eb3
title: SICEv2.3.2_Greenland_1000m_2023_08_06.nc

description: SICEv2.3.2 output: SICEv2.3.2_Greenland_1000m_2023_08_06.nc, date: 2023-08-06
DOI: 10.22008/fk2/sd56cb/vpgxno
title: SICEv2.3.2_Greenland_1000m_2023_08_07.nc

description: SICEv2.3.2 output: SICEv2.3.2_Greenland_1000m_2023_08_07.nc, date: 2023-08-07
DOI: 10.22008/fk2/sd56cb/qeilmg
title: SICEv2.3.2_Greenland_1000m_2023_08_08.nc

description: SICEv2.3.2 output: SICEv2.3.2_Greenland_1000m_2023_08_08.nc, date: 2023-08-08
DOI: 10.22008/fk2/sd56cb/nuve2p
title: SICEv2.3.2_Greenland_1000m_2023_08_09.nc

description: SICEv2.3.2 output: SICEv2.3.2_Greenland_1000m_2023_08_09.nc, date: 2023-08-09
DOI: 10.22008/fk2/sd56cb/9fzncm
title: SICEv2.3.2_Greenland_1000m_2023_08_10.nc

description: SICEv2.3.2 output: SICEv2.3.2_Greenland_1000m_2023_08_10.nc, date: 2023-08-10
DOI: 10.22008/fk2/sd56cb/uzrqse
title: SICEv2.3.2_Greenland_1000m_2023_08_11.nc

description: SICEv2.3.2 output: SICEv2.3.2_Greenland_1000m_2023_08_11.nc, date: 2023-08-11
DOI: 10.22008/fk2/sd56cb/5sfnvt
title: SICEv2.3.2_Greenland_1000m_2023_08_13.nc

description: SICEv2.3.2 output: SICEv2.3.2_Greenland_1000m_2023_08_13.nc, date: 2023-08-13
DOI: 10.22008/fk2/sd56cb/gykwck
title: SICEv2.3.2_Greenland_1000m_2023_08_14.nc

description: SICEv2.3.2 output: SICEv2.3.2_Greenland_1000m_2023_08_14.nc, date: 2023-08-14
DOI: 10.22008/fk2/sd56cb/p6kyrk
title: SICEv2.3.2_Greenland_1000m_2023_08_15.nc

description: SICEv2.3.2 output: SICEv2.3.2_Greenland_1000m_2023_08_15.nc, date: 2023-08-15
DOI: 10.22008/fk2/sd56cb/ntogfm
title: SICEv2.3.2_Greenland_1000m_2023_08_16.nc

description: SICEv2.3.2 output: SICEv2.3.2_Greenland_1000m_2023_08_16.nc, date: 2023-08-16
DOI: 10.22008/fk2/sd56cb/uar6av
title: SICEv2.3.2_Greenland_1000m_2023_08_17.nc

description: SICEv2.3.2 output: SICEv2.3.2_Greenland_1000m_2023_08_17.nc, date: 2023-08-17
DOI: 10.22008/fk2/sd56cb/5zi2po
title: SICEv2.3.2_Greenland_1000m_2023_08_18.nc

description: SICEv2.3.2 output: SICEv2.3.2_Greenland_1000m_2023_08_18.nc, date: 2023-08-18
DOI: 10.22008/fk2/sd56cb/b6giyy
title: SICEv2.3.2_Greenland_1000m_2023_08_19.nc

description: SICEv2.3.2 output: SICEv2.3.2_Greenland_1000m_2023_08_19.nc, date: 2023-08-19
DOI: 10.22008/fk2/sd56cb/b2nzih
title: SICEv2.3.2_Greenland_1000m_2023_08_20.nc

description: SICEv2.3.2 output: SICEv2.3.2_Greenland_1000m_2023_08_20.nc, date: 2023-08-20
DOI: 10.22008/fk2/sd56cb/hoggei
title: SICEv2.3.2_Greenland_1000m_2023_08_21.nc

description: SICEv2.3.2 output: SICEv2.3.2_Greenland_1000m_2023_08_21.nc, date: 2023-08-21
DOI: 10.22008/fk2/sd56cb/vslm6b
title: SICEv2.3.2_Greenland_1000m_2023_08_22.nc

description: SICEv2.3.2 output: SICEv2.3.2_Greenland_1000m_2023_08_22.nc, date: 2023-08-22
DOI: 10.22008/fk2/sd56cb/zkhip9
title: SICEv2.3.2_Greenland_1000m_2023_08_23.nc

description: SICEv2.3.2 output: SICEv2.3.2_Greenland_1000m_2023_08_23.nc, date: 2023-08-23
DOI: 10.22008/fk2/sd56cb/qoxnhq
title: SICEv2.3.2_Greenland_1000m_2023_08_25.nc

description: SICEv2.3.2 output: SICEv2.3.2_Greenland_1000m_2023_08_25.nc, date: 2023-08-25
DOI: 10.22008/fk2/sd56cb/3caaow
title: SICEv2.3.2_Greenland_1000m_2023_08_26.nc

description: SICEv2.3.2 output: SICEv2.3.2_Greenland_1000m_2023_08_26.nc, date: 2023-08-26
DOI: 10.5281/zenodo.10171287
title: 05052023 Purification compound 223
DOI: 10.5281/zenodo.10171288
title: 05052023 Purification compound 223
DOI: 10.34740/kaggle/dsv/7018221
title: Global Genomic Neurofibromatosis Neoplasia
DOI: 10.5517/ccdc.csd.cc26m253
title: CCDC 2044114: Experimental Crystal Structure Determination

description: Related Article: Meng Yang|2020|CSD Communication|||
description: Related Article: Xiaoman Kuang, Youhong Li, Meng Yang, Wen Dong, Jidong Leng|2023|Dalton Trans.|52|16791|doi:10.1039/D3DT02876C
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc26m264
title: CCDC 2044115: Experimental Crystal Structure Determination

description: Related Article: Meng Yang|2020|CSD Communication|||
description: Related Article: Xiaoman Kuang, Youhong Li, Meng Yang, Wen Dong, Jidong Leng|2023|Dalton Trans.|52|16791|doi:10.1039/D3DT02876C
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc259ybs
title: CCDC 2005555: Experimental Crystal Structure Determination

description: Related Article: Xiaoman Kuang, Youhong Li, Meng Yang, Wen Dong, Jidong Leng|2023|Dalton Trans.|52|16791|doi:10.1039/D3DT02876C
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc259y8q
title: CCDC 2005553: Experimental Crystal Structure Determination

description: Related Article: Xiaoman Kuang, Youhong Li, Meng Yang, Wen Dong, Jidong Leng|2023|Dalton Trans.|52|16791|doi:10.1039/D3DT02876C
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc29x6fw
title: CCDC 2142268: Experimental Crystal Structure Determination

description: Related Article: Wei Dong, Tian-Ze Li, Xiao-Yan Huang, Xiao-Feng He, Chang-An Geng, Xue -Mei Zhang, Ji-Jun Chen|2022|Bioorg.Chem.|128|106056|doi:10.1016/j.bioorg.2022.106056
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc29x6hy
title: CCDC 2142270: Experimental Crystal Structure Determination

description: Related Article: Wei Dong, Tian-Ze Li, Xiao-Yan Huang, Xiao-Feng He, Chang-An Geng, Xue -Mei Zhang, Ji-Jun Chen|2022|Bioorg.Chem.|128|106056|doi:10.1016/j.bioorg.2022.106056
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc29x6k0
title: CCDC 2142272: Experimental Crystal Structure Determination

description: Related Article: Wei Dong, Tian-Ze Li, Xiao-Yan Huang, Xiao-Feng He, Chang-An Geng, Xue -Mei Zhang, Ji-Jun Chen|2022|Bioorg.Chem.|128|106056|doi:10.1016/j.bioorg.2022.106056
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2dq2dm
title: CCDC 2225750: Experimental Crystal Structure Determination

description: Related Article: Li-Li Xu, Hong Cai, Dong Luo, Mian Li, Yong-Liang Huang, Jie-Ji Zhu, Mo Xie, Zi-Wei Chen, Dan Li|2023|Inorg.Chem.Front.|10|6860|doi:10.1039/D3QI01569F
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2dq2fn
title: CCDC 2225751: Experimental Crystal Structure Determination

description: Related Article: Li-Li Xu, Hong Cai, Dong Luo, Mian Li, Yong-Liang Huang, Jie-Ji Zhu, Mo Xie, Zi-Wei Chen, Dan Li|2023|Inorg.Chem.Front.|10|6860|doi:10.1039/D3QI01569F
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5281/zenodo.10142812
title: A data-and-knowledge driven structure-based molecular generative framework

description: Dataset for pretraining and finetuning of PocketFlow.
DOI: 10.5281/zenodo.10142813
title: A data-and-knowledge driven structure-based molecular generative framework

description: Dataset for pretraining and finetuning of PocketFlow.
DOI: 10.5281/zenodo.10171305
title: V28_1 Untargeted Metabolomics S.elongatus WT vs delta CutA
DOI: 10.5281/zenodo.10171306
title: V28_1 Untargeted Metabolomics S.elongatus WT vs delta CutA
DOI: 10.26050/wdcc/highresolclimnevada_eval
title: High-resolution Climate Data for a High-altitude Region in Southern Spain (Sierra Nevada): Evaluation

description: Project: High-resolution Climate Data for a High-altitude Region in Southern Spain: Sierra Nevada - Climate data at very-high resolution (1 km spatial resolution) for Sierra Nevada, the highest mountain region in the Iberian Peninsula located in southeastern Andalusia (Spain) https://smartecomountains.lifewatch.dev/. The use of very high resolution simulations allows a more adequate characterisation of the climate in regions with complex orography such as the Sierra Nevada, since at this resolution the convection is resolved by the model and is not parameterised. Summary: Climate data at very-high resolution (1 km spatial resolution) for Sierra Nevada, the highest mountain region in the Iberian Peninsula located in southeastern Andalusia (Spain). Data obtained using the Weather Research & Forecasting (WRF) model v4.3.3 (Skamarock et al., 2021) driven by the ERA5 reanalysis (Hersbach et al., 2018). The PBL was fixed to the Asymmetric Convective Model version 2 (ACM2, Pleim, 2007). Both, long- and short-wave radiation were parametrized using the Community Atmosphere Model 3.0 (CAM3.0, Collins et al., 2004). The microphysics scheme was the WRF single-moment 7-class (WSM7, Bae et al., 2019), and the land surface model the NOAH MP (Niu et al., 2011). The convection scheme was deactivated.
DOI: 10.25934/pr00008663
title: Confirmatory study of DSP-5423 in pediatric patients with schizophrenia.

description: Vivli is an independent, non-profit organization that has developed a global data-sharing and analytics platform to serve all elements of the international research community. Our mission is to promote, coordinate, and facilitate scientific sharing and reuse of clinical research data through the creation and implementation of a sustainable global data-sharing enterprise. The Vivli platform includes an independent data repository, in-depth search engine and a cloud-based, secure analytics platform.
DOI: 10.26050/wdcc/highresolclimnevada_warm
title: High-resolution Climate Data for a High-altitude Region in Southern Spain (Sierra Nevada): Pseudo-global warming

description: Project: High-resolution Climate Data for a High-altitude Region in Southern Spain: Sierra Nevada - Climate data at very-high resolution (1 km spatial resolution) for Sierra Nevada, the highest mountain region in the Iberian Peninsula located in southeastern Andalusia (Spain) https://smartecomountains.lifewatch.dev/. The use of very high resolution simulations allows a more adequate characterisation of the climate in regions with complex orography such as the Sierra Nevada, since at this resolution the convection is resolved by the model and is not parameterised. Summary: Climate data at very-high resolution (1 km spatial resolution) for Sierra Nevada, the highest mountain region in the Iberian Peninsula located in southeastern Andalusia (Spain). Data obtained using the Weather Research & Forecasting (WRF) model v4.3.3 (Skamarock et al., 2021) driven by ERA5 (Hersbach et al., 2018) plus a global warming signal calculated for the period 2070-2099 compared to 1985-2014 from an ensemble of 24 GCMs from CMIP6 under the SSP5-8.5 scenario. PBL was fixed to the Asymmetric Convective Model version 2 (ACM2, Pleim, 2007). Both long- and short-wave radiation were parametrized using the Community Atmosphere Model 3.0 (CAM3.0, Collins et al., 2004). The microphysics scheme used was WRF single-moment 6-class (WSM6, Hong and Lim 2006), and the land surface model the NOAH MP (Niu et al., 2011). The convection scheme was deactivated.
DOI: 10.18712/nsd-nsd2903-v6
title: Norwegian Citizen Panel Round 17, 2020

description: The Norwegian Citizen Panel is a platform for internet surveys of public opinion in important areas of society and politics in Norway. Participants are randomly recruited from the Norwegian population register, and they are encouraged to participate over time. The panel was fielded for the first time the fall of 2013 and as of 2017 the survey is carried out three times a year. The data set for The Norwegian Citizen Panel Wave 17, 2020  also contains variables from previous waves, the wave number is indicated by the number after "r" in the variable name. The University of Bergen owns and is responsible for the Citizen panel. The company Ideas 2 Evidence recruits respondents, produces the survey, and provides documentation of the data. Data is stored and shared by Sikt - Norwegian Agency for Shared Services in Education and Research. The different datasets of the Norwegian Citizen Panel have different ID numbers to prevent the identification of individual participants across rounds. If access to data that follows participants over time (time-series/panel data) is desired, then it must be applied for by contacting DIGSSCORE (uib.no/digsscore / digsscore@uib.no). The same applies to text data and other data that is withheld in these datasets for privacy reasons. The Norwegian Citizen Panel welcomes research proposals for survey content. More information about calls and other updates is available at www.uib.no/en/citizen

The full text for the answer options can be found in the codebook, which is located further down the page.
DOI: 10.18712/nsd-nsd3157-v1
title: Local Youth Survey 2010-2023

description: Ungdata is used by almost all Norwegian municipalities and counties to conduct surveys among students in lower and upper secondary school. The survey covers various aspects of young people's lives, such as their relationships with parents and friends, leisure activities, school, quality of life, health, substance use, and crime.

The dataset consists of individual data from students in secondary schools and has been collected in Norwegian municipalities from 2010 to 2023. The survey is conducted electronically. The questionnaire includes a mandatory core module used in all surveys and a set of optional, predefined questions that municipalities can choose from. The questionnaire for the Ungdata survey is typically conducted every three years.

Ungdata is the result of a collaborative effort between the research institute NOVA and seven regional competence centers in the field of substance abuse (KORUS). The project is funded by the Norwegian Directorate of Health through annual grants in the State Budget.

Until 2020, it is recommended to use three-year averages as national figures to reduce the impact of variations in the composition of municipalities from year to year. The 2021 survey was limited due to the COVID-19 pandemic. The surveys in 2021 and 2022 are considered nationally representative. The dataset for 2023 is not nationally representative. For more information, visit www.ungdata.no.
DOI: 10.6084/m9.figshare.24599094
title: Supplementary Material for: Early Net Ultrafiltration during Continuous Renal Replacement Therapy: Impact of Admission Diagnosis and Association with Mortality.

description: Introduction: Continuous Renal Replacement Therapy (CRRT) is common in the Intensive Care Unit (ICU) but a high Net ultrafiltration rate (UFNET) calculated with daily data may increase mortality. We aimed to study early UFNET practice using minute-by-minute CRRT machine recordings and to assess its association with admission diagnosis and mortality. Methods: We studied CRRT treatments in three adult ICUs over 7-years. We calculated early UFNET rates minute-by-minute and categorised UFNET into tertiles of mean UFNET in the first 72 hours and admission diagnosis. We applied Cox-proportional hazards modelling with censoring of patients who died within 72 hours. Results: We studied 1218 patients; 154,712 hours and 9,282,729 minutes of CRRT (5,702 circuits). Mean early UFNET was 1.52 (1.46 to 1.57) mL/kg/hr. Early UFNET tertiles were similar to previously reported values at 0.00-1.20 mL/kg/hr, 1.21 to 1.93mL/kg/hr and &gt;1.93mL/kg/hr. UFNET values were similar whether evaluated at 24 or 72 hours or for the entire duration of CRRT. There was, however, significant variation in UFNET practice by admission diagnosis: higher in respiratory diseases (pneumonia P=0.01, other P&lt;0.0001), and cardiovascular disease (P=0.005) but lower in cardiothoracic surgery (P=0.04), renal (P=0.0003) and toxicology-associated diagnoses (P=0.01). Higher UFNET was associated with an increased hazard of death, HR 1.24 (1.13 to 1.37), independent of admission diagnosis, weight, age, sex, presence of ESKD and severity of illness. Conclusion: Early UFNET practice reflects known tertiles but varies significantly by admission diagnosis. Higher early UFNET is independently associated with mortality. Impacts of UFNET on mortality may vary by admission diagnosis. Further work is required to elucidate the nature and mechanisms responsible for this association.
DOI: 10.34740/kaggle/dsv/7018419
title: 2021 World Population (updated daily)
DOI: 10.34740/kaggle/dsv/7018420
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.6084/m9.figshare.23631492.v2
title: Bothrops_alternatus_BUSCO_tetrapoda_odb10

description: BUSCO output using the tetrapoda_odb10 dataset to analyze the genome assembly of the urutu lancehead (Bothrops alternatus). <br> This data is linked to the BioSample SAMN16930333 in NCBI. <br> <br>