cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.58119/ulg/uowrsl/wbqjmo
title: mp-3954.json

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DOI: 10.58119/ulg/uowrsl/ffsfto
title: mp-5929.json

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DOI: 10.58119/ulg/uowrsl/v7ghp7
title: mp-8622.json

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DOI: 10.58119/ulg/uowrsl/fp3yxw
title: mp-556867.json

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DOI: 10.58119/ulg/uowrsl/ehvhic
title: mp-5595.json

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DOI: 10.58119/ulg/uowrsl/6d07bp
title: mp-8406.json

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DOI: 10.58119/ulg/uowrsl/whrvaa
title: mp-7923.json

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DOI: 10.58119/ulg/uowrsl/87yivi
title: mp-11266.json

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DOI: 10.58119/ulg/uowrsl/6vdp02
title: mp-9632.json

description: :unav
DOI: 10.5281/zenodo.6360650
title: Multi-aspect Integrated Migration Indicators (MIMI) dataset

description: Nowadays, new branches of research are proposing the use of non-traditional data sources for the study of migration trends in order to find an original methodology to answer open questions about cross-border human mobility. The Multi-aspect Integrated Migration Indicators (MIMI) dataset is a new dataset to be exploited in migration studies as a concrete example of this new approach. It includes both official data about bidirectional human migration (traditional flow and stock data) with multidisciplinary variables and original indicators, including economic, demographic, cultural and geographic indicators, together with the Facebook Social Connectedness Index (SCI). It is built by gathering, embedding and integrating traditional and novel variables, resulting in this new multidisciplinary dataset that could significantly contribute to nowcast/forecast bilateral migration trends and migration drivers. Thanks to this variety of knowledge, experts from several research fields (demographers, sociologists, economists) could exploit MIMI to investigate the trends in the various indicators, and the relationship among them. Moreover, it could be possible to develop complex models based on these data, able to assess human migration by evaluating related interdisciplinary drivers, as well as models able to nowcast and predict traditional migration indicators in accordance with original variables, such as the strength of social connectivity. Here, the SCI could have an important role. It measures the relative probability that two individuals across two countries are friends with each other on Facebook, therefore it could be employed as a proxy of social connections across borders, to be studied as a possible driver of migration. All in all, the motivations for building and releasing the MIMI dataset lie in the need of new perspectives, methods and analyses that can no longer prescind from taking into account a variety of new factors. The heterogeneous and multidimensional sets of data present in MIMI offer an all-encompassing overview of the characteristics of human migration, enabling a better understanding and an original potential exploration of the relationship between migration and non-traditional sources of data. The MIMI dataset is made up of one single CSV file that includes 28,821 rows (records/entries) and 876 columns (variables/features/indicators). Each row is identified uniquely by a pairs of countries, built from the joining of the two ISO-3166 alpha-2 codes for the origin and destination country, respectively. The dataset contains as main features the country-to-country bilateral migration flows and stocks, together with multidisciplinary variables measuring cultural, demographic, geographic and economic variables for the two countries, together with the Facebook strength of connectedness of each pair. <strong>Related paper: </strong>Goglia, D., Pollacci, L., Sirbu, A. (2022). Dataset of Multi-aspect Integrated Migration Indicators. https://doi.org/10.5281/zenodo.6500885
DOI: 10.5281/zenodo.6493325
title: Multi-aspect Integrated Migration Indicators (MIMI) dataset

description: Nowadays, new branches of research are proposing the use of non-traditional data sources for the study of migration trends in order to find an original methodology to answer open questions about cross-border human mobility. The Multi-aspect Integrated Migration Indicators (MIMI) dataset is a new dataset to be exploited in migration studies as a concrete example of this new approach. It includes both official data about bidirectional human migration (traditional flow and stock data) with multidisciplinary variables and original indicators, including economic, demographic, cultural and geographic indicators, together with the Facebook Social Connectedness Index (SCI). It is built by gathering, embedding and integrating traditional and novel variables, resulting in this new multidisciplinary dataset that could significantly contribute to nowcast/forecast bilateral migration trends and migration drivers. Thanks to this variety of knowledge, experts from several research fields (demographers, sociologists, economists) could exploit MIMI to investigate the trends in the various indicators, and the relationship among them. Moreover, it could be possible to develop complex models based on these data, able to assess human migration by evaluating related interdisciplinary drivers, as well as models able to nowcast and predict traditional migration indicators in accordance with original variables, such as the strength of social connectivity. Here, the SCI could have an important role. It measures the relative probability that two individuals across two countries are friends with each other on Facebook, therefore it could be employed as a proxy of social connections across borders, to be studied as a possible driver of migration. All in all, the motivations for building and releasing the MIMI dataset lie in the need of new perspectives, methods and analyses that can no longer prescind from taking into account a variety of new factors. The heterogeneous and multidimensional sets of data present in MIMI offer an all-encompassing overview of the characteristics of human migration, enabling a better understanding and an original potential exploration of the relationship between migration and non-traditional sources of data. The MIMI dataset is made up of one single CSV file that includes 28,821 rows (records/entries) and 876 columns (variables/features/indicators). Each row is identified uniquely by a pairs of countries, built from the joining of the two ISO-3166 alpha-2 codes for the origin and destination country, respectively. The dataset contains as main features the country-to-country bilateral migration flows and stocks, together with multidisciplinary variables measuring cultural, demographic, geographic and economic variables for the two countries, together with the Facebook strength of connectedness of each pair. <strong>Related paper: </strong>Goglia, D., Pollacci, L., Sirbu, A. (2022). Dataset of Multi-aspect Integrated Migration Indicators. https://doi.org/10.5281/zenodo.6500885
DOI: 10.58119/ulg/uowrsl/6zc7rk
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DOI: 10.58119/ulg/uowrsl/157fg2
title: mp-8616.json

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DOI: 10.58119/ulg/uowrsl/krsrih
title: mp-10749.json

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DOI: 10.58119/ulg/uowrsl/xisnuf
title: mp-27883.json

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DOI: 10.58119/ulg/uowrsl/xzckpm
title: mp-559093.json

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DOI: 10.58119/ulg/uowrsl/izismd
title: mp-30848.json

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title: mp-29530.json

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DOI: 10.58119/ulg/uowrsl/k5dkxb
title: mp-3079.json

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title: mp-8631.json

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DOI: 10.58119/ulg/uowrsl/xmrmyr
title: mp-23210.json

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DOI: 10.58119/ulg/uowrsl/kzkkos
title: mp-6984.json

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DOI: 10.58119/ulg/uowrsl/fqvcrt
title: mp-28267.json

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DOI: 10.58119/ulg/uowrsl/wthr5i
title: mp-22474.json

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DOI: 10.58119/ulg/uowrsl/e9q34y
title: mp-13032.json

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DOI: 10.58119/ulg/uowrsl/4etuyr
title: mp-2031.json

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DOI: 10.58119/ulg/uowrsl/1yu7z4
title: mp-24067.json

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DOI: 10.58119/ulg/uowrsl/snwvjv
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DOI: 10.58119/ulg/uowrsl/ecchel
title: mp-5214.json

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DOI: 10.58119/ulg/uowrsl/t8uwhy
title: mp-3840.json

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DOI: 10.58119/ulg/uowrsl/7fqtdk
title: mp-5049.json

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DOI: 10.58119/ulg/uowrsl/lpjuhv
title: mp-24293.json

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DOI: 10.58119/ulg/uowrsl/ucqwj7
title: mp-22324.json

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DOI: 10.58119/ulg/uowrsl/7jrksn
title: mp-28076.json

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DOI: 10.58119/ulg/uowrsl/zn5ooh
title: mp-7611.json

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DOI: 10.58119/ulg/uowrsl/tsyyc6
title: mp-3615.json

description: :unav
DOI: 10.6084/m9.figshare.22435057
title: MDPI-IJMS_Kissmann et al. 2023

description: item provides body mass, food, and fur index data of Djungarian hamsters in summer-like long photoperiod and during twelve weeks of acclimation to winter-like short photoperiod furthermore, 16S RNA Illumina sequence counts of intestinal microbiome taxa from pooled long and short photoperiod fecal samples are listed
DOI: 10.6084/m9.figshare.22435057.v1
title: MDPI-IJMS_Kissmann et al. 2023

description: item provides body mass, food, and fur index data of Djungarian hamsters in summer-like long photoperiod and during twelve weeks of acclimation to winter-like short photoperiod furthermore, 16S RNA Illumina sequence counts of intestinal microbiome taxa from pooled long and short photoperiod fecal samples are listed
DOI: 10.58119/ulg/uowrsl/ql1qo1
title: mp-5062.json

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DOI: 10.58119/ulg/uowrsl/pcy5td
title: mp-23071.json

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DOI: 10.58119/ulg/uowrsl/eehey6
title: mp-12492.json

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DOI: 10.58119/ulg/uowrsl/djzwrm
title: mp-569347.json

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DOI: 10.58119/ulg/uowrsl/r9pyvj
title: mp-558119.json

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DOI: 10.58119/ulg/uowrsl/wrbxgu
title: mp-7376.json

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DOI: 10.58119/ulg/uowrsl/zpbvxg
title: mp-9822.json

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DOI: 10.58119/ulg/uowrsl/oozdba
title: mp-1523.json

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DOI: 10.58119/ulg/uowrsl/hhfuyi
title: mp-488.json

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DOI: 10.58119/ulg/uowrsl/7d4qhi
title: mp-28112.json

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DOI: 10.58119/ulg/uowrsl/7y1rpq
title: mp-7913.json

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DOI: 10.58119/ulg/uowrsl/qeg88d
title: mp-4281.json

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DOI: 10.58119/ulg/uowrsl/6bi7xz
title: mp-8018.json

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DOI: 10.58119/ulg/uowrsl/5d4euu
title: mp-13278.json

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DOI: 10.58119/ulg/uowrsl/nevmxy
title: mp-14018.json

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DOI: 10.58119/ulg/uowrsl/zwgso9
title: mp-1191.json

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DOI: 10.58119/ulg/uowrsl/zni6wx
title: mp-23027.json

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DOI: 10.58119/ulg/uowrsl/66av1n
title: mp-546865.json

description: :unav
DOI: 10.58119/ulg/uowrsl/vqntol
title: mp-7068.json

description: :unav
DOI: 10.58119/ulg/uowrsl/kztrrw
title: mp-558072.json

description: :unav
DOI: 10.58119/ulg/uowrsl/aioc9j
title: mp-561562.json

description: :unav
DOI: 10.58119/ulg/uowrsl/z3khur
title: mp-683.json

description: :unav
DOI: 10.58119/ulg/uowrsl/y2xegr
title: mp-2694.json

description: :unav
DOI: 10.58119/ulg/uowrsl/3jxxvu
title: mp-9847.json

description: :unav
DOI: 10.58119/ulg/uowrsl/qcwntv
title: mp-22906.json

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DOI: 10.58119/ulg/uowrsl/beavey
title: mp-3449.json

description: :unav
DOI: 10.58119/ulg/uowrsl/nf56ug
title: mp-20621.json

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DOI: 10.58119/ulg/uowrsl/b0pkqf
title: mp-555167.json

description: :unav
DOI: 10.58119/ulg/uowrsl/g82klp
title: mp-7441.json

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DOI: 10.58119/ulg/uowrsl/rz7cwt
title: mp-15350.json

description: :unav
DOI: 10.58119/ulg/uowrsl/nugwzu
title: mp-10493.json

description: :unav
DOI: 10.58119/ulg/uowrsl/bsfspn
title: mp-7787.json

description: :unav
DOI: 10.34740/kaggle/dsv/5277934
title: COVID Vaccination in World (updated daily)
DOI: 10.58119/ulg/uowrsl/cdjmwy
title: mp-23138.json

description: :unav
DOI: 10.58119/ulg/uowrsl/rr3jp2
title: mp-5039.json

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DOI: 10.58119/ulg/uowrsl/azride
title: mp-28629.json

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DOI: 10.58119/ulg/uowrsl/rldmff
title: mp-22939.json

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DOI: 10.58119/ulg/uowrsl/xfxurr
title: mp-11124.json

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DOI: 10.58119/ulg/uowrsl/kt4eoa
title: mp-11020.json

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DOI: 10.58119/ulg/uowrsl/qy2wh5
title: mp-15559.json

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DOI: 10.58119/ulg/uowrsl/u9ea7h
title: mp-29679.json

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DOI: 10.58119/ulg/uowrsl/vlkaux
title: mp-5021.json

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DOI: 10.58119/ulg/uowrsl/qfbxhv
title: mp-7916.json

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DOI: 10.58119/ulg/uowrsl/l0wmcm
title: mp-29150.json

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DOI: 10.58119/ulg/uowrsl/pyv16g
title: mp-6385.json

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DOI: 10.58119/ulg/uowrsl/2gtqzi
title: mp-6990.json

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DOI: 10.58119/ulg/uowrsl/b4gfm6
title: mp-543025.json

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DOI: 10.58119/ulg/uowrsl/lu0tap
title: mp-3669.json

description: :unav
DOI: 10.58119/ulg/uowrsl/fiwgx5
title: mp-31236.json

description: :unav
DOI: 10.58119/ulg/uowrsl/3rxwhm
title: mp-2065.json

description: :unav
DOI: 10.58119/ulg/uowrsl/jmqpwh
title: mp-27370.json

description: :unav
DOI: 10.58119/ulg/uowrsl/o4ypzh
title: mp-23864.json

description: :unav
DOI: 10.58119/ulg/uowrsl/fziutx
title: mp-7470.json

description: :unav
DOI: 10.58119/ulg/uowrsl/wz2tlp
title: mp-1592.json

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DOI: 10.58119/ulg/uowrsl/h8w8yl
title: mp-9011.json

description: :unav
DOI: 10.58119/ulg/uowrsl/lcytyz
title: mp-30462.json

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DOI: 10.58119/ulg/uowrsl/l6nysd
title: mp-9779.json

description: :unav
DOI: 10.58119/ulg/uowrsl/h3n7sr
title: mp-1480.json

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DOI: 10.58119/ulg/uowrsl/udqypl
title: mp-1288.json

description: :unav
DOI: 10.58119/ulg/uowrsl/a7n7u7
title: mp-4360.json

description: :unav
DOI: 10.23670/irj.2023.128.17.2
title: Распределение клинического материала

description: Международный научно-исследовательский журнал, Выпуск 2 (128) 2023
DOI: 10.58119/ulg/uowrsl/yxx4ug
title: mp-831.json

description: :unav
DOI: 10.23670/irj.2023.128.17.3
title: Распределение пациентов по возрасту

description: Международный научно-исследовательский журнал, Выпуск 2 (128) 2023
DOI: 10.58119/ulg/uowrsl/pcth1g
title: mp-5653.json

description: :unav
DOI: 10.23670/irj.2023.128.17.4
title: Количество материала с позитивной PCR диагностикой штаммов ВПЧ

description: Международный научно-исследовательский журнал, Выпуск 2 (128) 2023
DOI: 10.58119/ulg/uowrsl/rda6kp
title: mp-7753.json

description: :unav
DOI: 10.58119/ulg/uowrsl/d5dnf3
title: mp-7864.json

description: :unav
DOI: 10.58119/ulg/uowrsl/ebartg
title: mp-29399.json

description: :unav
DOI: 10.58119/ulg/uowrsl/rq2em6
title: mp-27834.json

description: :unav
DOI: 10.58119/ulg/uowrsl/bfdp5e
title: mp-561903.json

description: :unav
DOI: 10.58119/ulg/uowrsl/e82sv4
title: mp-9777.json

description: :unav
DOI: 10.58119/ulg/uowrsl/d1zj5e
title: mp-6460.json

description: :unav
DOI: 10.58119/ulg/uowrsl/2wbv13
title: mp-23704.json

description: :unav
DOI: 10.58119/ulg/uowrsl/euzljf
title: mp-1286.json

description: :unav
DOI: 10.58119/ulg/uowrsl/wnnott
title: mp-11719.json

description: :unav
DOI: 10.58119/ulg/uowrsl/nldkgd
title: mp-555287.json

description: :unav
DOI: 10.58119/ulg/uowrsl/eog87m
title: mp-1969.json

description: :unav
DOI: 10.58119/ulg/uowrsl/7ua42p
title: mp-8980.json

description: :unav
DOI: 10.58119/ulg/uowrsl/h5lzln
title: mp-14438.json

description: :unav
DOI: 10.58119/ulg/uowrsl/qut1ze
title: mp-3971.json

description: :unav
DOI: 10.58119/ulg/uowrsl/3eozur
title: mp-224.json

description: :unav
DOI: 10.23670/irj.2023.128.30.3
title: Возрастные группы осмотренных детей

description: Международный научно-исследовательский журнал, Выпуск 2 (128) 2023
DOI: 10.58119/ulg/uowrsl/dva6rv
title: mp-13134.json

description: :unav
DOI: 10.23670/irj.2023.128.30.4
title: Распределение детей по группам здоровья

description: Международный научно-исследовательский журнал, Выпуск 2 (128) 2023
DOI: 10.58119/ulg/uowrsl/byi3kv
title: mp-1414.json

description: :unav
DOI: 10.58119/ulg/uowrsl/hvzs2m
title: mp-554156.json

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DOI: 10.58119/ulg/uowrsl/ugsfoe
title: mp-6868.json

description: :unav
DOI: 10.58119/ulg/uowrsl/ylrjz8
title: mp-3814.json

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DOI: 10.58119/ulg/uowrsl/bzhpj9
title: mp-27833.json

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DOI: 10.58119/ulg/uowrsl/rqtjhe
title: mp-685.json

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DOI: 10.58119/ulg/uowrsl/d3fahh
title: mp-23351.json

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DOI: 10.58119/ulg/uowrsl/ulphkx
title: mp-6614.json

description: :unav
DOI: 10.58119/ulg/uowrsl/laysvt
title: mp-12993.json

description: :unav
DOI: 10.6084/m9.figshare.21940031
title: Supplementary Material for: Clinical characteristics and management of patients with secondary hyperparathyroidism undergoing hemodialysis: a feasibility analysis of electronic health records using Natural Language Processing

description: Introduction: This study aimed to assess the feasibility of applying Natural Language Processing (NLP) to analyze real-world data (RWD) and resolve clinical problems in patients with secondary hyperparathyroidism and chronic kidney disease undergoing hemodialysis (SHPT/CKD-HD). The primary objective was to evaluate how well the guidelines-recommended analytical goals are achieved in a Spanish cohort of SHPT/CKD-HD patients based on RWD. Methods: Unstructured data in the EHRs from 8 hospitals were retrospectively analyzed using the EHRead® technology, based on NLP and machine learning. Variables extracted from EHRs included demographics, CKD-related clinical characteristics, comorbidities, and complications, mineral and bone disorder parameters levels and treatments at baseline, 6 months and 12 months follow up. Results: A total of 623 prevalent SHPT/CKD-HD patients were identified; of those, 282 fulfilled the inclusion criteria. They were predominantly elderly males with cardiovascular comorbidities and first cause of CKD was diabetic nephropathy. Diagnosis of SHPT was associated with an improvement in median values for parathyroid hormone (PTH), calcium and phosphate. However, the percentage of patients with normal PTH ranges remained stable during the study period (52.8% to 60.4%) while the percentage of patients with within-target range serum calcium or phosphate values showed an increasing trend (43.2% to 60% and 38.8% to 50%). At baseline, 74.1% of patients were using SHPT-related medication, including at least one Vitamin D or analogue (63.1%), phosphate binders (46.8%), and/or calcimimetics (9.6%). Conclusions: This study represents the first attempt to use clinical NLP to analyze SHPT/CKD-HD patients based on unstructured clinical data. This methodology is useful to address clinical problems based on RWD and identified a high rate of out-of-range mineral-bone analytical values in patients with HPT/CKD-HD and an increasing trend of out-of-range values for serum calcium and phosphate.
DOI: 10.58119/ulg/uowrsl/vfjiux
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title: mp-568053.json

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title: mp-541900.json

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title: mp-14040.json

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DOI: 10.58119/ulg/uowrsl/2knmat
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DOI: 10.58119/ulg/uowrsl/dtbgvh
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DOI: 10.58119/ulg/uowrsl/vftpcx
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