cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.18712/nsd-nsd2605-v7
title: Norwegian Citizen Panel Round 12, 2018

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 12, 2018 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 ideas2evidence 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 are 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-nsd2664-v7
title: Norwegian Citizen Panel Round 13, 2018

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 13, 2018 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 ideas2evidence 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 are 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-nsd3161-v1
title: Level of Living Survey on Working Conditions 2022

description: Statistics Norway has carried out level of living surveys since 1973, and from 1996 surveys have been carried out annually. The Level of Living Survey on the topic of working conditions had previously been carried out in 1996, 2000, 2003, 2006, 2009, 2013, 2016 and 2019. The Level of Living Survey on Working Conditions aims to map different working environment conditions among employees in Norway. The survey now takes place every 3 years, and covers topics such as attachment to the workplace, physical, chemical and ergonomic working environment, psychosocial working environment, work-related health problems and sickness absence and requirements and opportunities for self-determination at work.

For the Level of Living Survey on Working Conditions 2022, a nationally representative sample of approximately 35 000 people aged 18-66 was drawn. Data was collected using telephone interviews and a self-administered web form in the period August 2022 to April 2023. In Statistics Norway, the Division for income and living conditions statistics has the professional responsibility for the survey, the data collection is carried out by the Division for personal surveys.
DOI: 10.5517/ccdc.csd.cc1q3lt7
title: CCDC 1582389: Experimental Crystal Structure Determination

description: Related Article: Zehai Lu|2017|CSD Communication|||
description: Related Article: Zehai Lu, Shihan Liu, Yu Lan, Xuebing Leng, Qilong Shen|2021|Organometallics|40|1713|doi:10.1021/acs.organomet.1c00195
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: 10.25505/fiz.icsd.cc1xkz2j
title: CSD 1774938: Experimental Crystal Structure Determination

description: Related Article: Koppula Suresh, Manabolu Surya Surendra Babu, Poonam Jagasia|2024|Sep.Purif.Technol.|335|126052|doi:10.1016/j.seppur.2023.126052
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.18434/mds2-2938
title: Dynamic Range by Design in OTA EVM measurements
DOI: 10.6084/m9.figshare.21858501
title: Low Frequency Cointegrating Regression with Local to Unity Regressors and Unknown Form of Serial Dependence

description: This article develops new <i>t</i> and <i>F</i> tests in a low-frequency transformed triangular cointegrating regression when one may not be certain that the economic variables are exact unit root processes. We first show that the low-frequency transformed and augmented OLS (TA-OLS) method exhibits an asymptotic bias term in its limiting distribution. As a result, the test for the cointegration vector can have substantially large size distortion, even with minor deviations from the unit root regressors. To correct the asymptotic bias of the TA-OLS statistics for the cointegration vector, we develop modified TA-OLS statistics that adjust the bias and take account of the estimation uncertainty of the long-run endogeneity arising from the bias correction. Based on the modified test statistics, we provide Bonferroni-based tests of the cointegration vector using standard <i>t</i> and <i>F</i> critical values. Monte Carlo results show that our approach has the correct size and reasonable power for a wide range of local-to-unity parameters. Additionally, our method has advantages over the IVX approach when the serial dependence and the long-run endogeneity in the cointegration system are important.
DOI: 10.6084/m9.figshare.21858501.v3
title: Low Frequency Cointegrating Regression with Local to Unity Regressors and Unknown Form of Serial Dependence

description: This article develops new <i>t</i> and <i>F</i> tests in a low-frequency transformed triangular cointegrating regression when one may not be certain that the economic variables are exact unit root processes. We first show that the low-frequency transformed and augmented OLS (TA-OLS) method exhibits an asymptotic bias term in its limiting distribution. As a result, the test for the cointegration vector can have substantially large size distortion, even with minor deviations from the unit root regressors. To correct the asymptotic bias of the TA-OLS statistics for the cointegration vector, we develop modified TA-OLS statistics that adjust the bias and take account of the estimation uncertainty of the long-run endogeneity arising from the bias correction. Based on the modified test statistics, we provide Bonferroni-based tests of the cointegration vector using standard <i>t</i> and <i>F</i> critical values. Monte Carlo results show that our approach has the correct size and reasonable power for a wide range of local-to-unity parameters. Additionally, our method has advantages over the IVX approach when the serial dependence and the long-run endogeneity in the cointegration system are important.
DOI: 10.6084/m9.figshare.21983217
title: A One-Sided Refined Symmetrized Data Aggregation Approach to Robust Mutual Fund Selection

description: We consider the problem of identifying skilled funds among a large number of candidates under the linear factor pricing models containing both observable and latent market factors. Motivated by the existence of non-strong potential factors and diversity of error distribution types of the linear factor pricing models, we develop a distribution-free multiple testing procedure to solve this problem. The proposed procedure is established based on the statistical tool of symmetrized data aggregation, which makes it robust to the strength of potential factors and distribution type of the error terms. We then establish the asymptotic validity of the proposed procedure in terms of both the false discovery rate and true discovery proportion under some mild regularity conditions. Furthermore, we demonstrate the advantages of the proposed procedure over some existing methods through extensive Monte Carlo experiments. In an empirical application, we illustrate the practical utility of the proposed procedure in the context of selecting skilled funds, which clearly has much more satisfactory performance than its main competitors.
DOI: 10.6084/m9.figshare.21866064
title: Bayesian Nonparametric Panel Markov-Switching GARCH Models

description: This article proposes Bayesian nonparametric inference for panel Markov-switching GARCH models. The model incorporates series-specific hidden Markov chain processes that drive the GARCH parameters. To cope with the high-dimensionality of the parameter space, the article assumes soft parameter pooling through a hierarchical prior distribution and introduces cross sectional clustering through a Bayesian nonparametric prior distribution. An MCMC posterior approximation algorithm is developed and its efficiency is studied in simulations under alternative settings. An empirical application to financial returns data in the United States is offered with a portfolio performance exercise based on forecasts. A comparison shows that the Bayesian nonparametric panel Markov-switching GARCH model provides good forecasting performances and economic gains in optimal asset allocation.
DOI: 10.6084/m9.figshare.21983217.v1
title: A one-sided refined symmetrized data aggregation approach to robust mutual fund selection

description: We consider the problem of identifying skilled funds among a large number of candidates under the linear factor pricing models containing both observable and latent market factors. Motivated by the existence of non-strong potential factors and diversity of error distribution types of the linear factor pricing models, we develop a distribution-free multiple testing procedure to solve this problem. The proposed procedure is established based on the statistical tool of symmetrized data aggregation, which makes it robust to the strength of potential factors and distribution type of the error terms. We then establish the asymptotic validity of the proposed procedure in terms of both the false discovery rate and true discovery proportion under some mild regularity conditions. Furthermore, we demonstrate the advantages of the proposed procedure over some existing methods through extensive Monte Carlo experiments. In an empirical application, we illustrate the practical utility of the proposed procedure in the context of selecting skilled funds, which clearly has much more satisfactory performance than its main competitors.
DOI: 10.6084/m9.figshare.21983217.v2
title: A One-Sided Refined Symmetrized Data Aggregation Approach to Robust Mutual Fund Selection

description: We consider the problem of identifying skilled funds among a large number of candidates under the linear factor pricing models containing both observable and latent market factors. Motivated by the existence of non-strong potential factors and diversity of error distribution types of the linear factor pricing models, we develop a distribution-free multiple testing procedure to solve this problem. The proposed procedure is established based on the statistical tool of symmetrized data aggregation, which makes it robust to the strength of potential factors and distribution type of the error terms. We then establish the asymptotic validity of the proposed procedure in terms of both the false discovery rate and true discovery proportion under some mild regularity conditions. Furthermore, we demonstrate the advantages of the proposed procedure over some existing methods through extensive Monte Carlo experiments. In an empirical application, we illustrate the practical utility of the proposed procedure in the context of selecting skilled funds, which clearly has much more satisfactory performance than its main competitors.
DOI: 10.6084/m9.figshare.21866064.v2
title: Bayesian Nonparametric Panel Markov-Switching GARCH Models

description: This article proposes Bayesian nonparametric inference for panel Markov-switching GARCH models. The model incorporates series-specific hidden Markov chain processes that drive the GARCH parameters. To cope with the high-dimensionality of the parameter space, the article assumes soft parameter pooling through a hierarchical prior distribution and introduces cross sectional clustering through a Bayesian nonparametric prior distribution. An MCMC posterior approximation algorithm is developed and its efficiency is studied in simulations under alternative settings. An empirical application to financial returns data in the United States is offered with a portfolio performance exercise based on forecasts. A comparison shows that the Bayesian nonparametric panel Markov-switching GARCH model provides good forecasting performances and economic gains in optimal asset allocation.
DOI: 10.17630/d54ca921-ef39-44bc-8522-4696f2234947
title: The type I-G CRISPR system: mechanism, structure and application (thesis data)
DOI: 10.6084/m9.figshare.24936831
title: Data from: Semi-natural habitats mitigate early behavioral maturation of honey bees in farmlands

description: This dataset result from the monitoring of the life history of many honey bee individuals in an intensive agricultural area in western France, consisting of temporally and spatially fluctuating floral resources, using radio-frequency identification (RFID). Overall, we monitored the lifespan (LSP), age at first exit (AFE), foraging tenure, and foraging intensity of 1035 honey bees along a landscape gradient of semi-natural habitats in farmlands. We found a clear acceleration of behavioral maturation of bees during the period of food shortage, with precocity in AFE between 6 and 10 days earlier and reduced LSP by 5 to 9 days, with no effect on foraging tenure or foraging intensity. We also found that higher proportions of semi-natural habitats mitigated behavioral maturation of bees by up to 6 days. Beyond the direct effects on adult bees, we found no delayed effect of larval feeding status on adult life-history traits or foraging behavior. Nevertheless, our results suggest that promoting the conservation of semi-natural habitats in intensive agricultural landscapes could benefit honey bee colony survival.The data are related to the scientific paper "Verrier E., et al. (under review) Semi-natural habitats mitigate early behavioral maturation of honey bees in farmlands. PNAS".Data are available as a RData file titled "dataTHV.RData".# 'data.frame' : 1035 obs. of 9 variables :$site : unique alphanumeric ID of the hive location $id_abeille_sc : unique alphanumeric ID of the bee$season : period of bee introduction ('April', 'May', 'June' or 'July') $typeB : hive origin of the bee ('ex_situ' from the control colony or 'in_situ' from the in situ colony) $LSP : lifespan of the bee (in days) $AFE : age of first exit of the bee (in days)$FSP : foraging tenure of the bee (in days) $FI : foraging intensity of the bee (in hours) $Semi.Nartural_occupancy_rate : proportion of semi-natural habitats within a radius of 1.5 km around the hive (in percentage)
DOI: 10.6084/m9.figshare.24936831.v1
title: Data from: Semi-natural habitats mitigate early behavioral maturation of honey bees in farmlands

description: This dataset result from the monitoring of the life history of many honey bee individuals in an intensive agricultural area in western France, consisting of temporally and spatially fluctuating floral resources, using radio-frequency identification (RFID). Overall, we monitored the lifespan (LSP), age at first exit (AFE), foraging tenure, and foraging intensity of 1035 honey bees along a landscape gradient of semi-natural habitats in farmlands. We found a clear acceleration of behavioral maturation of bees during the period of food shortage, with precocity in AFE between 6 and 10 days earlier and reduced LSP by 5 to 9 days, with no effect on foraging tenure or foraging intensity. We also found that higher proportions of semi-natural habitats mitigated behavioral maturation of bees by up to 6 days. Beyond the direct effects on adult bees, we found no delayed effect of larval feeding status on adult life-history traits or foraging behavior. Nevertheless, our results suggest that promoting the conservation of semi-natural habitats in intensive agricultural landscapes could benefit honey bee colony survival.The data are related to the scientific paper "Verrier E., et al. (under review) Semi-natural habitats mitigate early behavioral maturation of honey bees in farmlands. PNAS".Data are available as a RData file titled "dataTHV.RData".# 'data.frame' : 1035 obs. of 9 variables :$site : unique alphanumeric ID of the hive location $id_abeille_sc : unique alphanumeric ID of the bee$season : period of bee introduction ('April', 'May', 'June' or 'July') $typeB : hive origin of the bee ('ex_situ' from the control colony or 'in_situ' from the in situ colony) $LSP : lifespan of the bee (in days) $AFE : age of first exit of the bee (in days)$FSP : foraging tenure of the bee (in days) $FI : foraging intensity of the bee (in hours) $Semi.Nartural_occupancy_rate : proportion of semi-natural habitats within a radius of 1.5 km around the hive (in percentage)
DOI: 10.5281/zenodo.10455388
title: Blastn results of Haemophilus Influenzae Vs Human Hg38 genome sequences – is it the 'Bumblebee'?

description: Aims: To study the overlapping Blastn alignments between Haemophilus influenzae and human genome sequences. Hemophilus influenzae, a Gram-negative coccobacillus, is a common commensal predominantly in the upper respiratory tract, and an opportunistic pathogen. It is assocaited with various types of infections.

Methods: Blastn was used to study the overlaps between the complete genome sequences of Haemophilus influenza (L42043, Rd KW20) and the Human Hg38 genome. Based on the expect values the results are arranged in the order of significance.

Results: There were many alignments or overlaps between the Hemophilus influenza and human genome sequences. The alignments were predominantly of short sequences with high expect scores. The results are given in the accompanying supplement of this abstract.

Conclusion: The common commensal and pathogen – Haemophilus influenzae has overlapping Blastn alignments with human genome sequences, and the clinical significance of the results has to be studied.
DOI: 10.5281/zenodo.10455389
title: Blastn results of Haemophilus Influenzae Vs Human Hg38 genome sequences – is it the 'Bumblebee'?

description: Aims: To study the overlapping Blastn alignments between Haemophilus influenzae and human genome sequences. Hemophilus influenzae, a Gram-negative coccobacillus, is a common commensal predominantly in the upper respiratory tract, and an opportunistic pathogen. It is assocaited with various types of infections.

Methods: Blastn was used to study the overlaps between the complete genome sequences of Haemophilus influenza (L42043, Rd KW20) and the Human Hg38 genome. Based on the expect values the results are arranged in the order of significance.

Results: There were many alignments or overlaps between the Hemophilus influenza and human genome sequences. The alignments were predominantly of short sequences with high expect scores. The results are given in the accompanying supplement of this abstract.

Conclusion: The common commensal and pathogen – Haemophilus influenzae has overlapping Blastn alignments with human genome sequences, and the clinical significance of the results has to be studied.
DOI: 10.6084/m9.figshare.24936903.v1
title: 20240104_starrydata2

description: All data on Starrydata2, a database of experimental properties in the field of inorganic materials
DOI: 10.6084/m9.figshare.24936903
title: 20240104_starrydata2

description: All data on Starrydata2, a database of experimental properties in the field of inorganic materials
DOI: 10.5281/zenodo.10455612
title: Functional analysis of single enzymes_Figure 6 H2O2 treatment

description: Data extracted from droplet microscopy images for all 9 frames (experiment GuGi546, H2O2 treatment of Nt.BstNBI enzyme, Figure 6). 

To be imported in Mathematica

Each element of the list corresponds to one droplet with data being concatenated in the following order: 

i) droplet feature ("XY coordinate, Radius (in pixel), Circularity")

ii) Raw fluorescence (Cascade Blue, Texas Red, Alexa 488, Atto633)

iii) Background-subtracted Fluorescence 

iv) Normalized fluorescence
DOI: 10.5281/zenodo.10455613
title: Functional analysis of single enzymes_Figure 6 H2O2 treatment

description: Data extracted from droplet microscopy images for all 9 frames (experiment GuGi546, H2O2 treatment of Nt.BstNBI enzyme, Figure 6). 

To be imported in Mathematica

Each element of the list corresponds to one droplet with data being concatenated in the following order: 

i) droplet feature ("XY coordinate, Radius (in pixel), Circularity")

ii) Raw fluorescence (Cascade Blue, Texas Red, Alexa 488, Atto633)

iii) Background-subtracted Fluorescence 

iv) Normalized fluorescence
DOI: 10.5281/zenodo.10455341
title: encode.tfds.tar.xz
DOI: 10.5281/zenodo.10455340
title: encode.tfds.tar.xz
DOI: 10.34740/kaggle/dsv/7331599
title: Mahanagar Yatayat Transport Dataset
DOI: 10.34740/kaggle/dsv/7331618
title: MoviesDatasets
DOI: 10.5281/zenodo.10455721
title: test

description: This is my first upload
DOI: 10.5281/zenodo.10455722
title: test

description: This is my first upload
DOI: 10.5281/zenodo.10452994
title: Sherlock RSS-related files backup (Data, glgc2021 Part 9)
DOI: 10.5281/zenodo.10452995
title: Sherlock RSS-related files backup (Data, glgc2021 Part 9)
DOI: 10.5281/zenodo.10455734
title: Russian prefixed verbs as constructional schema
DOI: 10.5281/zenodo.10455735
title: Russian prefixed verbs as constructional schema
DOI: 10.25810/s7n-vh24.46
title: African Newspaper Coverage of Climate Change or Global Warming, 2004-2023 November 2023

description: The Media and Climate Change Observatory Data monitors 117 sources (across newspapers, radio and TV) in 55 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://sciencepolicy.colorado.edu/media_coverage.
DOI: 10.17630/14390579-8a5f-45a8-ad79-090254af1327
title: Chimpanzee (Pan troglodytes) cognitive mechanisms for joint action and virtual environment navigation (thesis data)

description: Data files for data underpinning the thesis (excel sheets)