cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7728048
title: QuantMig microsimulation population projection model and migration scenarios for 31 European countries

description: This open data deposit contains the data and model code of QuantMig-Mic microsimulation population projection model for 31 European countries and accompanies deliverables D8.3: Model outputs for dissemination and D8.1: Microsimulation projection model. This Zenodo deposit contains datasets of model input (baseline population and immigration database) and output data (demography and components output tables) and model code with parameters of the Baseline scenario (termed Default in the model code) deposited in QuantMig_mic.zip file. To view the code the users must first install MODGEN software (or can view code files in Visual Studio). All scenarios share the same parameters except the immigrant population - to change the immigration assumptions the users can import immigration assumptions for any other scenario from the ImmigDataBase.csv and change it in the immigration module using the MODGEN user interface or using Visual Studio. <strong>The file structure and codebook for the data files is included in the cover note file "readme_quantmig_datasets.pdf"</strong> Detailed <strong>information about the QuantMig-Mic microsimulation model, its modules and parameters</strong>: Marois, G., Potančoková, M., González-Leonardo, M. (2023) QuantMig-Mic microsimulation population projection model. QuantMig Project Deliverable D8.2. International Institute for Applied Systems Analysis (IIASA), Austria. Available at:http://quantmig.eu/res/files/QuantMig_IIASA_Deliverable%20D8.2_forsubmission_21-07-2023_corr.pdf Instructions how to install MODGEN can be found in: Marois, G. and Potančoková, M. (2022) QuantMig-mic microsimulation tool. QuantMig Project Deliverable D8.1. International Institute for Applied Systems Analysis (IIASA). http://quantmig.eu/res/files/QuantMig_IIASA_Deliverable%20D8.1%20v1.1.pdf Detailed <strong>information about the QuantMig migration scenarios</strong> can be found in: Marois, G., Potančoková, M., González-Leonardo, M. (2023) QuantMig-Mic microsimulation population projection model. QuantMig Project Deliverable D8.2. International Institute for Applied Systems Analysis (IIASA), Austria. Available at:http://quantmig.eu/res/files/QuantMig_IIASA_Deliverable%20D8.2_forsubmission_21-07-2023_corr.pdf A <strong>guide to the datasets</strong> and the codebook can be found in: <strong>readme_quantmig_datasets.pdf</strong> <strong>Countries included in the model: </strong> Austria, Belgium, Bulgaria, Croatia, Czechia, Cyprus, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Norway, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, Switzerland, United Kingdom
DOI: 10.5281/zenodo.7728049
title: QuantMig microsimulation population projection model and migration scenarios for 31 European countries

description: This open data deposit contains the data and model code of QuantMig-Mic microsimulation population projection model for 31 European countries and accompanies deliverables D8.3: Model outputs for dissemination and D8.1: Microsimulation projection model. This Zenodo deposit contains datasets of model input (baseline population and immigration database) and output data (demography and components output tables) and model code with parameters of the Baseline scenario (termed Default in the model code) deposited in QuantMig_mic.zip file. To view the code the users must first install MODGEN software (or can view code files in Visual Studio). All scenarios share the same parameters except the immigrant population - to change the immigration assumptions the users can import immigration assumptions for any other scenario from the ImmigDataBase.csv and change it in the immigration module using the MODGEN user interface or using Visual Studio. <strong>The file structure and codebook for the data files is included in the cover note file "readme_quantmig_datasets.pdf"</strong> Detailed <strong>information about the QuantMig-Mic microsimulation model, its modules and parameters</strong>: Marois, G., Potančoková, M., González-Leonardo, M. (2023) QuantMig-Mic microsimulation population projection model. QuantMig Project Deliverable D8.2. International Institute for Applied Systems Analysis (IIASA), Austria. Available at:http://quantmig.eu/res/files/QuantMig_IIASA_Deliverable%20D8.2_forsubmission_21-07-2023_corr.pdf Instructions how to install MODGEN can be found in: Marois, G. and Potančoková, M. (2022) QuantMig-mic microsimulation tool. QuantMig Project Deliverable D8.1. International Institute for Applied Systems Analysis (IIASA). http://quantmig.eu/res/files/QuantMig_IIASA_Deliverable%20D8.1%20v1.1.pdf Detailed <strong>information about the QuantMig migration scenarios</strong> can be found in: Marois, G., Potančoková, M., González-Leonardo, M. (2023) QuantMig-Mic microsimulation population projection model. QuantMig Project Deliverable D8.2. International Institute for Applied Systems Analysis (IIASA), Austria. Available at:http://quantmig.eu/res/files/QuantMig_IIASA_Deliverable%20D8.2_forsubmission_21-07-2023_corr.pdf A <strong>guide to the datasets</strong> and the codebook can be found in: <strong>readme_quantmig_datasets.pdf</strong> <strong>Countries included in the model: </strong> Austria, Belgium, Bulgaria, Croatia, Czechia, Cyprus, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Norway, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, Switzerland, United Kingdom
DOI: 10.5281/zenodo.7872004
title: CASENet: Deep Category-Aware Semantic Edge Detection

description: <strong>Introduction</strong> This dataset includes initial and trained model weights (caffemodel files) for SBD/Cityscapes datasets which is used in our CASENet software available (https://github.com/merlresearch/CASENet). <strong>Citation</strong> If you use the data, please cite the following TR2017-100: <pre><code>@inproceedings{Yu2017jul, author = {Yu, Zhiding and Feng, Chen and Liu, Ming-Yu and Ramalingam, Srikumar}, title = {CASENet: Deep Category-Aware Semantic Edge Detection}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = 2017, month = jul, doi = {10.1109/CVPR.2017.191}, url = {https://www.merl.com/publications/TR2017-100} }</code></pre> Copyright and License<br> The CASENet dataset is released under CC-BY-SA-4.0 license. <br> All data: <pre><code>Created by Mitsubishi Electric Research Laboratories (MERL), 2017, 2023 SPDX-License-Identifier: CC-BY-SA-4.0</code></pre> <br>
DOI: 10.5281/zenodo.7872003
title: CASENet: Deep Category-Aware Semantic Edge Detection

description: <strong>Introduction</strong> This dataset includes initial and trained model weights (caffemodel files) for SBD/Cityscapes datasets which is used in our CASENet software available (https://github.com/merlresearch/CASENet). <strong>Citation</strong> If you use the data, please cite the following TR2017-100: <pre><code>@inproceedings{Yu2017jul, author = {Yu, Zhiding and Feng, Chen and Liu, Ming-Yu and Ramalingam, Srikumar}, title = {CASENet: Deep Category-Aware Semantic Edge Detection}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = 2017, month = jul, doi = {10.1109/CVPR.2017.191}, url = {https://www.merl.com/publications/TR2017-100} }</code></pre> Copyright and License<br> The CASENet dataset is released under CC-BY-SA-4.0 license. <br> All data: <pre><code>Created by Mitsubishi Electric Research Laboratories (MERL), 2017, 2023 SPDX-License-Identifier: CC-BY-SA-4.0</code></pre> <br>
DOI: 10.5517/ccdc.csd.cc27wm6z
title: CCDC 2082121: Experimental Crystal Structure Determination

description: Related Article: Guo-Li Yang, Yao Xie, Zhuo-Hao Jiao, Jian Zhao, Sheng-Li Hou, Ying Shi, Jie Han, Bin Zhao|2023|J.Mater.Chem.A|11|16255|doi:10.1039/D3TA02520A
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.cc27wm70
title: CCDC 2082122: Experimental Crystal Structure Determination

description: Related Article: Guo-Li Yang, Yao Xie, Zhuo-Hao Jiao, Jian Zhao, Sheng-Li Hou, Ying Shi, Jie Han, Bin Zhao|2023|J.Mater.Chem.A|11|16255|doi:10.1039/D3TA02520A
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.cc27wm81
title: CCDC 2082123: Experimental Crystal Structure Determination

description: Related Article: Guo-Li Yang, Yao Xie, Zhuo-Hao Jiao, Jian Zhao, Sheng-Li Hou, Ying Shi, Jie Han, Bin Zhao|2023|J.Mater.Chem.A|11|16255|doi:10.1039/D3TA02520A
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: 10.6084/m9.figshare.23870073
title: <b>Mechanical Properties Assessment of Indigenous Timber Wood Species in Southeastern Nigeria</b>

description: Provides the bending and compression test data of four timber species sourced from southeastern Nigeria: <i>Alstonia congensis</i>, <i>Ceiba pentandra</i>, <i>Milicia excelsa</i>, and <i>Terminalia superba</i>.
DOI: 10.5281/zenodo.8215619
title: lepetodrilus_elevatus_decontaminated_FSCR.fasta.gz

description: .
DOI: 10.5281/zenodo.8215618
title: lepetodrilus_elevatus_decontaminated_FSCR.fasta.gz

description: .
DOI: 10.6084/m9.figshare.23871543
title: 20230805_starrydata2

description: All data on Starrydata2, a database of experimental properties in the field of inorganic materials
DOI: 10.6084/m9.figshare.23871543.v1
title: 20230805_starrydata2

description: All data on Starrydata2, a database of experimental properties in the field of inorganic materials
DOI: 10.5281/zenodo.8215638
title: lepetodrilus_gordensis_decontaminated_FSCR.fasta.gz

description: .
DOI: 10.5281/zenodo.8215637
title: lepetodrilus_gordensis_decontaminated_FSCR.fasta.gz

description: .
DOI: 10.6084/m9.figshare.23646331
title: Conditional Particle Filters with Bridge Backward Sampling

description: Conditional particle filters (CPFs) with backward/ancestor sampling are powerful methods for sampling from the posterior distribution of the latent states of a dynamic model such as a hidden Markov model. However, the performance of these methods deteriorates with models involving weakly informative observations and/or slowly mixing dynamics. Both of these complications arise when sampling finely time-discretized continuous-time path integral models, but can occur with hidden Markov models too. Multinomial resampling, which is commonly employed with CPFs, resamples excessively for weakly informative observations and thereby introduces extra variance. Furthermore, slowly mixing dynamics render the backward/ancestor sampling steps ineffective, leading to degeneracy issues. We detail two conditional resampling strategies suitable for the weakly informative regime: the so-called “killing” resampling and the systematic resampling with mean partial order. To avoid the degeneracy issues, we introduce a generalization of the CPF with backward sampling that involves auxiliary “bridging” CPF steps that are parameterized by a blocking sequence. We present practical tuning strategies for choosing an appropriate blocking. Our experiments demonstrate that the CPF with a suitable resampling and the developed “bridge backward sampling” can lead to substantial efficiency gains in the weakly informative and slow mixing regime. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.23646331.v2
title: Conditional Particle Filters with Bridge Backward Sampling

description: Conditional particle filters (CPFs) with backward/ancestor sampling are powerful methods for sampling from the posterior distribution of the latent states of a dynamic model such as a hidden Markov model. However, the performance of these methods deteriorates with models involving weakly informative observations and/or slowly mixing dynamics. Both of these complications arise when sampling finely time-discretized continuous-time path integral models, but can occur with hidden Markov models too. Multinomial resampling, which is commonly employed with CPFs, resamples excessively for weakly informative observations and thereby introduces extra variance. Furthermore, slowly mixing dynamics render the backward/ancestor sampling steps ineffective, leading to degeneracy issues. We detail two conditional resampling strategies suitable for the weakly informative regime: the so-called “killing” resampling and the systematic resampling with mean partial order. To avoid the degeneracy issues, we introduce a generalization of the CPF with backward sampling that involves auxiliary “bridging” CPF steps that are parameterized by a blocking sequence. We present practical tuning strategies for choosing an appropriate blocking. Our experiments demonstrate that the CPF with a suitable resampling and the developed “bridge backward sampling” can lead to substantial efficiency gains in the weakly informative and slow mixing regime. Supplementary materials for this article are available online.
DOI: 10.5281/zenodo.8215703
title: FoldingNet++

description: <strong>Introduction</strong> This dataset includes training and testing datasets needed for our FoldingNet++ software available at https://merl.com/research/license/FoldingNet_Plus. <strong>Citation</strong> If you use the data, please cite the following TR2020-004: <pre><code>@article{Chen2020jan, author = {Chen, Siheng and Duan, Chaojing and Yang, Yaoqing and Feng, Chen and Li, Duanshun and Tian, Dong}, title = {Deep Unsupervised Learning of 3D Point Clouds via Graph Topology Inference and Filtering}, journal = {IEEE Transactions on Image Processing}, year = 2020, pages = {3183--3198}, month = jan, doi = {10.1109/TIP.2019.2957935}, url = {https://www.merl.com/publications/TR2020-004} }</code></pre> <strong>Copyright and License</strong> The CASENet dataset is released under CC-BY-SA-4.0 license. All data: <pre><code>Created by Mitsubishi Electric Research Laboratories (MERL), 2018-2019, 2023 SPDX-License-Identifier: CC-BY-SA-4.0</code></pre>
DOI: 10.5281/zenodo.8215704
title: FoldingNet++

description: <strong>Introduction</strong> This dataset includes training and testing datasets needed for our FoldingNet++ software available at https://merl.com/research/license/FoldingNet_Plus. <strong>Citation</strong> If you use the data, please cite the following TR2020-004: <pre><code>@article{Chen2020jan, author = {Chen, Siheng and Duan, Chaojing and Yang, Yaoqing and Feng, Chen and Li, Duanshun and Tian, Dong}, title = {Deep Unsupervised Learning of 3D Point Clouds via Graph Topology Inference and Filtering}, journal = {IEEE Transactions on Image Processing}, year = 2020, pages = {3183--3198}, month = jan, doi = {10.1109/TIP.2019.2957935}, url = {https://www.merl.com/publications/TR2020-004} }</code></pre> <strong>Copyright and License</strong> The CASENet dataset is released under CC-BY-SA-4.0 license. All data: <pre><code>Created by Mitsubishi Electric Research Laboratories (MERL), 2018-2019, 2023 SPDX-License-Identifier: CC-BY-SA-4.0</code></pre>
DOI: 10.6084/m9.figshare.23587089
title: RSeqFlow-OlivePollen3 - Picual-TFs-orthologous-to-wildoliveTFs.tsv: 'Picual' orthologues against wild olive transcription factors (TFs).

description: Tab separated list containing 'Picual' v0.61 IDs and their orthologue in <em>Olea europaea</em> var <em>sylvestris</em> transcription factors from PlantTFDB v5.0 at http://planttfdb.gao-lab.org/index_ext.php?sp=Oeu together with the corresponding TF family.
DOI: 10.6084/m9.figshare.23587089.v1
title: RSeqFlow-OlivePollen3 - Picual-TFs-orthologous-to-wildoliveTFs.tsv: 'Picual' orthologues against wild olive transcription factors (TFs).

description: Tab separated list containing 'Picual' v0.61 IDs and their orthologue in <em>Olea europaea</em> var <em>sylvestris</em> transcription factors from PlantTFDB v5.0 at http://planttfdb.gao-lab.org/index_ext.php?sp=Oeu together with the corresponding TF family.
DOI: 10.6084/m9.figshare.23587269
title: RSeqFlow-OlivePollen10 - OE-kmeans.zip: Enrichment results for k-means clusters.

description: A compressed file containing the biological processes and KEGG pathways for each of the three clusters obtained using <em>k</em>-means as tab separated lists. Columns in each file are the same as in RSeqFlow-OlivePollen8 for biological processes (files starting by <em>BP-</em>) and RSeqFlow-OlivePollen9 for KEGG pathways (files starting by <em>KEGG-</em>).
DOI: 10.6084/m9.figshare.23587254
title: RSeqFlow-OlivePollen8 - OE-PBs.zip: Enriched biological processes in time contrasts.

description: A compressed file containing the biological processes up- and down-regulated in each time contrast as a tab separated list. Columns in each file correspond to: • <em>ID</em>: the GO code. • <em>Description</em>: explanation of the <em>ID</em>. • <em>GeneRatio</em>: ratio of DEGs annotated in the <em>ID</em> term respect to the total number of DEGs in this contrast. • <em>BgRatio</em>: ratio of all genes that are annotated in this <em>ID</em> term respect to the total number of genes in the organism (i.e., <em>A. thaliana</em>). • <em>pvalue</em>: reliability of enrichment. • <em>p.adjusted</em>: Benjamini and Hochberg’s multitasking adjustment of <em>P</em>-values. • <em>qvalue</em>: John Storey’s estimation of the false discovery rate by the R package <em>qvalue</em>. • <em>GenID</em>: TAIR10 code of <em>A. thaliana</em> orthologues for DEGs belonging to the <em>ID</em> term. • <em>Count</em>: number of TAIR codes in the <em>ID</em> term. • <em>TF</em>: family of transcription factors present in the <em>ID</em> term. <br> The <em>fold enrichment</em> can be easily calculated by dividing <em>geneRatio</em> by <em>BgRatio</em>.
DOI: 10.6084/m9.figshare.23587257
title: RSeqFlow-OlivePollen9 - OE-KEGG-pathways.zip: Enriched KEGG pathways for time contrasts

description: A compressed file containing the KEGG pathways up- and down-regulated in each time contrast as a tab separated list. Columns in each file are the same as in RSeqFlow-OlivePollen8, but the <em>ID</em> column corresponds now the the <em>A. thaliana</em> pathway. No column for TFs is present.
DOI: 10.6084/m9.figshare.23587257.v1
title: RSeqFlow-OlivePollen9 - OE-KEGG-pathways.zip: Enriched KEGG pathways for time contrasts

description: A compressed file containing the KEGG pathways up- and down-regulated in each time contrast as a tab separated list. Columns in each file are the same as in RSeqFlow-OlivePollen8, but the <em>ID</em> column corresponds now the the <em>A. thaliana</em> pathway. No column for TFs is present.
DOI: 10.6084/m9.figshare.23587125
title: RSeqFlow-OlivePollen4 - TF-families-comparison.tsv: Members of TF families in <em>Arabidopsis</em>, wild olive and cv. Picual.

description: Tab separated list of the number of members of each transcription factor family as in PlantTFDB v5.0 for <em>A. thaliana</em>TAIR10, wild olive tree, cv. Picual v0.61, the 'Picual' DEGs determined in the current study, and the percent of enrichment of each TF family. The table is ordered by the last column.
DOI: 10.6084/m9.figshare.23587194
title: RSeqFlow-OlivePollen6 - unk173-ReprOlive-OeuTFs.tsv: What is known about unknown 'Picual' DEGs.

description: Tab separated list of the 173 unknown cv. Picual v0.61 IDs with their putative orthologue with a pollen transcript in ReprOlive, a transcript in reproductive tissues of ReprOlive, or to the tentative transcription factors predicted for wild olive tree (<em>Olea europaea</em> var <em>sylvestris</em>) in PlantTFDB v5.0 at http://planttfdb.gao-lab.org/index_ext.php?sp=Oeu.
DOI: 10.6084/m9.figshare.23587269.v1
title: RSeqFlow-OlivePollen10 - OE-kmeans.zip: Enrichment results for k-means clusters.

description: A compressed file containing the biological processes and KEGG pathways for each of the three clusters obtained using <em>k</em>-means as tab separated lists. Columns in each file are the same as in RSeqFlow-OlivePollen8 for biological processes (files starting by <em>BP-</em>) and RSeqFlow-OlivePollen9 for KEGG pathways (files starting by <em>KEGG-</em>).
DOI: 10.6084/m9.figshare.23587125.v1
title: RSeqFlow-OlivePollen4 - TF-families-comparison.tsv: Members of TF families in <em>Arabidopsis</em>, wild olive and cv. Picual.

description: Tab separated list of the number of members of each transcription factor family as in PlantTFDB v5.0 for <em>A. thaliana</em>TAIR10, wild olive tree, cv. Picual v0.61, the 'Picual' DEGs determined in the current study, and the percent of enrichment of each TF family. The table is ordered by the last column.
DOI: 10.6084/m9.figshare.23587128
title: RSeqFlow-OlivePollen5 - Annotation extension for Picual DEGs with more TAIR10 orthologues. Two files: a) DEG-annot-extension.pdf and b) orthology-Picual-TAIR10-extended-230608.tsv

description: a) The <strong>PDF</strong> contains the biscussion about the results obtained with the extended annotation of DEGs using <em>DIAMOND</em> to assign a TAIR 10 orthologue to unnanotated Picual transcripts. <br> b) The <strong>tsv file </strong>is a tab separated list of Picual v0.61 IDs with TAIR10 IDs of <em>A. thaliana</em> using the orthology downloaded from OliveTreeDB (https://genomaolivar.dipujaen.es/downloads/Annotations/Olea_europaea_v061_annotations.xlsx) extended for the TAIR10 IDs found using <em>DIAMOND</em>.
DOI: 10.6084/m9.figshare.19782403
title: Mechanistically-grounded pathways connect remotely sensed canopy structure to soil respiration plot summary

description: A plot-level summary of the variables used for statisical analysis and figure preparation, including variables describing remotely-sensed canopy VAI and rugosity, root structure as root density and root complexity/rugosity, soil microclimate, soil respiration rates, and stand and plot soil characteristics.
DOI: 10.6084/m9.figshare.19782403.v1
title: Mechanistically-grounded pathways connect remotely sensed canopy structure to soil respiration plot summary

description: A plot-level summary of the variables used for statisical analysis and figure preparation, including variables describing remotely-sensed canopy VAI and rugosity, root structure as root density and root complexity/rugosity, soil microclimate, soil respiration rates, and stand and plot soil characteristics.
DOI: 10.6084/m9.figshare.19782397
title: Mechanistically-grounded pathways connect remotely sensed canopy structure to soil respiration R Project

description: An R Project containing all data and R scripts used for data summarization, statistical analysis, and preparation of figures 2 and 3 in the associated manuscript<em> </em>
DOI: 10.6084/m9.figshare.19782397.v1
title: Mechanistically-grounded pathways connect remotely sensed canopy structure to soil respiration R Project

description: An R Project containing all data and R scripts used for data summarization, statistical analysis, and preparation of figures 2 and 3 in the associated manuscript<em> </em>
DOI: 10.25345/c5tm72b42
title: MassIVE MSV000092593 - QC mix; quality control mix of 6 compounds trial3
title: This is another test of the quality control text mix. This is not really useful for anybody but me.