cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4286452
title: Chromsome number evolution in Carex (Cyperaceae)

description: These files correspond to the supplementary material of the paper published in <em>Journal of Systematics and Evolution</em> entitled "Macroevolutionary insights in sedges (<em>Carex</em>: Cyperaceae): The effects of rapid chromosome number evolution on lineage diversification" (doi: __). Data S1. Summary stats of the species with chromosome data vs. the accepted species by lineage and the most recent species with molecular data available (Roalson et al., 2020). List of taxa used in the study with chromosome number, morphological and bioclimatic data. This list contains only those taxa used in Martín-Bravo et al. (2019) that also present chromosome number reports available. Data S2. Results from chromosome number evolution models comparison using R package motmot (Harmon & Freckleton, 2008). Data S3. Zip file including the script file used for bayou analysis (Uyeda & Harmon, 2014) and the output summary and figures for chromosome number (diploid), culm length (cm), lateral inflorescence length (mm), annual mean temperature (BIO1, ºCx10), temperature seasonality (standard deviation x 100), temperature annual range (BIO7, ºCx10) and annual precipitation (BIO12, mm). Data S4. Zip file including the script file used for QuaSSE analysis (FitzJohn, 2012) and the table showing the different models results. Data S5. Log-likelihood space in function of phylogenetic signals (t1/2) and stationary variances (vy) for each studied model with slouch (Kopperud et al., 2018). Figure S1. Dendrogram for hierarchical clustering with the 19 WorldClim bioclimatic variables labeled.
DOI: 10.5281/zenodo.4449391
title: Chromsome number evolution in Carex (Cyperaceae)

description: These files correspond to the supplementary material of the paper published in <em>Journal of Systematics and Evolution</em> entitled "Macroevolutionary insights in sedges (<em>Carex</em>: Cyperaceae): The effects of rapid chromosome number evolution on lineage diversification" (doi: __). Data S1. Summary stats of the species with chromosome data vs. the accepted species by lineage and the most recent species with molecular data available (Roalson et al., 2020). List of taxa used in the study with chromosome number, morphological and bioclimatic data. This list contains only those taxa used in Martín-Bravo et al. (2019) that also present chromosome number reports available. Data S2. Results from chromosome number evolution models comparison using R package motmot (Harmon & Freckleton, 2008). Data S3. Zip file including the script file used for bayou analysis (Uyeda & Harmon, 2014) and the output summary and figures for chromosome number (diploid), culm length (cm), lateral inflorescence length (mm), annual mean temperature (BIO1, ºCx10), temperature seasonality (standard deviation x 100), temperature annual range (BIO7, ºCx10) and annual precipitation (BIO12, mm). Data S4. Zip file including the script file used for QuaSSE analysis (FitzJohn, 2012) and the table showing the different models results. Data S5. Log-likelihood space in function of phylogenetic signals (t1/2) and stationary variances (vy) for each studied model with slouch (Kopperud et al., 2018). Figure S1. Dendrogram for hierarchical clustering with the 19 WorldClim bioclimatic variables labeled.
DOI: 10.5281/zenodo.4146937
title: Chromsome number evolution in Carex (Cyperaceae)

description: These files correspond to the supplementary material of the paper published in Journal of Systematics and Evolution entitled "Macroevolutionary insights in sedges (Carex: Cyperaceae): The effects of rapid chromosome number evolution on lineage diversification" (doi: https://doi.org/10.1111/jse.12730).





Data S1. Summary stats of the species with chromosome data vs. the accepted species by lineage and the most recent species with molecular data available (Roalson et al., 2020). List of taxa used in the study with chromosome number, morphological and bioclimatic data. This list contains only those taxa used in Martín-Bravo et al. (2019) that also present chromosome number reports available.




Data S2. Results from chromosome number evolution models comparison using R package motmot (Harmon & Freckleton, 2008).




Data S3. Zip file including the script file used for bayou analysis (Uyeda & Harmon, 2014) and the output summary and figures for chromosome number (diploid), culm length (cm), lateral inflorescence length (mm), annual mean temperature (BIO1, ºCx10), temperature seasonality (standard deviation x 100), temperature annual range (BIO7, ºCx10) and annual precipitation (BIO12, mm).




Data S4. Zip file including the script file used for QuaSSE analysis (FitzJohn, 2012) and the table showing the different models results.




Data S5. Log-likelihood space in function of phylogenetic signals (t1/2) and stationary variances (vy) for each studied model with slouch (Kopperud et al., 2018).




Figure S1. Dendrogram for hierarchical clustering with the 19 WorldClim bioclimatic variables labeled.
DOI: 10.5281/zenodo.4146938
title: Chromsome number evolution in Carex (Cyperaceae)

description: These files correspond to the supplementary material of the paper published in <em>Journal of Systematics and Evolution</em> entitled "Macroevolutionary insights in sedges (<em>Carex</em>: Cyperaceae): The effects of rapid chromosome number evolution on lineage diversification" (doi: __). Data S1. Summary stats of the species with chromosome data vs. the accepted species by lineage and the most recent species with molecular data available (Roalson et al., 2020). List of taxa used in the study with chromosome number, morphological and bioclimatic data. This list contains only those taxa used in Martín-Bravo et al. (2019) that also present chromosome number reports available. Data S2. Results from chromosome number evolution models comparison using R package motmot (Harmon & Freckleton, 2008). Data S3. Zip file including the script file used for bayou analysis (Uyeda & Harmon, 2014) and the output summary and figures for chromosome number (diploid), culm length (cm), lateral inflorescence length (mm), annual mean temperature (BIO1, ºCx10), temperature seasonality (standard deviation x 100), temperature annual range (BIO7, ºCx10) and annual precipitation (BIO12, mm). Data S4. Zip file including the script file used for QuaSSE analysis (FitzJohn, 2012) and the table showing the different models results. Data S5. Log-likelihood space in function of phylogenetic signals (t1/2) and stationary variances (vy) for each studied model with slouch (Kopperud et al., 2018). Figure S1. Dendrogram for hierarchical clustering with the 19 WorldClim bioclimatic variables labeled.
DOI: 10.5281/zenodo.4450475
title: Chromsome number evolution in Carex (Cyperaceae)

description: These files correspond to the supplementary material of the paper published in Journal of Systematics and Evolution entitled "Macroevolutionary insights in sedges (Carex: Cyperaceae): The effects of rapid chromosome number evolution on lineage diversification" (doi: https://doi.org/10.1111/jse.12730).





Data S1. Summary stats of the species with chromosome data vs. the accepted species by lineage and the most recent species with molecular data available (Roalson et al., 2020). List of taxa used in the study with chromosome number, morphological and bioclimatic data. This list contains only those taxa used in Martín-Bravo et al. (2019) that also present chromosome number reports available.




Data S2. Results from chromosome number evolution models comparison using R package motmot (Harmon & Freckleton, 2008).




Data S3. Zip file including the script file used for bayou analysis (Uyeda & Harmon, 2014) and the output summary and figures for chromosome number (diploid), culm length (cm), lateral inflorescence length (mm), annual mean temperature (BIO1, ºCx10), temperature seasonality (standard deviation x 100), temperature annual range (BIO7, ºCx10) and annual precipitation (BIO12, mm).




Data S4. Zip file including the script file used for QuaSSE analysis (FitzJohn, 2012) and the table showing the different models results.




Data S5. Log-likelihood space in function of phylogenetic signals (t1/2) and stationary variances (vy) for each studied model with slouch (Kopperud et al., 2018).




Figure S1. Dendrogram for hierarchical clustering with the 19 WorldClim bioclimatic variables labeled.
DOI: 10.6084/m9.figshare.22179071.v12
title: Supplementary material of the project "Phylogenomics and chromosome mapping show that ectopic recombination of subtelomeres is critical for antigenic diversity and has a complex evolutionary history in <i>Plasmodium</i> parasites"

description: <b>Supplementary material datasets:</b><b>Dataset 1:</b> Database for phylogenetic reconstruction of the genus <i>Plasmodium.</i><b>Dataset 2:</b> Trees of the genus <i>Plasmodium</i> generated with six phylogenetic tools. The four trees generated after removing the third base of codons in the alignments and the final majority-rule consensus tree of the ten trees are also included.<b>Dataset 3:</b> Database for chromosome mapping of gene conservation.<b>Dataset 4:</b> List of predominant gene families in young regions per species.<b>Dataset 5: </b>Classification of gene families (with the highest number of sequences in young regions) based on the nature of its products.<b>Dataset 6: </b>Chromosome maps showing the gene conservation profile, presence of young regions, and distribution of antigenic genes in 19 <i>Plasmodium</i> species.<b>Dataset 7: </b>A hundred comparisons of genomic features associated with the process of generating antigenic diversity through ectopic recombination of subtelomeres among equally sized subsamples (n=4) randomly generated from <i>Plasmodium</i>, <i>Laverania</i>, and <i>Vinckeia</i>. For the subgenus <i>Plasmodium</i>, the monophyletic subclade was included in the comparisons.<b>Supplementary material:</b><b>Table S1: </b>Number of species present in each major clade and in their respective minor clades.<b>Table S2:</b> Criteria to define the four levels in which <i>Plasmodium</i> species are classified based on evidence of the intensity of production of antigenic diversity through ectopic recombination.<b>Table S3: </b>Results of normality tests. Shapiro Wilk's test was used due to the small sample size per group (n&lt;30). The statistic and p-value are shown.<b>Table S4: </b>Results of homoscedasticity tests. Levene's Test was applied to evaluate homoscedasticity when the data did not follow a normal distribution before conducting the Wilcoxon's test. The statistic and p-value are shown per test.<b>Table S5: </b>Results of Welch's and Wilcoxon's tests. When the data followed a normal distribution, Welch's test was used. Otherwise, Wilcoxon's test was applied after verifying homoscedasticity. The statistic and p-value are shown per comparison.<b>Figure S1:</b> Phylogenetic reconstruction of the genus <i>Plasmodium</i> after removing the third base of the codons in the alignments.<b>Figure S2:</b> Proportion of species-specific or young genes excluded from chromosome mapping analysis.<br>
DOI: 10.17028/rd.lboro.24942117.v1
title: Supplementary information files for Hydrothermal carbonisation of mixed agri-food waste: process optimisation and mechanistic evaluation of hydrochar inorganic chemistry

description: © the authors, CC-BY NC ND 4.0Supplementary information files for Hydrothermal carbonisation of mixed agri-food waste: process optimisation and mechanistic evaluation of hydrochar inorganic chemistryIn this study, low-quality mixed agri-food waste (MAFW) was upgraded into homogenous high-quality solid fuel (i.e., hydrochar) via hydrothermal carbonisation (HTC). Response surface methodology (RSM) and principal component analysis (PCA) were used to study the effect of temperature (190–230 °C), residence time (1–5 h) and solid loading (5–20 %) on hydrochar fuel characteristics and provide mechanistic insights into hydrochar inorganic chemistry. In addition, HTC operating conditions were optimised to maximise hydrochar yield and calorific value and minimise ash content. Results from RSM revealed that reaction temperature and solid loading had most influence on the studied responses. The process optimisation of the HTC of MAFW resulted in hydrochar yield, calorific value and ash content of 52.25 %, 24.56 MJ/kg and 6.20 % (further validated within 3 % error), respectively at optimum operating condition of ⁓212 °C, 5 h and ⁓7.8 % solid loading. Furthermore, analysis by PCA revealed solid loading had a more significant impact on the fate of inorganic elements compared to reaction temperature and residence time. The results also suggests that inorganics are less concentrated in hydrochar at low solid loading and medium reaction severity. Compared to the raw-MAFW, the likelihood of fouling and slagging during combustion was reduced in the hydrochar.<br>
DOI: 10.17028/rd.lboro.24942117
title: Supplementary information files for Hydrothermal carbonisation of mixed agri-food waste: process optimisation and mechanistic evaluation of hydrochar inorganic chemistry

description: © the authors, CC-BY NC ND 4.0Supplementary information files for Hydrothermal carbonisation of mixed agri-food waste: process optimisation and mechanistic evaluation of hydrochar inorganic chemistryIn this study, low-quality mixed agri-food waste (MAFW) was upgraded into homogenous high-quality solid fuel (i.e., hydrochar) via hydrothermal carbonisation (HTC). Response surface methodology (RSM) and principal component analysis (PCA) were used to study the effect of temperature (190–230 °C), residence time (1–5 h) and solid loading (5–20 %) on hydrochar fuel characteristics and provide mechanistic insights into hydrochar inorganic chemistry. In addition, HTC operating conditions were optimised to maximise hydrochar yield and calorific value and minimise ash content. Results from RSM revealed that reaction temperature and solid loading had most influence on the studied responses. The process optimisation of the HTC of MAFW resulted in hydrochar yield, calorific value and ash content of 52.25 %, 24.56 MJ/kg and 6.20 % (further validated within 3 % error), respectively at optimum operating condition of ⁓212 °C, 5 h and ⁓7.8 % solid loading. Furthermore, analysis by PCA revealed solid loading had a more significant impact on the fate of inorganic elements compared to reaction temperature and residence time. The results also suggests that inorganics are less concentrated in hydrochar at low solid loading and medium reaction severity. Compared to the raw-MAFW, the likelihood of fouling and slagging during combustion was reduced in the hydrochar.<br>
DOI: 10.6073/pasta/2c780710fc3e74344668b0b4d3c381ea
title: Integrated Topography and Bathymetry for the Eastern Shore of Virginia

description: Description
 This dataset integrates elevation and bathymetry data from multiple sources into a single mostly-seamless digital elevation model (DEM) that covers the entire Eastern Shore of Virginia and its surrounding coastal waters. Data sources include airborn LiDAR, VCRLTER and ODU bathymetric surveys, NOAA navigational data, NOS oceanographic surveys, USGS NED data plus contours and features from topo quad maps, and VGIN-VBMP aerial imagery.
 Proposed uses for the dataset include deriving detailed watershed boundaries, hypsometric curves, tidal prisms, 3-D physical models, and input for numerical hydrodynamic simulation models.
 The version 2.0 DEM (August 2014) is suitable for both local- and regional-scale analyses. It has a resolution matched to the LiDAR data (3.048 m. or 10 ft.), resulting in 31,152 x 46,002 cells (5.4 Gigabyte in ESRI GRID format). Users can aggregate to coarser cell resolutions as needed. The data is projected in UTM Zone 18 North coordinates relative to the WGS84 horizontal datum. All elevations are in meters relative to the NAVD88 vertical datum. Spatially modelled conversion factors between NAVD88 and local sea level datums (MSL, MLW, MHW, etc.) based on NOAA VDATUM data are available as a separate dataset (VCR13215).
 See "METHODS" for full details on data sources and integration methodologies.
DOI: 10.5519/qd.8d5n3snd
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (469 records)
DOI: 10.5281/zenodo.8178781
title: Brazilian GSM Telecom (BGSMT) mobility dataset

description: This is a GSM dataset acquired in partnership with a Brazilian telecom company that comprises several areas in diverse states in Brazil with a 15-minute granularity.The dataset contains 526.894 instances from 12 months or 350 days starting in September 2017 and finishing in September 2018 consisting of 4.545 individuals. The points were recorded in many cities in Brazil with a coarse granularity of one point every 15 minutes. No information about the users is derived from these data as the entire dataset is anonymized. Each point consists of a user sequential identification number, a pair of (latitude, and longitude), and a timestamp. 

Please cite the following papers in order to use the datasets:

T. Andrade, B. Cancela, and J. Gama, "Discovering locations and habits from human mobility data," Annals of Telecommunications, vol. 75, no. 9, pp. 505–521, 2020. 10.1007/s12243-020-00807-x (DOI)and T. Andrade, B. Cancela, and J. Gama, "From mobility data to habits and common pathways," Expert Systems, vol. 37, no. 6, p. e12627, 2020.10.1111/exsy.12627 (DOI)
DOI: 10.5281/zenodo.8178782
title: Brazilian GSM Telecom (BGSMT) mobility dataset

description: This is a GSM dataset acquired in partnership with a Brazilian telecom company that comprises several areas in diverse states in Brazil with a 15-minute granularity.The dataset contains 526.894 instances from 12 months or 350 days starting in September 2017 and finishing in September 2018 consisting of 4.545 individuals. The points were recorded in many cities in Brazil with a coarse granularity of one point every 15 minutes. No information about the users is derived from these data as the entire dataset is anonymized. Each point consists of a user sequential identification number, a pair of (latitude, and longitude), and a timestamp. 

Please cite the following papers in order to use the datasets:

T. Andrade, B. Cancela, and J. Gama, "Discovering locations and habits from human mobility data," Annals of Telecommunications, vol. 75, no. 9, pp. 505–521, 2020. 10.1007/s12243-020-00807-x (DOI)and T. Andrade, B. Cancela, and J. Gama, "From mobility data to habits and common pathways," Expert Systems, vol. 37, no. 6, p. e12627, 2020.10.1111/exsy.12627 (DOI)
DOI: 10.5281/zenodo.8349568
title: Google Location History (GLH) mobility dataset

description: This is a GPS dataset acquired from Google.

Google tracks the user’s device location through Google Maps, which also works on Android devices, the iPhone, and the web. It’s possible to see the Timeline from the user’s settings in the Google Maps app on Android or directly from the Google Timeline Website. It has detailed information such as when an individual is walking, driving, and flying. Such functionality of tracking can be enabled or disabled on demand by the user directly from the smartphone or via the website. Google has a Take Out service where the users can download all their data or select from the Google products they use the data they want to download. The dataset contains 120,847 instances from a period of 9 months or 253 unique days from February 2019 to October 2019 from a single user. The dataset comprises a pair of (latitude, and longitude), and a timestamp. All the data was delivered in a single CSV file. As the locations of this dataset are well known by the researchers, this dataset will be used as ground truth in many mobility studies.

 

Please cite the following papers in order to use the datasets:

T. Andrade, B. Cancela, and J. Gama, "Discovering locations and habits from human mobility data," Annals of Telecommunications, vol. 75, no. 9, pp. 505–521, 2020. 10.1007/s12243-020-00807-x (DOI)and T. Andrade, B. Cancela, and J. Gama, "From mobility data to habits and common pathways," Expert Systems, vol. 37, no. 6, p. e12627, 2020.10.1111/exsy.12627 (DOI)
DOI: 10.5281/zenodo.8349569
title: Google Location History (GLH) mobility dataset

description: This is a GPS dataset acquired from Google.

Google tracks the user’s device location through Google Maps, which also works on Android devices, the iPhone, and the web. It’s possible to see the Timeline from the user’s settings in the Google Maps app on Android or directly from the Google Timeline Website. It has detailed information such as when an individual is walking, driving, and flying. Such functionality of tracking can be enabled or disabled on demand by the user directly from the smartphone or via the website. Google has a Take Out service where the users can download all their data or select from the Google products they use the data they want to download. The dataset contains 120,847 instances from a period of 9 months or 253 unique days from February 2019 to October 2019 from a single user. The dataset comprises a pair of (latitude, and longitude), and a timestamp. All the data was delivered in a single CSV file. As the locations of this dataset are well known by the researchers, this dataset will be used as ground truth in many mobility studies.

 

Please cite the following papers in order to use the datasets:

T. Andrade, B. Cancela, and J. Gama, "Discovering locations and habits from human mobility data," Annals of Telecommunications, vol. 75, no. 9, pp. 505–521, 2020. 10.1007/s12243-020-00807-x (DOI)and T. Andrade, B. Cancela, and J. Gama, "From mobility data to habits and common pathways," Expert Systems, vol. 37, no. 6, p. e12627, 2020.10.1111/exsy.12627 (DOI)
DOI: 10.6084/m9.figshare.24562463
title: Optimal Subsampling via Predictive Inference

description: In the big data era, subsampling or sub-data selection techniques are often adopted to extract a fraction of informative individuals from the massive data. Existing subsampling algorithms focus mainly on obtaining a representative subset to achieve the best estimation accuracy under a given class of models. In this article, we consider a semi-supervised setting wherein a small or moderate sized “labeled” data is available in addition to a much larger sized “unlabeled” data. The goal is to sample from the unlabeled data with a given budget to obtain informative individuals that are characterized by their unobserved responses. We propose an optimal subsampling procedure that is able to maximize the diversity of the selected subsample and control the false selection rate (FSR) simultaneously, allowing us to explore reliable information as much as possible. The key ingredients of our method are the use of predictive inference for quantifying the uncertainty of response predictions and a reformulation of the objective into a constrained optimization problem. We show that the proposed method is asymptotically optimal in the sense that the diversity of the subsample converges to its oracle counterpart with FSR control. Numerical simulations and a real-data example validate the superior performance of the proposed strategy. Supplementary materials for this article are available online.