cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.11924496.v1
title: Additional file 13 of Evolution of metabolic capabilities and molecular features of diplonemids, kinetoplastids, and euglenids

description: Additional file 13: Tables S13-S51. Estimates of evolutionary divergence among putative sequences of the following classes: KKTs (Table S13-S31), KKIPs (Tables S32-S38), centromeric histone H3 (Table S39) and enzymes of trypanothione biosynthesis and utilization (Tables S41-S51).
DOI: 10.6084/m9.figshare.11924406
title: Additional file 5 of Genome-wide discovery, and computational and transcriptional characterization of an AIG gene family in the freshwater snail Biomphalaria glabrata, a vector for Schistosoma mansoni

description: Additional file 5: Table S4. Summary of AIG gene expression in Biomphalaria species from all related studies There are five relevant expression datasets available. I). AIG genes expression in 12 different organs from unstimulated BB02 B. glabrata; II). a microarray study of schistosome resistant BS-90 B. glabrata injected with different PAMPs, including LPS, PGN, and FCN; III). An RNA-Seq transcriptomics study for field-derived Kenyan B. pfeifferi exposed for 1, 3, or 30+ days to S. mansoni, relative to unexposed controls; IV). An RNA-Seq study for S. mansoni-resistant BS-90 snails relative to susceptible M line snails at constitutive level (no exposure to S. mansoni), and V) for both M line and BS-90- snails, intrastrain comparisons of snails exposed for 0.5, 2, 8- and 40-day to S. mansoni, relative to time-matched unexposed controls.
DOI: 10.6084/m9.figshare.11924409
title: Additional file 6 of Genome-wide discovery, and computational and transcriptional characterization of an AIG gene family in the freshwater snail Biomphalaria glabrata, a vector for Schistosoma mansoni

description: Additional file 6: File S1. Multiple sequence alignment of AIG1 domains from selected eukaryotes.
DOI: 10.6084/m9.figshare.11924406.v1
title: Additional file 5 of Genome-wide discovery, and computational and transcriptional characterization of an AIG gene family in the freshwater snail Biomphalaria glabrata, a vector for Schistosoma mansoni

description: Additional file 5: Table S4. Summary of AIG gene expression in Biomphalaria species from all related studies There are five relevant expression datasets available. I). AIG genes expression in 12 different organs from unstimulated BB02 B. glabrata; II). a microarray study of schistosome resistant BS-90 B. glabrata injected with different PAMPs, including LPS, PGN, and FCN; III). An RNA-Seq transcriptomics study for field-derived Kenyan B. pfeifferi exposed for 1, 3, or 30+ days to S. mansoni, relative to unexposed controls; IV). An RNA-Seq study for S. mansoni-resistant BS-90 snails relative to susceptible M line snails at constitutive level (no exposure to S. mansoni), and V) for both M line and BS-90- snails, intrastrain comparisons of snails exposed for 0.5, 2, 8- and 40-day to S. mansoni, relative to time-matched unexposed controls.
DOI: 10.6084/m9.figshare.11924409.v1
title: Additional file 6 of Genome-wide discovery, and computational and transcriptional characterization of an AIG gene family in the freshwater snail Biomphalaria glabrata, a vector for Schistosoma mansoni

description: Additional file 6: File S1. Multiple sequence alignment of AIG1 domains from selected eukaryotes.
DOI: 10.6084/m9.figshare.11924400
title: Additional file 4 of Genome-wide discovery, and computational and transcriptional characterization of an AIG gene family in the freshwater snail Biomphalaria glabrata, a vector for Schistosoma mansoni

description: Additional file 4: Table S3. Overview of the 14 snail groups considered and sequencing information for the RNA-Seq study Two strains of B. glabrata snails were used for this study: schistosome-resistant BS-90 and -susceptible M line snails were sampled at 0.5, 2-, 8- and 40-day post-exposure to S. mansoni. Time-matched unexposed BS-90 and M line were sampled as control for differential expression analysis. Totals of 3~6 biological replicates for each group with raw reads (9â€“18 million reads/snail sample) were obtained.
DOI: 10.6084/m9.figshare.11924394
title: Additional file 3 of Genome-wide discovery, and computational and transcriptional characterization of an AIG gene family in the freshwater snail Biomphalaria glabrata, a vector for Schistosoma mansoni

description: Additional file 3: Table S2. Features of the predicted AIG footprint in B. glabrata The 111 AIG genes (complete and incomplete) and the 148 proteins were detailed, with types of AIG gene, predicted domain architecture, coiled-coil, multimer forms, and genome location.
DOI: 10.6084/m9.figshare.11924400.v1
title: Additional file 4 of Genome-wide discovery, and computational and transcriptional characterization of an AIG gene family in the freshwater snail Biomphalaria glabrata, a vector for Schistosoma mansoni

description: Additional file 4: Table S3. Overview of the 14 snail groups considered and sequencing information for the RNA-Seq study Two strains of B. glabrata snails were used for this study: schistosome-resistant BS-90 and -susceptible M line snails were sampled at 0.5, 2-, 8- and 40-day post-exposure to S. mansoni. Time-matched unexposed BS-90 and M line were sampled as control for differential expression analysis. Totals of 3~6 biological replicates for each group with raw reads (9â€“18 million reads/snail sample) were obtained.
DOI: 10.6084/m9.figshare.11924385
title: Additional file 2 of Genome-wide discovery, and computational and transcriptional characterization of an AIG gene family in the freshwater snail Biomphalaria glabrata, a vector for Schistosoma mansoni

description: Additional file 2: Table S1. Consensus sequences of motifs within AIG1 domain among selected organisms. G1, G2, G3, CB, G4, G5/IAN are conserved motifs within AIG1 domain. The consensus sequences of these motifs in different organisms may differ or be absent.
DOI: 10.6084/m9.figshare.11924394.v1
title: Additional file 3 of Genome-wide discovery, and computational and transcriptional characterization of an AIG gene family in the freshwater snail Biomphalaria glabrata, a vector for Schistosoma mansoni

description: Additional file 3: Table S2. Features of the predicted AIG footprint in B. glabrata The 111 AIG genes (complete and incomplete) and the 148 proteins were detailed, with types of AIG gene, predicted domain architecture, coiled-coil, multimer forms, and genome location.
DOI: 10.6084/m9.figshare.11924385.v1
title: Additional file 2 of Genome-wide discovery, and computational and transcriptional characterization of an AIG gene family in the freshwater snail Biomphalaria glabrata, a vector for Schistosoma mansoni

description: Additional file 2: Table S1. Consensus sequences of motifs within AIG1 domain among selected organisms. G1, G2, G3, CB, G4, G5/IAN are conserved motifs within AIG1 domain. The consensus sequences of these motifs in different organisms may differ or be absent.
DOI: 10.6084/m9.figshare.11924367
title: Additional file 3 of Modelling the impact of MAUP on environmental drivers for Schistosoma japonicum prevalence

description: Additional file 3: Table S2. Regression coefficient estimates, variance of spatial random effect, correlation decay coefficient and deviance information criteria for each risk factor at five descending spatial supports of analysis.
DOI: 10.6084/m9.figshare.11924367.v1
title: Additional file 3 of Modelling the impact of MAUP on environmental drivers for Schistosoma japonicum prevalence

description: Additional file 3: Table S2. Regression coefficient estimates, variance of spatial random effect, correlation decay coefficient and deviance information criteria for each risk factor at five descending spatial supports of analysis.
DOI: 10.6084/m9.figshare.11924361
title: Additional file 2 of Modelling the impact of MAUP on environmental drivers for Schistosoma japonicum prevalence

description: Additional file 2: Text S1. Code for the convolution model used in OpenBUGS. It includes the prior distributions used for the covariate and spatial parameters and the model itself. The model uses two indexes, k for the barangay and j for the number of city pixels within barangays. The betas are the individual-level regression coefficients, m is the number of city pixels in a barangay. Covariates are ndvi: normalized difference vegetation index, ndwi: normalized difference water index, lstd: land surface temperature day, lstn: land surface temperature night, e: elevation, ndwb: nearest distance to water bodies. The spatial parameter is represented as s.
DOI: 10.6084/m9.figshare.11924358
title: Additional file 1 of Modelling the impact of MAUP on environmental drivers for Schistosoma japonicum prevalence

description: Additional file 1: Table S1. Survey data aggregated at the barangay level. These data show the number of positive cases (y) and the total number of sampled people (n) in a barangay (k).
DOI: 10.6084/m9.figshare.11924361.v1
title: Additional file 2 of Modelling the impact of MAUP on environmental drivers for Schistosoma japonicum prevalence

description: Additional file 2: Text S1. Code for the convolution model used in OpenBUGS. It includes the prior distributions used for the covariate and spatial parameters and the model itself. The model uses two indexes, k for the barangay and j for the number of city pixels within barangays. The betas are the individual-level regression coefficients, m is the number of city pixels in a barangay. Covariates are ndvi: normalized difference vegetation index, ndwi: normalized difference water index, lstd: land surface temperature day, lstn: land surface temperature night, e: elevation, ndwb: nearest distance to water bodies. The spatial parameter is represented as s.
DOI: 10.6084/m9.figshare.11924358.v1
title: Additional file 1 of Modelling the impact of MAUP on environmental drivers for Schistosoma japonicum prevalence

description: Additional file 1: Table S1. Survey data aggregated at the barangay level. These data show the number of positive cases (y) and the total number of sampled people (n) in a barangay (k).
DOI: 10.6084/m9.figshare.11924280
title: Assembly

description: De novo assembled transcripts via Trinity.
DOI: 10.6084/m9.figshare.11924142
title: Lagrangian Plankton Model

description: Lagrangian Plankton Model (LPM) simulation result, which is used for the research about the spring bloom of phytoplankton, is attached as netCDF and dat files. These files are divided into two format. First, the profile datas includes the horizontal averaged vertical-time series data of the ocean mixed layer. Second, the particle datas includes the vertical position(Z) and phytoplankton concentration(CHL) distribution of all Lagrangian particles (90,000 particles) at -1 day (CON) and 10 day (CON, SW, EQ) cases respectively.<br>This simulation is conducted by using the PALM, which is the large eddy simulation (LES) model developed by <em> Leibniz University Hannover </em>and<em> </em> Lagrangian Phytoplankton code is uploaded on the github. Basic setup for Langmuir circulation forcing is referred by Noh et al. (2004), oceanic heat flux condition is referred by Goh & Noh (2013) and light penetration scheme is adapted from Manniza et al. (2005). Basic concept of LPM code is originated from NP (Nutrient Plankton) model.<br>Three different cases are simulated by this code with different latitude and friction velocity : control experiment (CON : lat = 40<sup>o</sup> & u* = 0.01 m/s), strong wind experiment (SW : lat = 40<sup>o</sup> & u* = 0.02 m/s), and equatorial experiment (SW : lat = 00<sup>o</sup> & u* = 0.01 m/s) to investigate the how the spring bloom is occurred. and what controls the spring bloom. <br><br>LES : https://palm.muk.uni-hannover.de/tracLPM : https://github.com/blackcata/MOD_plankton_model<br>
DOI: 10.6084/m9.figshare.11924280.v1
title: Assembly

description: De novo assembled transcripts via Trinity.
DOI: 10.6084/m9.figshare.11924142.v1
title: Lagrangian Plankton Model

description: Lagrangian Plankton Model (LPM) simulation result, which is used for the research about the spring bloom of phytoplankton, is attached as netCDF and dat files. These files are divided into two format. First, the profile datas includes the horizontal averaged vertical-time series data of the ocean mixed layer. Second, the particle datas includes the vertical position(Z) and phytoplankton concentration(CHL) distribution of all Lagrangian particles (90,000 particles) at -1 day (CON) and 10 day (CON, SW, EQ) cases respectively.<br>This simulation is conducted by using the PALM, which is the large eddy simulation (LES) model developed by <em> Leibniz University Hannover </em>and<em> </em> Lagrangian Phytoplankton code is uploaded on the github. Basic setup for Langmuir circulation forcing is referred by Noh et al. (2004), oceanic heat flux condition is referred by Goh & Noh (2013) and light penetration scheme is adapted from Manniza et al. (2005). Basic concept of LPM code is originated from NP (Nutrient Plankton) model.<br>Three different cases are simulated by this code with different latitude and friction velocity : control experiment (CON : lat = 40<sup>o</sup> & u* = 0.01 m/s), strong wind experiment (SW : lat = 40<sup>o</sup> & u* = 0.02 m/s), and equatorial experiment (SW : lat = 00<sup>o</sup> & u* = 0.01 m/s) to investigate the how the spring bloom is occurred. and what controls the spring bloom. <br><br>LES : https://palm.muk.uni-hannover.de/tracLPM : https://github.com/blackcata/MOD_plankton_model<br>
DOI: 10.6084/m9.figshare.11923800
title: Sea surface salinity (SSS) in the eastern Arabian Sea

description: SSS monitoring was conducted off Mangalore, India at four different stations (12°57'07.33'' N, 74°46'48.34'' E; 12°57'00.48'' N, 74°46'09.63'' E; 12°56'38.16'' N, 74°45' 26.46'' E; 12°56'47.50'' N, 74°44'45.85'' E; depth &lt; 15 m). The data were collected from surface water at all the stations on a monthly basis, excluding June to September (southwest monsoon) due to logistical problems. Water samples were collected with an Aquatrap (2.5 l Partex Products) and immediately transferred into dark 5 L containers. SSS were measured using titration method (Strickland & Parsons, 1972).
DOI: 10.6084/m9.figshare.11923659
title: Influence of Patient Characteristics on Chimeric Antigen Receptor T Cell Therapy in B-cell Acute Lymphoblastic Leukemia

description: Dataset of the study “Influence of Patient Characteristics on Chimeric Antigen Receptor T Cell Therapy in B-cell Acute Lymphoblastic Leukemia”
DOI: 10.6084/m9.figshare.11923659.v1
title: Influence of Patient Characteristics on Chimeric Antigen Receptor T Cell Therapy in B-cell Acute Lymphoblastic Leukemia

description: Dataset of the study “Influence of Patient Characteristics on Chimeric Antigen Receptor T Cell Therapy in B-cell Acute Lymphoblastic Leukemia”
DOI: 10.6084/m9.figshare.11923800.v1
title: Sea surface salinity (SSS) in the eastern Arabian Sea

description: SSS monitoring was conducted off Mangalore, India at four different stations (12°57'07.33'' N, 74°46'48.34'' E; 12°57'00.48'' N, 74°46'09.63'' E; 12°56'38.16'' N, 74°45' 26.46'' E; 12°56'47.50'' N, 74°44'45.85'' E; depth &lt; 15 m). The data were collected from surface water at all the stations on a monthly basis, excluding June to September (southwest monsoon) due to logistical problems. Water samples were collected with an Aquatrap (2.5 l Partex Products) and immediately transferred into dark 5 L containers. SSS were measured using titration method (Strickland & Parsons, 1972).
DOI: 10.6084/m9.figshare.11923647
title: regional community dataset

description: The study covers a large area of natural forests in Northeastern China. The latitudinal and longitudinal ranges of the study region extend from 39–54°N (across 15 degree range) and 119–134°E (15 degree range), respectively. The climate varies from a middle temperate zone to a cool temperate zone. All these zones are characterized by strong seasonality with four distinctly different seasons. The main precipitation period is between July and September, and there is a harsh dry spring season. In the summer of 2017 and 2018, a total of 456 0.1-ha circlular (radius=17.84 m) forest plots were established in the region. All trees with a diameter at breast height (dbh) greater than 5 cm were identified, measured and mapped in each plot. To minimize the potentially bias resulting from uneven sampling coverage, we sampled a subset of plots to ensure even sampling coverage, leading to 262 plots. We used each of the forest plots and its nearest n neighbouring plots to define a particular regional community as a basis for calculating a species pool. In this study, the β-diversity is calculated in each regional community consisting of five or ten local plots (n=4, or 9, plus the focal plot in each case), respectively. Therefore, n+1 (i.e., 5 or 10) indicates the size of a regional community, and defines the spatial scale of each community (there are two scales: 5 and 10 plots).
DOI: 10.6084/m9.figshare.11923626
title: An extremely large saber-tooth cat skull from Uruguay (late Pleistocene–early Holocene, Dolores Formation): body size and paleobiological implications

description: Among the three recognized species of <i>Smilodon</i>, <i>S. populator</i> is the largest in size and has the widest distribution across South America. The present contribution describes an almost complete skull assigned to the aforementioned felid. The material was recovered from sediments of the Dolores Formation (Lujanian Stage/Age) from the southern part of Uruguay. This specimen is remarkable for its unusual craniodental measurements, indicating that it is one of the largest known specimens of the genus. Estimates of body mass indicate that this individual weighed over 400 kg. In addition, maximum prey size estimation greatly surpasses 1 t and approach nearly 3 t. Based on this, aspects of the paleobiology and paleoecology of <i>S. populator</i> are discussed. Undoubtedly, this kind of carnivorous mammal was at the top of the food chain, with clear adaptations for feeding upon the largest available herbivores, which potentially includes several megafaunal mammal species found in South America during the late Pleistocene. <i>Aldo Manzuetti [aldomanzuetti@adinet.com.uy], Facultad de Ciencias (UdelaR), Iguá 4225, CP 11400, Montevideo, Uruguay; Daniel Perea [perea@fcien.edu.uy], Facultad de Ciencias (UdelaR), Iguá 4225, CP 11400, Montevideo, Uruguay; Washington Jones [wawijo@yahoo.com.ar], Museo Nacional de Historia Natural, 25 de Mayo 582, CP 11000, Montevideo, Uruguay; Martín Ubilla [ubilla@fcien.edu.uy], Facultad de Ciencias (UdelaR), Iguá 4225, CP 11400, Montevideo, Uruguay; Andrés Rinderknecht [apaleorinder@yahoo.com], Museo Nacional de Historia Natural, 25 de Mayo 582, CP 11000, Montevideo, Uruguay</i>.
DOI: 10.6084/m9.figshare.11923626.v1
title: An extremely large saber-tooth cat skull from Uruguay (late Pleistocene–early Holocene, Dolores Formation): body size and paleobiological implications

description: Among the three recognized species of <i>Smilodon</i>, <i>S. populator</i> is the largest in size and has the widest distribution across South America. The present contribution describes an almost complete skull assigned to the aforementioned felid. The material was recovered from sediments of the Dolores Formation (Lujanian Stage/Age) from the southern part of Uruguay. This specimen is remarkable for its unusual craniodental measurements, indicating that it is one of the largest known specimens of the genus. Estimates of body mass indicate that this individual weighed over 400 kg. In addition, maximum prey size estimation greatly surpasses 1 t and approach nearly 3 t. Based on this, aspects of the paleobiology and paleoecology of <i>S. populator</i> are discussed. Undoubtedly, this kind of carnivorous mammal was at the top of the food chain, with clear adaptations for feeding upon the largest available herbivores, which potentially includes several megafaunal mammal species found in South America during the late Pleistocene. <i>Aldo Manzuetti [aldomanzuetti@adinet.com.uy], Facultad de Ciencias (UdelaR), Iguá 4225, CP 11400, Montevideo, Uruguay; Daniel Perea [perea@fcien.edu.uy], Facultad de Ciencias (UdelaR), Iguá 4225, CP 11400, Montevideo, Uruguay; Washington Jones [wawijo@yahoo.com.ar], Museo Nacional de Historia Natural, 25 de Mayo 582, CP 11000, Montevideo, Uruguay; Martín Ubilla [ubilla@fcien.edu.uy], Facultad de Ciencias (UdelaR), Iguá 4225, CP 11400, Montevideo, Uruguay; Andrés Rinderknecht [apaleorinder@yahoo.com], Museo Nacional de Historia Natural, 25 de Mayo 582, CP 11000, Montevideo, Uruguay</i>.
DOI: 10.6084/m9.figshare.11923611
title: BlogCatalog dataset

description: <b>Abstract</b>: BlogCatalog is the social blog directory which manages the bloggers and their blogs.<br><b>Number of Nodes:</b>10,312<b>Number of Edges:</b>333,983<b>Missing Values?</b>no<br><b>Source:</b>Nitin Agarwal+, Xufei Wang*, Huan Liu*<br><br>+ Department of Information Science, University of Arkansas at Little Rock. E-mail:nxagarwal@ualr.edu<br><br>* School of Computing, Informatics and Decision Systems Engineering, Arizona State University. E-mail: huan.liu@asu.edu, xufei.wang@asu.edu<br><b>Data Set Information:</b>2 files are included:<br><br>1. nodes.csv<br>-- it's the file of all the users. This file works as a dictionary of all the users in this data set. It's useful for fast reference. It contains all the node ids used in the dataset.<br><br>2. edges.csv<br>-- this is the friendship network among the bloggers. The blogger's friends are represented using edges. Here is an example.<br><br>1,2<br><br>This means blogger with id "1" is friend with blogger id "2".<br><b>Attribute Information:</b>This is the data set crawled on July, 2009 from BlogCatalog ( http://www.blogcatalog.com ). BlogCatalog is a social blog directory website. This contains the friendship network crawled. For easier understanding, all the contents are organized in CSV file format.<br><br>-. Basic statistics<br><br>Number of bloggers : 88,784<br><br>Number of friendship pairs: 4,186,390<br><b>Relevant Papers:</b><b><br></b>Nitin Agarwal and Huan Liu. ”Modeling and Data Mining in Blogosphere”, Synthesis Lectures on Data Mining and Knowledge Discovery #1, Morgan & Claypool Publishers, Robert Grossman (Editor), August 2009. ISBN: 9781598299083 (paperback) ISBN: 9781598299090 (ebook) Nitin Agarwal, Magdiel Galan, Huan Liu, and Shankar Subramanya. WisColl: Collective Wisdom based Blog Clustering. Journal of Information Science, 180(1): 39-61, January, 2010. Nitin Agarwal, Huan Liu, Sudheendra Murthy, Arunabha Sen, and Xufei Wang. A Social Identity Approach to Identify Familiar Strangers in a Social Network. In Proceedings of the Third International AAAI Conference on Weblogs and Social Media (ICWSM09), pp. 2 - 9, May 17-20, 2009. San Jose, California. Nitin Agarwal, Huan Liu, Sudheendra Murthy, Arunabha Sen, and Xufei Wang. "A Social Identity Approach to Identify Familiar Strangers in a Social Network", 3rd International AAAI Conference on Weblogs and Social Media (ICWSM09), pp. 2 - 9, May 17-20, 2009. San Jose, California.<br>
DOI: 10.6084/m9.figshare.11923590
title: Influence of Patient Characteristics on Chimeric Antigen Receptor T Cell Therapy in B-cell Acute Lymphoblastic Leukemia

description: Dataset of the study “Influence of Patient Characteristics on Chimeric Antigen Receptor T Cell Therapy in B-cell Acute Lymphoblastic Leukemia”<br>
DOI: 10.6084/m9.figshare.11923590.v1
title: Source Data.xlsx

description: 该数据支持了研究的结果“ <b>在嵌合抗原受体的T细胞疗法患者特征的影响</b><b> B细胞</b><b>急性淋巴细胞性白血病</b> ”<b> </b><b></b><b> </b><b></b><br>
DOI: 10.6084/m9.figshare.11923590.v3
title: Influence of Patient Characteristics on Chimeric Antigen Receptor T Cell Therapy in B-cell Acute Lymphoblastic Leukemia

description: Dataset of the study “Influence of Patient Characteristics on Chimeric Antigen Receptor T Cell Therapy in B-cell Acute Lymphoblastic Leukemia”<br>
DOI: 10.6084/m9.figshare.11923590.v2
title: Source Data.xlsx

description: Dataset of the study “Influence of Patient Characteristics on Chimeric Antigen Receptor T Cell Therapy in B-cell Acute Lymphoblastic Leukemia”<br>