cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.24406336
title: Increasing plant species diversity suppresses free-living N2 fixation in litter and soil of a subtropical karst forest

description: 1. Free-living N<sub>2</sub> fixation (FNF) is an important source of bioavailable nitrogen for forests. Though increasing plant species diversity (PSD) benefits soil nitrogen accumulation, how it impacts FNF rate has not been explored.2. Forty-five plots covering a PSD gradient were selected in a subtropical karst forest, southwest China. FNF rates in leaf litter and soil were measured using acetylene reduction assay calibrated with a <sup>15</sup>N<sub>2</sub> fixation method. Diazotrophic community, litter and soil properties were determined as well.3. The <i>nifH</i> gene abundance was not significantly altered in litter, but was significantly decreased in soil by higher PSD. Diazotrophic community’s Shannon diversity was significantly increased by higher PSD in both litter and soil. Increasing PSD inhibited litter FNF rate via aggravating phosphorus limitation and decreasing the relative abundance of <i>Betaproteobacteria</i> and <i>Deltaproteobacteria</i>. For the mineral soil horizon, increasing PSD suppressed FNF rate via decreasing <i>nifH</i> gene abundance caused by elevated N availability and lowered phosphorus and iron availability, or through decreasing the relative abundance of <i>Deltaproteobacteria</i> but increasing that of <i>Betaproteobacteria</i>.4. Our findings, for the first time, reveal the microbial and abiotic controls on FNF in litter and soil in response to PSD, and hence benefit the prediction of nitrogen input via biological N<sub>2</sub> fixation under changes in PSD.
DOI: 10.15468/6jdvj7
title: Model-based mapping of assemblages for ecology and conservation management: A case study of demersal fish on the Kerguelen Plateau

description: Quantifying biological assemblages and their environment is a fundamental, yet statistically challenging task in conservation ecology. Here, we use a recently developed approach called Regions of Common Profile (RCP) to quantify and map the distribution of demersal fish assemblages in an ecologically significant region of the Southern Ocean to (1) gain ecological and management insights and (2) evaluate the utility of the new method for ecoregionalization.
DOI: 10.6084/m9.figshare.24406390
title: Teacher OHS Leadership and Peers' Safety Behavior on Indonesian Student Safety Behavior

description: The data is the results of measurement on 959 Indonesian Students to investigate the predictors of their decision-making in safety-related behavior in academic laboratories/workshops.
DOI: 10.6084/m9.figshare.24406390.v1
title: Teacher OHS Leadership and Peers' Safety Behavior on Indonesian Student Safety Behavior

description: The data is the results of measurement on 959 Indonesian Students to investigate the predictors of their decision-making in safety-related behavior in academic laboratories/workshops.
DOI: 10.34740/kaggle/dsv/6744081
title: WallStreetBets 2022
DOI: 10.22032/dbt.48661
title: Hypnotic visual blockade study

description: EEG raw data recordings of 60 individuals during a hypnosis (HYP) and control (CON) condition. In each condition, participants completed a visual three-stimulus-oddball paradigm and a visual steady-state stimulation block. During HYP, participants were suggested that a wooden board in front of their eyes would obstruct their view of the screen. The data collection took place in the periods from June 2014 until July 2014 (L. Kerscher) and from December 2015 until February 2016 (B. Schmidt) at the Institute for Psychology, Department of Clinical Psychology. Meta information about data structure is included in the readme.txt. Data and further information are presented in: [1] Schmidt B, Hecht H, Naumann E, Miltner WHR (2017). The Power of mind: Blocking visual perception by hypnosis. Scientific Reports, 7: 4889, https://doi.org/10.1038/s41598-017-05195-2 [2] Franz M, Schmidt B, Hecht H, Naumann E, Miltner WHR (submitted). Suggested visual blockade during hypnosis: Top-down modulation of stimulus processing in a visual oddball task.
DOI: 10.5281/zenodo.10023602
title: Data set of flow-sediment processes in the Yarlung Zangbo River (2021)
DOI: 10.5281/zenodo.10023601
title: Data set of flow-sediment processes in the Yarlung Zangbo River (2021)
DOI: 10.22032/dbt.48840
title: Fragebogen-Synopse zum Panelbefragungs-Projekt: The Relevance of Communication during Corona Crises (RCCC): (Wellen 1-5) 2020/21
DOI: 10.25919/xtqn-2459
title: Hourly near-surface air temperature grids for Australia

description: This collection provides hourly near-surface (1.5 m) air temperature grids for Australia with a spatial resolution of 1 km. These data are generated using climatologically-aided spatial interpolation, and include two key products: i) a long term climatology for every hour on every 5th day of the year (representing 1990-2019), and ii) a time-series of hourly air temperature data beginning on 01/Jan/2015.
DOI: 10.22032/dbt.50184
title: Forschungsdaten zum Energiesystemmodell Thüringen
DOI: 10.6084/m9.figshare.24406363
title: Spatial transcriptome of one sample of psoraisis.

description: Spatial transcriptome of one sample of psoraisis.
DOI: 10.6084/m9.figshare.24406363.v1
title: Spatial transcriptome of one sample of psoraisis.

description: Spatial transcriptome of one sample of psoraisis.
DOI: 10.5281/zenodo.10023606
title: Population data for Leionema ambiens targeted surveys 2023

description: Population survey data for Leionema ambiens collected during targeted surveys in Jan 2023
DOI: 10.5281/zenodo.8162022
title: Population data for Grevillea buxifolia subsp. ecorniculata targeted surveys 2023

description: Population data for Grevillea buxifolia subsp. ecorniculata targeted surveys 2023
DOI: 10.5281/zenodo.8162023
title: Population data for Grevillea buxifolia subsp. ecorniculata targeted surveys 2023

description: Population data for Grevillea buxifolia subsp. ecorniculata targeted surveys 2023
DOI: 10.5281/zenodo.10023607
title: Population data for Leionema ambiens targeted surveys 2023

description: Population survey data for Leionema ambiens collected during targeted surveys in Jan 2023
DOI: 10.5281/zenodo.8118653
title: Population data for Grevillea buxifolia subsp. ecorniculata targeted surveys February 2023

description: Population data collected in targeted field survey as part of Flora Connections project
DOI: 10.5281/zenodo.8118654
title: Population data for Grevillea buxifolia subsp. ecorniculata targeted surveys February 2023

description: Population data collected in targeted field survey as part of Flora Connections project
DOI: 10.5281/zenodo.7762102
title: Population data for Dodonaea serratifolia targeted surveys January 2023

description: Population data for Dodonaea serratifolia targeted surveys January 2023
DOI: 10.5281/zenodo.7762103
title: Population data for Dodonaea serratifolia targeted surveys January 2023

description: Population data for Dodonaea serratifolia targeted surveys January 2023
DOI: 10.5281/zenodo.7980175
title: Population data for Callitris monticola targeted surveys January 2023

description: Population data for Callitris monticola collected during targeted surveys during January 2023. Data collection follows the Flora Connections protocol
DOI: 10.5281/zenodo.7980176
title: Population data for Callitris monticola targeted surveys January 2023

description: Population data for Callitris monticola collected during targeted surveys during January 2023. Data collection follows the Flora Connections protocol
DOI: 10.5281/zenodo.10023636
title: Unexpected Tradeoffs of Fossil Fuel Reduction on PM2.5 and O3 Pollution Regulation Can be Offset by Synergistic Control of VOCs Source
DOI: 10.5281/zenodo.10023635
title: Unexpected Tradeoffs of Fossil Fuel Reduction on PM2.5 and O3 Pollution Regulation Can be Offset by Synergistic Control of VOCs Source
DOI: 10.25345/c5fj29q0z
title: MassIVE MSV000093156 - SCD KO in vitro and in vivo lipidomics and lysosomal proteomics
title: Data is associated with manuscript titled: SCD1 and monounsaturated lipids are required for autophagy and survival of adipocytes
DOI: 10.22032/dbt.50983
title: Recent evolution of flowering time across multiple European plant species correlates with changes in aridity - Greenhouse data

description: Ongoing global warming and increased drought frequencies have a large impact on plant populations and potentially drive evolutionary adaptations. Historical comparisons, where plants grown from seeds collected in the past (“ancestors”) are compared to plants grown from freshly collected seeds from the same populations (“descendants”), are a powerful method to investigate such evolutionary changes across many taxa. When applied to multiple species simultaneously, historical comparisons can reveal recent parallel evolutionary shifts. We used 21-38 year old seeds of 13 European plant species, stored in seed banks and originating from Mediterranean and temperate regions, for a greenhouse experiment that investigated shifts of flowering phenology, as a potential result of adaptive evolution to increased drought over the last decades. We additionally used single nucleotide polymorphism (SNP) markers to quantify relatedness and levels of genetic variation, and to characterize potential neutral processes and differences in sampling schemes. We found that, across species, descendants grew faster and advanced their flowering, and that these shifts were correlated with changes in aridity at the population origins, suggesting that drought induced evolution of earlier flowering. In 6 out of the 13 species, however, the SNP markers detected strong differences in genetic variation and relatedness between ancestors and descendants, indicating that other evolutionary processes may have contributed to genetic changes. Our results suggest that changes in aridity due climate change may have influenced the evolutionary trajectories of many plant species in different regions of Europe, and that flowering phenology may be one of the key traits that is rapidly evolving. Our study provides further evidence that seed bank collections, with some limitations, are a largely untapped resource for investigating the impact of global environmental changes on plant populations.
DOI: 10.6084/m9.figshare.24406438
title: Source Data IidaM

description: Source data for Iida's manuscript
DOI: 10.6084/m9.figshare.24406438.v1
title: Source Data IidaM

description: Source data for Iida's manuscript
DOI: 10.34740/kaggle/dsv/6744332
title: Our World in Data - COVID-19
DOI: 10.57841/casdc.0002918
title: 南岭山地维管植物多样性编目
DOI: 10.15485/2008461
title: Demographic data of Sedum lanceolatum from 2013 to 2014 under a climate manipulation experiment

description: Demographic data of Sedum lanceolatum under a climate manipulation experiment (heating and watering). Dataset includes one .csv with demographic data for 232 individuals monitored over 2013-2014 which was used, in part, to draw conclusions in "Elevation effects on vital rate sensitivities generate variation in neighbor effects on population growth rate in Sedum lanceolatum" by Herzog et al. (in review). All data was collected under a watering and warming experiment as part of the Alpine Treeline Warming Experiment at Niwot Ridge, Colorado, USA. There are two main data file formats in this archive: comma-separated values (.csv) which can be read using any simple text editor program, such as TextEdit (Mac) and Notepad (Windows). The .pdf data user’s guide can be read using Adobe Acrobat Reader, or any other compatible software.
DOI: 10.25582/data.2022-03.2448907/2104240
title: Data for EMSL Project 51278 from March 2022
DOI: 10.25582/data.2022-01.2422670/2104241
title: Data for EMSL Project 51278 from January 2022
DOI: 10.25582/data.2021-08.2296520/2104242
title: Data for EMSL Project 51278 from August 2021
DOI: 10.25582/data.2021-10.2369919/2104243
title: Data for EMSL Project 51278 from October 2021
DOI: 10.25582/data.2021-06.2252823/2104244
title: Data for EMSL Project 51278 from June 2021
DOI: 10.25582/data.2021-11.2387064/2104245
title: Data for EMSL Project 51278 from November 2021
DOI: 10.25582/data.2021-06.2252820/2104246
title: Data for EMSL Project 51278 from June 2021
DOI: 10.25582/data.2021-06.2252858/2104247
title: Data for EMSL Project 51278 from June 2021
DOI: 10.25582/data.2022-01.2424028/2104248
title: Data for EMSL Project 51278 from January 2022
DOI: 10.25582/data.2021-10.2368878/2104249
title: Data for EMSL Project 51278 from October 2021
DOI: 10.25582/data.2022-01.2420889/2104250
title: Data for EMSL Project 51278 from January 2022
DOI: 10.25582/data.2022-03.2448925/2104251
title: Data for EMSL Project 51278 from March 2022
DOI: 10.25582/data.2021-08.2296003/2104252
title: Data for EMSL Project 51278 from August 2021
DOI: 10.25582/data.2021-08.2296964/2104253
title: Data for EMSL Project 51278 from August 2021
DOI: 10.25582/data.2021-06.2252715/2104254
title: Data for EMSL Project 51278 from June 2021
DOI: 10.25582/data.2021-10.2368872/2104255
title: Data for EMSL Project 51278 from October 2021
DOI: 10.25582/data.2021-12.2390072/2104256
title: Data for EMSL Project 51278 from December 2021
DOI: 10.25582/data.2022-01.2421386/2104257
title: Data for EMSL Project 51278 from January 2022
DOI: 10.25582/data.2022-01.2424017/2104258
title: Data for EMSL Project 51278 from January 2022
DOI: 10.25582/data.2022-01.2424069/2104259
title: Data for EMSL Project 51278 from January 2022
DOI: 10.25582/data.2021-06.2252447/2104260
title: Data for EMSL Project 51278 from June 2021
DOI: 10.25582/data.2022-01.2424018/2104261
title: Data for EMSL Project 51278 from January 2022
DOI: 10.25582/data.2022-03.2448856/2104262
title: Data for EMSL Project 51278 from March 2022
DOI: 10.5519/qd.a0nxgmwl
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1001 records)
DOI: 10.25582/data.2021-12.2390535/2104263
title: Data for EMSL Project 51278 from December 2021
DOI: 10.25582/data.2022-01.2418088/2104264
title: Data for EMSL Project 51278 from January 2022
DOI: 10.25582/data.2022-03.2448908/2104265
title: Data for EMSL Project 51278 from March 2022
DOI: 10.25582/data.2021-10.2369701/2104266
title: Data for EMSL Project 51278 from October 2021
DOI: 10.25582/data.2021-12.2390409/2104267
title: Data for EMSL Project 51278 from December 2021
DOI: 10.25582/data.2021-12.2390897/2104268
title: Data for EMSL Project 51278 from December 2021
DOI: 10.25582/data.2022-01.2420678/2104269
title: Data for EMSL Project 51278 from January 2022
DOI: 10.25582/data.2021-06.2253326/2104270
title: Data for EMSL Project 51278 from June 2021
DOI: 10.25582/data.2021-06.2253262/2104271
title: Data for EMSL Project 51278 from June 2021
DOI: 10.25582/data.2021-11.2388937/2104272
title: Data for EMSL Project 51278 from November 2021
DOI: 10.25582/data.2022-01.2421719/2104273
title: Data for EMSL Project 51278 from January 2022
DOI: 10.25582/data.2022-01.2419980/2104274
title: Data for EMSL Project 51278 from January 2022
DOI: 10.25582/data.2022-03.2448898/2104275
title: Data for EMSL Project 51278 from March 2022
DOI: 10.25582/data.2021-06.2252795/2104276
title: Data for EMSL Project 51278 from June 2021
DOI: 10.25582/data.2021-06.2253328/2104277
title: Data for EMSL Project 51278 from June 2021
DOI: 10.25582/data.2022-03.2448866/2104278
title: Data for EMSL Project 51278 from March 2022
DOI: 10.25582/data.2021-11.2389009/2104279
title: Data for EMSL Project 51278 from November 2021
DOI: 10.25582/data.2022-01.2421211/2104280
title: Data for EMSL Project 51278 from January 2022
DOI: 10.25582/data.2021-10.2369681/2104281
title: Data for EMSL Project 51278 from October 2021
DOI: 10.25582/data.2021-06.2254874/2104282
title: Data for EMSL Project 51278 from June 2021
DOI: 10.25582/data.2022-01.2418840/2104283
title: Data for EMSL Project 51278 from January 2022
DOI: 10.25582/data.2022-01.2424060/2104284
title: Data for EMSL Project 51278 from January 2022
DOI: 10.25582/data.2022-01.2418129/2104285
title: Data for EMSL Project 51278 from January 2022
DOI: 10.25582/data.2021-10.2369644/2104286
title: Data for EMSL Project 51278 from October 2021
DOI: 10.25582/data.2021-06.2252861/2104287
title: Data for EMSL Project 51278 from June 2021
DOI: 10.25582/data.2021-06.2253209/2104288
title: Data for EMSL Project 51278 from June 2021
DOI: 10.25582/data.2021-10.2368847/2104289
title: Data for EMSL Project 51278 from October 2021
DOI: 10.25582/data.2021-11.2389640/2104290
title: Data for EMSL Project 51278 from November 2021
DOI: 10.25582/data.2021-11.2389063/2104291
title: Data for EMSL Project 51278 from November 2021
DOI: 10.25582/data.2022-01.2418717/2104292
title: Data for EMSL Project 51278 from January 2022
DOI: 10.25582/data.2022-01.2421083/2104293
title: Data for EMSL Project 51278 from January 2022
DOI: 10.25582/data.2022-01.2421578/2104294
title: Data for EMSL Project 51278 from January 2022
DOI: 10.25582/data.2022-01.2424044/2104295
title: Data for EMSL Project 51278 from January 2022
DOI: 10.25582/data.2021-10.2363322/2104296
title: Data for EMSL Project 51278 from October 2021
DOI: 10.25582/data.2021-10.2368074/2104297
title: Data for EMSL Project 51278 from October 2021
DOI: 10.25582/data.2022-03.2459220/2104298
title: Data for EMSL Project 51278 from March 2022
DOI: 10.25582/data.2022-01.2424048/2104299
title: Data for EMSL Project 51278 from January 2022
DOI: 10.25582/data.2022-03.2448921/2104300
title: Data for EMSL Project 51278 from March 2022
DOI: 10.25582/data.2021-06.2252720/2104301
title: Data for EMSL Project 51278 from June 2021
DOI: 10.25582/data.2021-08.2296919/2104302
title: Data for EMSL Project 51278 from August 2021
DOI: 10.25582/data.2022-03.2459391/2104303
title: Data for EMSL Project 51278 from March 2022
DOI: 10.25582/data.2021-06.2253442/2104304
title: Data for EMSL Project 51278 from June 2021
DOI: 10.5519/qd.wrsneytj
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (693318 records)
DOI: 10.25582/data.2021-08.2296286/2104305
title: Data for EMSL Project 51278 from August 2021
DOI: 10.25582/data.2021-08.2296972/2104306
title: Data for EMSL Project 51278 from August 2021
DOI: 10.25582/data.2021-12.2390890/2104307
title: Data for EMSL Project 51278 from December 2021
DOI: 10.25582/data.2022-03.2460209/2104308
title: Data for EMSL Project 51278 from March 2022
DOI: 10.25582/data.2021-10.2368732/2104309
title: Data for EMSL Project 51278 from October 2021
DOI: 10.25582/data.2021-11.2388394/2104310
title: Data for EMSL Project 51278 from November 2021
DOI: 10.25582/data.2021-12.2390887/2104311
title: Data for EMSL Project 51278 from December 2021
DOI: 10.25582/data.2021-06.2253191/2104312
title: Data for EMSL Project 51278 from June 2021
DOI: 10.25582/data.2021-06.2253446/2104313
title: Data for EMSL Project 51278 from June 2021
DOI: 10.25582/data.2021-11.2388938/2104314
title: Data for EMSL Project 51278 from November 2021
DOI: 10.25582/data.2021-11.2387507/2104315
title: Data for EMSL Project 51278 from November 2021
DOI: 10.25582/data.2022-01.2423995/2104316
title: Data for EMSL Project 51278 from January 2022
DOI: 10.25582/data.2022-03.2448913/2104317
title: Data for EMSL Project 51278 from March 2022
DOI: 10.25582/data.2022-03.2457869/2104318
title: Data for EMSL Project 51278 from March 2022
DOI: 10.25582/data.2022-03.2448868/2104319
title: Data for EMSL Project 51278 from March 2022
DOI: 10.25582/data.2022-03.2459428/2104320
title: Data for EMSL Project 51278 from March 2022
DOI: 10.25582/data.2021-08.2296948/2104321
title: Data for EMSL Project 51278 from August 2021
DOI: 10.25582/data.2021-10.2368880/2104322
title: Data for EMSL Project 51278 from October 2021
DOI: 10.25582/data.2021-10.2369104/2104323
title: Data for EMSL Project 51278 from October 2021
DOI: 10.25582/data.2021-08.2296929/2104324
title: Data for EMSL Project 51278 from August 2021
DOI: 10.25582/data.2022-03.2459966/2104325
title: Data for EMSL Project 51278 from March 2022
DOI: 10.25582/data.2021-12.2390862/2104326
title: Data for EMSL Project 51278 from December 2021
DOI: 10.25582/data.2022-01.2424219/2104327
title: Data for EMSL Project 51278 from January 2022
DOI: 10.25582/data.2021-10.2369663/2104328
title: Data for EMSL Project 51278 from October 2021
DOI: 10.5281/zenodo.7865582
title: Improved annotation of enzyme encoding genes using deep learning

description: Supplementary datasets for the paper "Improved annotation of enzyme encoding genes using deep learning". The datasets include Supplementary Dataset 1 (Visualization of the latent representations of enzyme sequences in the Swiss-Prot database using TMAP), Supplementary Dataset 2 (Commonly highlighted motifs for each EC number using DeepEC v2 neural network), Supplementary Dataset 3 (Sequences for each of strain specific alleles), Supplementary Dataset 4 (EC number prediction results for the y-ome proteins), and Supplementary Dataset 5 (EC numbers of 128,100,490 protein sequences in 70,600 genomes in NCBI).
DOI: 10.5281/zenodo.10023678
title: Functional annotation of enzyme-encoding genes using deep learning with transformer layers

description: Supplementary datasets for the paper "Improved annotation of enzyme-encoding genes using deep learning with transformer layers".
The datasets include Supplementary Data 1 (Predicted EC numbers for protein sequences from Swiss-Prot database using DeepECtransformer), Supplementary Data 2 (Visualization of the latent representations of enzyme sequences in the Swiss-Prot database using TMAP), Supplementary Data 3 (Commonly highlighted motifs for each EC number using DeepEC v2 neural network), Supplementary Data 4 (Sequences for each of strain specific alleles), Supplementary Data 5 (EC number prediction results for the y-ome proteins), Supplementary Data 6 (EC numbers of 128,100,490 protein sequences in 70,600 genomes in NCBI), and Supplementary Data 7 (Solubility prediction results for 295 y-ome proteins).
DOI: 10.5281/zenodo.6510617
title: Functional annotation of enzyme-encoding genes using deep learning with transformer layers

description: Supplementary datasets for the paper "Improved annotation of enzyme-encoding genes using deep learning with transformer layers".
The datasets include Supplementary Data 1 (Predicted EC numbers for protein sequences from Swiss-Prot database using DeepECtransformer), Supplementary Data 2 (Visualization of the latent representations of enzyme sequences in the Swiss-Prot database using TMAP), Supplementary Data 3 (Commonly highlighted motifs for each EC number using DeepEC v2 neural network), Supplementary Data 4 (Sequences for each of strain specific alleles), Supplementary Data 5 (EC number prediction results for the y-ome proteins), Supplementary Data 6 (EC numbers of 128,100,490 protein sequences in 70,600 genomes in NCBI), and Supplementary Data 7 (Solubility prediction results for 295 y-ome proteins).
DOI: 10.5281/zenodo.7022655
title: DeepEC v2

description: Supplementary dataset 2 and 3
DOI: 10.5281/zenodo.7902108
title: Functional annotation of enzyme encoding genes using deep learning with transformer layers

description: Supplementary datasets for the paper "Improved annotation of enzyme encoding genes using deep learning with transformer layers". The datasets include Supplementary Data 1 (Predicted EC numbers for protein sequences from Swiss-Prot database using DeepECtransformer), Supplementary Data 2 (Visualization of the latent representations of enzyme sequences in the Swiss-Prot database using TMAP), Supplementary Data 3 (Commonly highlighted motifs for each EC number using DeepEC v2 neural network), Supplementary Data 4 (Sequences for each of strain specific alleles), Supplementary Data 5 (EC number prediction results for the y-ome proteins), and Supplementary Data 6 (EC numbers of 128,100,490 protein sequences in 70,600 genomes in NCBI).
DOI: 10.25582/data.2021-06.2253231/2104329
title: Data for EMSL Project 51278 from June 2021
DOI: 10.25582/data.2021-12.2390861/2104330
title: Data for EMSL Project 51278 from December 2021
DOI: 10.25582/data.2021-06.2253235/2104331
title: Data for EMSL Project 51278 from June 2021
DOI: 10.25582/data.2021-08.2296956/2104332
title: Data for EMSL Project 51278 from August 2021