cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.14915841.v1
title: Witnessing subsystems for probabilistic systems with low treewidth - Supplementary material

description: Supplementary material including instructions to reproduce all experiments and datasets used in the paper "Witnessing subsystems for probabilistic systems with low treewidth".<br>
DOI: 10.5281/zenodo.5075471
title: Supplementary Data for "Imaging biological tissue with high-throughput single-pixel compressive holography"

description: This file concludes two subfolders, which were archived with relevant data that are necessary for reconstructing the holographic images of stained tissue from mouse tails and unstained tissue from mouse brains, respectively. Both two subfolders have the same file components, including the MATLAB data and raw data collected from the data acquisition card that are necessary for holographic imaging reconstruction. <br> Here, 'Supplementary Data 1' is prepared for the holographic reconstruction of stained tissue from mouse tails, while 'Supplementary Data 2' is provided for unstained tissue from mouse brains. *) biological_sample.mat: The raw data of imaging a slice of rat tail. The format of the data has been converted from .tdms to .mat file. *) background_curvature.mat: The raw data used to correct for phase contaminations from system aberrations. The format of the data has been converted from .tdms to .mat file. *) biological_sample_rawdata.tdms: The raw data of imaging a slice of rat tail. The data was collected through DAC and was in the format of TDMS. *) background_curvature_rawdata.tdms: The raw data used to correct for phase contaminations from system aberrations. The data was collected through DAC and was in the format of TDMS.<br>
DOI: 10.6073/pasta/4a92461aaea7145a56dff93b7ba412bf
title: Ectomycorrhizal Community of Red Oak at Harvard Forest 2013

description: There is evidence that ectomycorrhizal Pezizales prevail on roots at woodland edges or habitats where trees are well spaced, and where there is minimal understory vegetation, soil pH is relatively high, and bare soil is a feature. This observation was repeated in a recent study of mitospores produced by these fungi, where they were found most often at woodland edges, road sides, the middle of paths, and lawns. A key feature that distinguishes Basidiomycota from Ascomycota is that the dikaryon necessary for sexual reproduction is generally formed early after spore germination in the former, but not until fruitbody formation in the latter. Our hypothesis is that the mitospores produced by ectomycorrhizal Pezizales act as spermatia, and that these spores are more effectively produced and dispersed from bare soil than from areas with a thick organic layer. Here we tested our hypothesis by comparing the community of ectomycorrhizal root fungi on red oak trees in Harvard Forest and the Arnold Arboretum.
DOI: 10.6084/m9.figshare.14915655
title: Carvacrol ameliorates beta-amyloid-induced neurotoxicity, oxidative stress and cognitive impairment in in-vitro and in-vivo models of Alzheimer's disease

description: The datasets generated during this study
DOI: 10.6084/m9.figshare.14915655.v1
title: Carvacrol ameliorates beta-amyloid-induced neurotoxicity, oxidative stress and cognitive impairment in in-vitro and in-vivo models of Alzheimer's disease

description: The datasets generated during this study
DOI: 10.6073/pasta/e800750a769f71f2b4d9ae5eda17e4ef
title: Taxonomy and Biogeography of Nematode Communities at Harvard Forest 2014

description: Our overall goal is to describe and map nematode biodiversity in North America. Specifically we will concentrate on Criconematina, a suborder of plant parasitic, soil-dwelling nematodes. Criconematina, commonly referred to as ring-nematodes, are distributed globally associated with a wide range of hosts and habitats. In native grasslands and forests, they may constitute as much as 30% of the below-ground nematode community. Their abundance often approaches 500 individuals per 100cc of soil with as many as a dozen species recorded from a single habitat. Host associations may be broad, covering entire plant families, or they may specialize in feeding on a few closely related plant species. Several are known agronomic pest species, but the vast majority is known only from native habitats and responds negatively to soil disturbance. Due to their sensitivity to disturbance and associations with a range of plant species, some ecologists have suggested that ring nematodes could serve as a below-ground biological indicator of habitat quality. Before this application is possible taxonomic boundaries need to be evaluated and a reference database needs to be established.
DOI: 10.6084/m9.figshare.14663313
title: Metadata record for the article: Analysing the life cycle greenhouse (GHG) emissions of cities: decoupling density from tallness

description: <b>Summary</b><br> This metadata record provides details of the data supporting the claims of the related article: “Decoupling density from tallness in analysing the life cycle greenhouse gas emissions of cities”. The related study proposes a method that decouples density and tallness of buildings in urban environments and allows each to be analysed individually when estimating life cycle greenhouse gas (GHG) emissions. Type of data: quantitative analysis of life cycle greenhouse gas (GHG) emissions Sample size: 5000 synthetically generated urban environments (5 different cases with 1000 urban environments per case) Sampling strategy: The urban environments are generated through stochastic modelling, which relies on input data collected from real buildings, cities and neighbourhoods. <b>Data access</b> All code and supporting data can be accessed via <i>GitHub</i> at https://github.com/jayarehart/Denser-Taller. Static versions of the two data files included in the <i>GitHub </i>repository have also been included with this <i>figshare </i>data record (downloaded from <i>GitHub </i>on 24/05/2021). These files are ‘fixed_area.xlsx’ and ‘fixed_pop.xlsx’. Additional supplementary data and notes are available in the files ‘supplementary_methods.xlsx’ (Excel spreadsheet with multiple tabs) and ‘supplementary_notes.pdf’, which are publicly available in the <i>Mendeley Data </i>repository at https://doi.org/10.17632/kj3zn5nx6b.1, as well as together with this <i>figshare </i>data record. <b>Corresponding author(s) for this study</b> Francesco Pomponi, Resource Efficient Built Environment Lab (REBEL), Edinburgh Napier University. f.pomponi@napier.ac.uk.
DOI: 10.6084/m9.figshare.14663313.v1
title: Metadata record for the article: Analysing the life cycle greenhouse (GHG) emissions of cities: decoupling density from tallness

description: <b>Summary</b><br> This metadata record provides details of the data supporting the claims of the related article: “Decoupling density from tallness in analysing the life cycle greenhouse gas emissions of cities”. The related study proposes a method that decouples density and tallness of buildings in urban environments and allows each to be analysed individually when estimating life cycle greenhouse gas (GHG) emissions. Type of data: quantitative analysis of life cycle greenhouse gas (GHG) emissions Sample size: 5000 synthetically generated urban environments (5 different cases with 1000 urban environments per case) Sampling strategy: The urban environments are generated through stochastic modelling, which relies on input data collected from real buildings, cities and neighbourhoods. <b>Data access</b> All code and supporting data can be accessed via <i>GitHub</i> at https://github.com/jayarehart/Denser-Taller. Static versions of the two data files included in the <i>GitHub </i>repository have also been included with this <i>figshare </i>data record (downloaded from <i>GitHub </i>on 24/05/2021). These files are ‘fixed_area.xlsx’ and ‘fixed_pop.xlsx’. Additional supplementary data and notes are available in the files ‘supplementary_methods.xlsx’ (Excel spreadsheet with multiple tabs) and ‘supplementary_notes.pdf’, which are publicly available in the <i>Mendeley Data </i>repository at https://doi.org/10.17632/kj3zn5nx6b.1, as well as together with this <i>figshare </i>data record. <b>Corresponding author(s) for this study</b> Francesco Pomponi, Resource Efficient Built Environment Lab (REBEL), Edinburgh Napier University. f.pomponi@napier.ac.uk.
DOI: 10.5517/ccdc.csd.cc27qf9q
title: CCDC 2077133: Experimental Crystal Structure Determination

description: Related Article: Andrey V. Protchenko, M. Ángeles Fuentes, Jamie Hicks, Caitilín McManus, Rémi Tirfoin, Simon Aldridge|2021|Dalton Trans.|50|9059|doi:10.1039/D1DT01216A
DOI: 10.5517/ccdc.csd.cc27qfbr
title: CCDC 2077134: Experimental Crystal Structure Determination

description: Related Article: Andrey V. Protchenko, M. Ángeles Fuentes, Jamie Hicks, Caitilín McManus, Rémi Tirfoin, Simon Aldridge|2021|Dalton Trans.|50|9059|doi:10.1039/D1DT01216A
DOI: 10.5517/ccdc.csd.cc27qfcs
title: CCDC 2077135: Experimental Crystal Structure Determination

description: Related Article: Andrey V. Protchenko, M. Ángeles Fuentes, Jamie Hicks, Caitilín McManus, Rémi Tirfoin, Simon Aldridge|2021|Dalton Trans.|50|9059|doi:10.1039/D1DT01216A
DOI: 10.5517/ccdc.csd.cc27qfdt
title: CCDC 2077136: Experimental Crystal Structure Determination

description: Related Article: Andrey V. Protchenko, M. Ángeles Fuentes, Jamie Hicks, Caitilín McManus, Rémi Tirfoin, Simon Aldridge|2021|Dalton Trans.|50|9059|doi:10.1039/D1DT01216A
DOI: 10.5255/ukda-sn-854781
title: History of Recorded Jazz: DTL1000, 1920-2020

description: We present the DTL1000 dataset, which was created in the “Dig That Lick” project and covers the history of recorded jazz with a sample of 1,750 improvisations extracted from 1,060 audio tracks. The dataset contains a mixture of collected (editorial metadata), manually annotated (structure, style), and automatically generated (main melody transcriptions of solos) data describing the recordings. The motivation for creating this dataset was the study of patterns in jazz improvisation, but there are many other applications for this resource. The accompanying paper presents the dataset creation process, data structure and contents with descriptive statistics and discusses the origin and process of the annotations, as well as general use cases and specifically the case of pattern analysis. These components and their combinations enable a number of use cases for jazz studies as well as algorithm development for music analysis. The DTL1000 dataset provides a rich resource for a variety of disciplines, and constitutes a contribution to a field where large datasets with rich annotations are scarce.
DOI: 10.34777/ecmc-0z76
title: ramp-pulse-zebrafish-heart

description: This dataset contains image series of the beating heart of a 48 hours post fertilization old Tg(actb2:LIFEACT-RFP) transgenic zebrafish.
DOI: 10.15468/wrztp9
title: Two new Palaearctic species of moth flies (Diptera, Psychodidae, Psychodinae) from the Caucasus Mts.

description: This dataset contains the digitized treatments in Plazi based on the original journal article Ježek, Jan, Oboňa, Jozef, Manko, Peter (2021): Two new Palaearctic species of moth flies (Diptera, Psychodidae, Psychodinae) from the Caucasus Mts. Zootaxa 4985 (4): 582-594, DOI: https://doi.org/10.11646/zootaxa.4985.4.11
DOI: 10.5281/zenodo.5072592
title: Synaptic encoding of vestibular sensation regulates movement timing and coordination

description: HamlingSchoppik2021 <br> Data/Code Repository Notes Note: All code was written and tested on Matlab R2017b 0) Uncompress the tar files as follows:<br> Code.tar.gz into HamlingSchoppik2021/Code/<br> Anatomy.tar.gz,Behavior.tar.gz,CCShake.tar.gz,EPSCShake.tar.gz,HypDep.tar.gz,IPSCShake.tar.gz,NefmaLesionBackfillControl.tar.gz,NefmaPAGFPComparison.tar.gz,RecordingProperties.tar.gz,SpontCC.tar.gz,SpontaneousEPSC.tar.gz,SpontaneousIPSC.tar.gz into HamlingSchoppik2021/ProcessedData/<br> LesionsDay1.tar.gz,LesionsDay2.tar.gz,NefmaControls.tar.gz,NefmaLesions.tar.gz,PA-GFPcontrols.tar.gz,PA-GFPlesion.tar.gz into /HamlingSchoppik2021/RawBehaviorData/ LesionsDay1ByDate.tar.gz,LesionsDay2ByDate.tar.gz,NefmaControlsByDate.tar.gz,NefmaLesionsByDate.tar.gz,PA-GFPcontrolsByDate.tar.gz,PA-GFPlesionByDate.tar.gz into /HamlingSchoppik2021/RawBehaviorData_byExpt/<br> and<br> RawEphysData2015.tar.gz,RawEphysData2016.tar.gz,RawEphysData2017.tar.gz into /HamlingSchoppik2021/RawEphysData 1) (on a mac) Add HamlingSchoppik2021 to the Desktop 2) Add HamlingSchoppik2021 to Matlab path 3) If starting from Raw Data (optional):<br> Run preprocessData.m to take process raw Ephys data and raw behavioral data. Processed ephys data will save in folders in /ProcessedData. Processed behavioral data will save in the RawBehaviorData folders. <br> Pull all Behavior folders from /RawBehaviorData and /RawBehaviorData_byExperiment into /ProcessedData/Behavior for further plotting. 4) If starting from Processed Data (recommended):<br> Run code in /HamlingSchoppik2021/Code for each figure. These codes call functions from /Code/calledFunctions. <br> The code for each figure can be run independently (they do not need to be called in any order)<br> Each code makes figures for a main figure, calculates any stats that are in the text for that figure, and makes any supplementary figures associated with that figure. (With exception of Figure S1 which has its own code).
DOI: 10.5281/zenodo.5072591
title: Synaptic encoding of vestibular sensation regulates movement timing and coordination

description: HamlingSchoppik2021 <br> Data/Code Repository Notes Note: All code was written and tested on Matlab R2017b 0) Uncompress the tar files as follows:<br> Code.tar.gz into HamlingSchoppik2021/Code/<br> Anatomy.tar.gz,Behavior.tar.gz,CCShake.tar.gz,EPSCShake.tar.gz,HypDep.tar.gz,IPSCShake.tar.gz,NefmaLesionBackfillControl.tar.gz,NefmaPAGFPComparison.tar.gz,RecordingProperties.tar.gz,SpontCC.tar.gz,SpontaneousEPSC.tar.gz,SpontaneousIPSC.tar.gz into HamlingSchoppik2021/ProcessedData/<br> LesionsDay1.tar.gz,LesionsDay2.tar.gz,NefmaControls.tar.gz,NefmaLesions.tar.gz,PA-GFPcontrols.tar.gz,PA-GFPlesion.tar.gz into /HamlingSchoppik2021/RawBehaviorData/ LesionsDay1ByDate.tar.gz,LesionsDay2ByDate.tar.gz,NefmaControlsByDate.tar.gz,NefmaLesionsByDate.tar.gz,PA-GFPcontrolsByDate.tar.gz,PA-GFPlesionByDate.tar.gz into /HamlingSchoppik2021/RawBehaviorData_byExpt/<br> and<br> RawEphysData2015.tar.gz,RawEphysData2016.tar.gz,RawEphysData2017.tar.gz into /HamlingSchoppik2021/RawEphysData 1) (on a mac) Add HamlingSchoppik2021 to the Desktop 2) Add HamlingSchoppik2021 to Matlab path 3) If starting from Raw Data (optional):<br> Run preprocessData.m to take process raw Ephys data and raw behavioral data. Processed ephys data will save in folders in /ProcessedData. Processed behavioral data will save in the RawBehaviorData folders. <br> Pull all Behavior folders from /RawBehaviorData and /RawBehaviorData_byExperiment into /ProcessedData/Behavior for further plotting. 4) If starting from Processed Data (recommended):<br> Run code in /HamlingSchoppik2021/Code for each figure. These codes call functions from /Code/calledFunctions. <br> The code for each figure can be run independently (they do not need to be called in any order)<br> Each code makes figures for a main figure, calculates any stats that are in the text for that figure, and makes any supplementary figures associated with that figure. (With exception of Figure S1 which has its own code).
DOI: 10.5281/zenodo.4537822
title: Contact-based temperature, breathing and cough patterns dataset for early COVID-19 symptoms identification

description: This release features the dataset recorded using our prototype hardware device (contact-based) that collects accelerometer and temperature readings to investigate breathing patterns, personal activity and cough patterns. As fever and cough are considered as two of the most common symptoms for COVID-19, our aim was to focus on these physiological features. In addition, research also states that all COVID-19 contractions showed elevated breathing patterns in patients, that could be easily identifiable from Eupnea state. Therefore, in this research project, we aimed at:<br> 1. Designing a prototype chest-worn device to measure dynamic chest movements (to record breathing and cough patterns)<br> 2. Detect different activity patterns<br> 3. Record temperature variations during idle and active stage<br> 4. Using unsupervised machine learning algorithm to detect anomalies by creating a composite score for (breathing and cough patterns as well as temperature). This release also features some code examples that were used for data pre-processing, feature identification and anomaly detection using (K-means and DBSCAN algorithms). It is important to note that this dataset should be considered and further investigated for preliminary exploratory analysis. The data was collected from healthy adults (that did not undergo COVID-19 clinical screening tests). Therefore, it must be clearly identified that this dataset DOES NOT represent positive COVID-19 contractions. --Published Articles [1]. O. Ali, M. K. Ishak and M. Kamran, "Early covid-19 symptoms identification using hybrid unsupervised machine learning techniques," Computers, Materials & Continua, vol. 69, no.1, pp. 747–766, 2021.
description: The dataset is self explanatory with different fields representing different states of the sensors. If unsure, please feel free to email the authors.
DOI: 10.5281/zenodo.4115561
title: Contact-based temperature, breathing and cough patterns dataset for early COVID-19 symptoms identification

description: This release features the dataset recorded using our prototype hardware device (contact-based) that collects accelerometer and temperature readings to investigate breathing patterns, personal activity and cough patterns. As fever and cough are considered as two of the most common symptoms for COVID-19, our aim was to focus on these physiological features. In addition, research also states that all COVID-19 contractions showed elevated breathing patterns in patients, that could be easily identifiable from Eupnea state. Therefore, in this research project, we aimed at:<br> 1. Designing a prototype chest-worn device to measure dynamic chest movements (to record breathing and cough patterns)<br> 2. Detect different activity patterns<br> 3. Record temperature variations during idle and active stage<br> 4. Using unsupervised machine learning algorithm to detect anomalies by creating a composite score for (breathing and cough patterns as well as temperature). This release also features some code examples that were used for data pre-processing, feature identification and anomaly detection using (K-means and DBSCAN algorithms). It is important to note that this dataset should be considered and further investigated for preliminary exploratory analysis. The data was collected from healthy adults (that did not undergo COVID-19 clinical screening tests). Therefore, it must be clearly identified that this dataset DOES NOT represent positive COVID-19 contractions. --Published Articles [1]. O. Ali, M. K. Ishak and M. Kamran, "Early covid-19 symptoms identification using hybrid unsupervised machine learning techniques," Computers, Materials & Continua, vol. 69, no.1, pp. 747–766, 2021.
description: The dataset is self explanatory with different fields representing different states of the sensors. If unsure, please feel free to email the authors.
DOI: 10.15468/vcguh6
title: Revised taxonomic status of Scythia Kiritchenko, 1938 and Mohelnia Šulc, 1941 (Hemiptera: Coccomorpha: Coccidae: Eriopeltinae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Chen, Lijie, Xing, Jichun (2021): Revised taxonomic status of Scythia Kiritchenko, 1938 and Mohelnia Šulc, 1941 (Hemiptera: Coccomorpha: Coccidae: Eriopeltinae). Zootaxa 4985 (4): 595-597, DOI: https://doi.org/10.11646/zootaxa.4985.4.12
DOI: 10.6073/pasta/452c241875715e1217f799ce3b8d46d0
title: Predator Contributions to Belowground Responses to Climate Warming at Harvard Forest 2014

description: Identifying the factors that control soil CO2 emissions will improve our ability to predict the magnitude of climate change-soil ecosystem feedbacks. Despite the integral role of invertebrates in belowground systems, they are excluded from climate change models. Soil invertebrates have consumptive and non-consumptive effects on microbes, whose respiration accounts for nearly half of soil CO2 emissions. By altering the behavior and abundance of invertebrates that interact with microbes, invertebrate predators may have indirect effects on soil respiration. We examined the effects of a generalist arthropod predator on belowground respiration under different warming scenarios. Based on research suggesting invertebrates may mediate soil CO2 emission responses to warming, we predicted that predator presence would result in increased emissions by negatively affecting these invertebrates. We altered the presence of wolf spiders (Pardosa spp.) in mesocosms containing a forest floor community. To simulate warming, we placed mesocosms of each treatment in ten open-top warming chambers ranging from 1.5 to 5.5° C above ambient at Harvard Forest, MA. As expected, CO2 emissions increased under warming and we found an interactive effect of predator presence and warming, though the effect was not consistent through time. The interaction between predator presence and warming was the inverse of our predictions: mesocosms with predators had lower respiration at higher levels of warming than those without predators. Carbon dioxide emissions were not significantly associated with microbial biomass. We did not find evidence of consumptive effects of predators on the invertebrate community, suggesting that predator presence mediates response of microbial respiration to warming through non-consumptive means. In our system we found a significant interaction between warming and predator presence that warrants further research into mechanism and generality of this pattern to other systems.
DOI: 10.5281/zenodo.5075102
title: Database for Geng2021WRR

description: Data used in paper "Intertwined ecomorphodynamic evolution of salt-marsh platforms and tidal networks cutting through them".
DOI: 10.6073/pasta/ef887a166d1419bd61f45bf386c2e180
title: Impacts of Climate Warming on Trophic Function at Harvard Forest and Duke Forest 2013

description: Forest floor food webs play pivotal roles in carbon cycling, but they are rarely considered in models of carbon fluxes, including soil carbon dioxide emissions (respiration), under climatic warming. The indirect effects of invertebrates on heterotrophic respiration through interactions with microbial communities are significant and will be altered by warming. However, the interactive effects of invertebrates and warming on microbes and heterotrophic respiration in the field are poorly understood. In this study we combined field and common garden laboratory approaches to examine relationships between warming, forest floor food web structure, and heterotrophic respiration. We found that soil animals can overwhelm the effects of warming (to 5 degrees Celsius above ambient) on heterotrophic respiration. In particular, the presence of higher trophic levels and burrowing detritivores strongly determined heterotrophic respiration rates in temperate forest soils, dictating the ecosystem response to warming. These effects were, however, context-dependent, with greater effects in a lower-latitude site. Without isolating and including the significant impact of invertebrates, climate models will be incomplete, hindering well-informed policy decisions.