cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4282989
title: Harnessing the power of digitized natural history collections to visualize spatiotemporal patterns in native and non-native bee flight phenology

description: What time of year are bees flying, where are they flying, and how do biogeographical factors, sex, and native status affect flight phenology? Consistent monitoring along with creating spatially and temporally explicit visualizations using large openly available data sets enhance our understanding of trends in flight time phenology and shape our understanding of bee-plant interactions, including shifts in the phenology of bee pollinators. Species occurrence data from digitized collection networks (iNaturalist, Global Biodiversity Information Faculty (GBIF), Integrated Digitized Biocollections (iDigBio), Symbiota Collections of Arthropods Network (SCAN), and UC Santa Barbara Collection Network) are part of an effort to improve our understanding of bees in coastal Santa Barbara County, including the California Channel Islands. New inventory collections combined with historical data from over 11 natural history museums and 2 observation networks are used in an effort to examine patterns and changes in phenology of native and non-native bee species, and create updated species inventories. Synthesizing species observation data from digitized natural history collections makes use of a wealth of existing data and multiplies the analytical power of isolated observations, but it is not without limitations and challenges. By exploring novel techniques to generate clear and accurate visualizations to communicate bee flight time, we present our key initial findings and identify geographic, temporal, and taxonomic gaps, which will lead to further focused inventory projects of coastal Santa Barbara County, improved data quality for phenological analyses, and reusable methods for visualizing insect phenology data across taxa or geography. <strong>The attached files include the R code and some of the .csv files used to produce the figures in my poster that was available on demand at the Entomology Society of America 2020 virtual meeting. </strong>
DOI: 10.6084/m9.figshare.13269320
title: TJS150723_BT474m1_input_DMSO_24h_S9_sorted.bw

description: ChIPseq
DOI: 10.6084/m9.figshare.13269320.v1
title: TJS150723_BT474m1_input_DMSO_24h_S9_sorted.bw

description: ChIPseq
DOI: 10.6084/m9.figshare.13263863
title: Observation regridded_era_t2m_anomalies data for EnsoPredict

description: Observation data to do hindcast prediction on EnsoPredict. Alternatives of Cobe SST.
DOI: 10.6084/m9.figshare.13263863.v2
title: Observation regridded_era_t2m_anomalies data for EnsoPredict

description: Observation data to do hindcast prediction on EnsoPredict. Alternatives of Cobe SST.
DOI: 10.6084/m9.figshare.13263863.v1
title: Observation regridded_era_t2m_anomalies data for EnsoPredict

description: Observation data to do hindcast prediction on EnsoPredict. Alternatives of Cobe SST.
DOI: 10.6084/m9.figshare.13227509
title: Sea Surface Temperature for EnsoPredict

description: This data contain instrumental/actual historical data of sea surface temperature from year 1880 to 2018 that will be used by EnsoPredict program. Original data can be accessed athttp://portal.nersc.gov/project/dasrepo/AGU_ML_Tutorial/sst.mon.mean.trefadj.anom.1880to2018.nc
DOI: 10.6084/m9.figshare.13227509.v2
title: Sea Surface Temperature for EnsoPredict

description: This data contain instrumental/actual historical data of sea surface temperature from year 1880 to 2018 that will be used by EnsoPredict program. Original data can be accessed athttp://portal.nersc.gov/project/dasrepo/AGU_ML_Tutorial/sst.mon.mean.trefadj.anom.1880to2018.nc
DOI: 10.6084/m9.figshare.13227509.v1
title: Sea Surface Temperature for EnsoPredict

description: This data contain instrumental/actual historical data of sea surface temperature from year 1880 to 2018 that will be used by EnsoPredict program. Original data can be accessed athttp://portal.nersc.gov/project/dasrepo/AGU_ML_Tutorial/sst.mon.mean.trefadj.anom.1880to2018.nc
DOI: 10.15482/usda.adc/1520517
title: Data from: Temporal and agricultural factors influence E. coli survival in soil and transfer to cucumbers
DOI: 10.5281/zenodo.4283013
title: EXpLoRa2

description: LoRa has gained significant momentum among industrial and research communities, with several device manufacturers adopting the LoRa technology in their IoT solutions. However, in spite of LoRa’s wide penetration, the resulting performance under realistic conditions and real-life scenarios has only recently been considered and mainly under line-of-sight environments. For instance, issues like communication across multiple floors and the performance degradation impact of interference have not been analyzed in detail. In the EXpLoRa proposal, we aim to answer the previously identified inquiries from the side of a technology adopter company, namely domX SME, which develops custom IoT monitoring solutions and considers the adoption of LoRa technology for covering the company’s needs. Through the EXpLoRa experiment, we executed LoRa experiments, under realistic city-scale and varying channel conditions (range of ~35 dB, LOS/ NLOS), under controlled high interference conditions and under typical multi-floor buildings. For the purposes of our experiment, NITOS testbed provided full access to ±20 LoRa nodes and one LoRa Gateway with an additional Monitor interface to capture the prevailing channel occupancy. The set of conducted experiments employed all 12 LoRa end nodes, the 8 available channels, 3 different transmission power levels (0,7,14 dBm), 10 Transmission modes (modulation settings) and multiple payload sizes. In total, more than 200K LoRa packets were transmitted and analysed within the RSSI range of --75 and -137 dBm.
DOI: 10.5281/zenodo.4283012
title: EXpLoRa2

description: LoRa has gained significant momentum among industrial and research communities, with several device manufacturers adopting the LoRa technology in their IoT solutions. However, in spite of LoRa’s wide penetration, the resulting performance under realistic conditions and real-life scenarios has only recently been considered and mainly under line-of-sight environments. For instance, issues like communication across multiple floors and the performance degradation impact of interference have not been analyzed in detail. In the EXpLoRa proposal, we aim to answer the previously identified inquiries from the side of a technology adopter company, namely domX SME, which develops custom IoT monitoring solutions and considers the adoption of LoRa technology for covering the company’s needs. Through the EXpLoRa experiment, we executed LoRa experiments, under realistic city-scale and varying channel conditions (range of ~35 dB, LOS/ NLOS), under controlled high interference conditions and under typical multi-floor buildings. For the purposes of our experiment, NITOS testbed provided full access to ±20 LoRa nodes and one LoRa Gateway with an additional Monitor interface to capture the prevailing channel occupancy. The set of conducted experiments employed all 12 LoRa end nodes, the 8 available channels, 3 different transmission power levels (0,7,14 dBm), 10 Transmission modes (modulation settings) and multiple payload sizes. In total, more than 200K LoRa packets were transmitted and analysed within the RSSI range of --75 and -137 dBm.
DOI: 10.6084/m9.figshare.13269458
title: 4-SUPPLEMENTARY MATERIALS-SHI ET AL.doc

description: Primer sequences for the six tight junction proteins and GAPDH
DOI: 10.6084/m9.figshare.13269458.v1
title: 4-SUPPLEMENTARY MATERIALS-SHI ET AL.doc

description: Primer sequences for the six tight junction proteins and GAPDH
DOI: 10.3886/e114599v1-70360
title: Replication data for: Up in Smoke: The Influence of Household Behavior on the Long-Run Impact of Improved Cooking Stoves

description: Laboratory studies suggest that improved cooking stoves can reduce indoor air pollution, improve health, and decrease greenhouse gas emissions in developing countries. We provide evidence, from a large-scale randomized trial in India, on the benefits of a common, laboratory-validated stove with a four-year follow-up. While smoke inhalation initially falls, this effect disappears by year two. We find no changes across health outcomes or greenhouse gas emissions. Households used the stoves irregularly and inappropriately, failed to maintain them, and usage declined over time. This study underscores the need to test environmental technologies in real-world settings where behavior may undermine potential impacts. (JEL D12, O12, O13, Q53, Q54, Q55)
DOI: 10.3886/e115972
title: National Neighborhood Data Archive (NaNDA): Retail Establishments by Census Tract, United States, 2003-2017

description: This dataset contains measures of the number and per capita density of select types of retail establishments—such as clothing, department, building and garden, furniture, and thrift stores—per United States census tract from 2003 through 2017.<br>
description: Retail establishments in all census tracts in the United States, excluding US island territories. Smallest Geographic Unit: census tract
DOI: 10.3886/e127042
title: National Neighborhood Data Archive (NaNDA): Retail Establishments by ZIP Code Tabulation Area, United States, 2003-2017

description: This dataset contains measures of the number and per capita density of select types of retail establishments—such as clothing, department, building and garden, furniture, and thrift stores—per ZIP code tabulation area (ZCTA) in the United States from 2003 through 2017.
description: A ZIP code to ZCTA crosswalk must be used to combine this dataset with ZIP code geocoded data. Such a crosswalk is available on the UDS Mapper website at https://www.udsmapper.org/zcta-crosswalk.cfm. Sample code for merging the UDS Mapper crosswalk with NaNDA datasets is available on the NaNDA repository at https://www.openicpsr.org/openicpsr/project/120088/.
description: Retail establishments in all ZIP code tabulation areas in the United States, including Puerto Rico and the US Virgin Islands. Smallest Geographic Unit: ZIP code tabulation area
DOI: 10.3886/e124681v6
title: Supplementary materials for report “Shining a Brighter Light: Comprehensive Evidence on Adoption and Diffusion of CGIAR-Related Innovations in Ethiopia”

description: The report presents an unprecedented stocktaking of all CGIAR-related innovations in a given country as well as new estimates of adoption of those innovations from a nationally representative dataset generated through a partnership among the Ethiopian Central Statistics Agency (CSA), the World Bank Living Standards Measurement Study (LSMS) team, and the CGIAR Standing Panel on Impact Assessment (SPIA). Ethiopia was chosen for this exercise because it is a hotspot of CGIAR research, with almost all the CGIAR centers represented in Addis Ababa. <br> <br>The report documents the reach of CGIAR-related agricultural innovations in a comprehensive manner across the core domains of CGIAR research activity: animal agriculture; crop germplasm improvement; natural resource management; and policy research. In order to identify the right innovations to collect data on, SPIA conducted more than 90 interviews with CGIAR research leaders, scientists, government officials, and colleagues from the Ethiopian Institute for Agricultural Research (EIAR), all the while compiling documented evidence to support claims made by these key informants. The output of that work is a stocktaking of 52 agricultural innovations and 26 claims of policy influence.<br><br>Quantitative evidence on the adoption of 18 of these innovations was obtained through the incorporation of measurements of the reach of these innovations in the Ethiopian Socioeconomic Survey (ESS), a regionally and nationally representative panel survey of households. We report some data from the third wave (ESS3, carried out in 2015/16), but our major focus is on ESS4 (2018/19).
DOI: 10.3886/e127141
title: Repression in Time: Learning from Troop Movements of British Empire, 1890-1936

description: This project will collect a new geo-referenced dataset on violent conflict and state responses to violent conflict among the colonies of British Empire, from 1881 to 1950. Focusing on troop movements per army lists of British empire, available monthly for most of the period covered, the resulting data can provide valuable information and spark new theories on how colonial armies develop over time and space, and addresses significant challenges in data limitation on colonial coercive apparatus due to intentional hiding of information on repression during colonial rule.
description: NA
description: Response Rates: Not applicable
description: Presence of Common Scales: NA
description: other;
description: British India Army battalions. Smallest Geographic Unit: Town/village
description: Full sample
DOI: 10.3886/e127141v1
title: Repression in Time: Learning from Troop Movements of British Empire, 1890-1936

description: This project will collect a new geo-referenced dataset on violent conflict and state responses to violent conflict among the colonies of British Empire, from 1881 to 1950. Focusing on troop movements per army lists of British empire, available monthly for most of the period covered, the resulting data can provide valuable information and spark new theories on how colonial armies develop over time and space, and addresses significant challenges in data limitation on colonial coercive apparatus due to intentional hiding of information on repression during colonial rule.
description: NA
description: Response Rates: Not applicable
description: Presence of Common Scales: NA
description: other;
description: British India Army battalions. Smallest Geographic Unit: Town/village
description: Full sample
DOI: 10.5519/qd.q6efvnx4
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.25345/c5kb6h
title: MassIVE MSV000086495 - GNPS-SIRIUS-Test-Set-Xenbiotic Standards
title: LC-MS/MS analysis of xenbiotic standards at 100 ng/mL
DOI: 10.25919/r465-8856
title: Parkes observations for project P885 semester 2020APRS_20

description: We propose to observe the magnetars 1E 1547.0-5408 and PSR J1622-4950 approximately weekly, to track their rotation, flux density, and polarisation, as we have long been doing through this project. The revolutionary UWL capabilities promise new opportunities to disentangle still mysterious radio emission mechanisms in magnetars. This promise is supported by the recent follow up of XTE J1810-197, again a radio magnetar after 10 years dormant, for which our observations with the UWL receiver are providing unique and tantalizing measurements of its radio spectrum and polarisation.
DOI: 10.25919/5750-5956
title: Parkes observations for project P1067 semester 2020APRS_13

description: The Parkes Ultra-wide-bandwidth receiver enables, for the first time with the Parkes telescope, a commensal high time and high frequency resolution survey. We will observe Baade's window in which we are guaranteed detections of pulsars and spectral lines. Our science drivers are diverse and range from understanding scattering, to detecting new pulsars, to searching for galaxies in the zone of avoidance, probing the globular cluster at the centre of the window and studying OH circumstellar masers and CO-dark molecular gas. The observing strategy and processing software developed for this survey will be applicable to all future Parkes surveys with the wide-band receiver or cyrogenically-cooled phased array feed.
DOI: 10.5281/zenodo.4282819
title: EcologIA Net

description: EcologIANet_3Classes_401: Dataset of 3 classes of solid waste (Usable Waste, Non-Usable Waste, Organic Waste).<br> <br> EcologIANet_3Classes_906: Dataset of 3 classes of solid waste (Usable Waste, Non-Usable Waste, Organic Waste)<br> <br> EcologIANet_9Classes.zip: Dataset of 9 classes of solid waste (Cardboard, Electronic Waste, Glass, Hazardous Waste, Metal, Non-Recyclable Waste, Organic, Paper and Plastic). EcologIANet_Crop_9Classes.zip: Dataset of 9 classes of solid waste (Cardboard, Electronic Waste, Glass, Hazardous Waste, Metal, Non-Recyclable Waste, Organic, Paper and Plastic); this dataset was cropped to emphasise the region of interest and simplify the train of a Convolutional Neural Network.
DOI: 10.5281/zenodo.4282818
title: EcologIA Net

description: EcologIANet_3Classes_401: Dataset of 3 classes of solid waste (Usable Waste, Non-Usable Waste, Organic Waste).<br> <br> EcologIANet_3Classes_906: Dataset of 3 classes of solid waste (Usable Waste, Non-Usable Waste, Organic Waste)<br> <br> EcologIANet_9Classes.zip: Dataset of 9 classes of solid waste (Cardboard, Electronic Waste, Glass, Hazardous Waste, Metal, Non-Recyclable Waste, Organic, Paper and Plastic). EcologIANet_Crop_9Classes.zip: Dataset of 9 classes of solid waste (Cardboard, Electronic Waste, Glass, Hazardous Waste, Metal, Non-Recyclable Waste, Organic, Paper and Plastic); this dataset was cropped to emphasise the region of interest and simplify the train of a Convolutional Neural Network.
DOI: 10.6084/m9.figshare.13265540.v1
title: 1.xlsx

description: gbmmolecular<br>
DOI: 10.17632/4xnnzrp334.1
title: Data for: Psychostimulants may reduce long-term memory formation by degrading sleep in healthy adults

description: Study Abstract: Sleep is vital for biological function and long-term memory formation, with preferential enhancement of emotionally laden content. A growing trend in healthy young adults is the off-label use of psychostimulants, or “smart drugs”, to prevent sleep and, hopefully, enhance cognition. However, the effect of these drugs on sleep-dependent memory processes are unclear. Here, in a within-subject, double-blind, placebo-controlled design, we investigated the impact of morning administration of dextroamphetamine on memory retention of negative and neutral pictures after 1) 12 hours of wake, and 2) 24 hours with sleep. After 12-hrs of wake, stimulants demonstrated a 6% boost in memory for neutral, but not negative, pictures, compared to placebo. In addition, stimulants impaired nighttime sleep and resulted in a 12% reduction in memory for neutral pictures at 24-hrs, compared to placebo. Again, no performance differences between drug conditions were found for negative pictures. Together, these findings suggest that stimulants provide a fleeting recognition memory boost during the day, but their impairment of nighttime sleep likely leads to next day memory costs.
DOI: 10.17632/hny2332hxk.1
title: Data for: Effect of Temperature on Electrochemical Pitting Corrosion Behavior of 316L Stainless Steel in MDEA Solution Containing Chloride

description: (1) Potentiodynamic polarization data of 316L SS in two solution at different temperatures: (a) plain MDEA solution (b) MDEA solution containing 1.0 wt % Cl−. (2)Breakdown potenital- temperature data for 316L SS in MDEA solution containing 1.0 wt % Cl−. (3)Potentiostatic polarization data of 316L SS at an anodic potential of 300 mV vs. Ag/AgCl (0.1 mol/L-1 KCl) in two solution at different temperatures and elapsed time: (a) plain MDEA solution (b) MDEA solution containing 1.0 wt % Cl−. (4)Data for EIS plots of the corrosion of 316L SS in plain MDEA amine solution at different temperatures: (a) Nyquist plot (b) impedance amplitude (c) phase angle. (5) Data for EIS plots of the corrosion of 316L SS in MDEA amine solution containing 1.0 wt% Cl− at different temperatures: (a) Nyquist plot (b) impedance amplitude (c) phase angle.
DOI: 10.6084/m9.figshare.13263881.v3
title: How fast is Australia's broadband compared to our Top Ten Trading Partners (Speedtest Global Index 2017-2020)?

description: This data collates an excerpt from Speedtest Global Index, over time (2018 - 2020), for Australia's Top Ten Trading Partners by imports and exports. Download speeds are in Mbps. Figures are rounded to nearest 5Mbps, except for % growth, and smallest observations.<br>Australia's Top 10 Trading Partners are sourced from:- Observatory of Economic Complexity (https://oec.world).<br>A version of this data (2018 - 19) was published in the Journal of Telecommunications and the Digital Economy (JTDE link below), Figure 3 (Ferrers 2020) in 2020, and presented at a Telsoc NBN Future Forum on Learning from International Experiences. Data from paper is at Figshare (link below).<br>File is provided in formats:- numbers with paper graph, and formulae- csv (for browsing)- png version of Figure 3 from JTDE paper.<br>This dataset is intended to be updated annually. Speedtest provides monthly updates.<br>Versions:v3 - include graph image from JTDE paper (Figure 3) 2018 - 19.v2 - Amended title to include yearsv1 - Initial Load
DOI: 10.6084/m9.figshare.13249955
title: Example and unit testing input for REGENS algorithm

description: All genotype data was downloaded from here:<br>https://www.cog-genomics.org/plink/2.0/resources<br><br>I filtered it to 500000 quality controlled SNPs that exist in all subpopulations. <br>More information about what I did is here:<br>https://github.com/EpistasisLab/regens/tree/main<br><br>note: reuploaded once to adjust paths in test files to accommodate REGENS as a package
DOI: 10.6084/m9.figshare.13249955.v2
title: Example and unit testing input for REGENS algorithm

description: All genotype data was downloaded from here:<br>https://www.cog-genomics.org/plink/2.0/resources<br><br>I filtered it to 500000 quality controlled SNPs that exist in all subpopulations. <br>More information about what I did is here:<br>https://github.com/EpistasisLab/regens/tree/main<br><br>note: reuploaded once to adjust paths in test files to accommodate REGENS as a package
DOI: 10.6084/m9.figshare.13249955.v1
title: Example and unit testing input for REGENS algorithm

description: All genotype data was downloaded from here:<br>https://www.cog-genomics.org/plink/2.0/resources<br><br>I filtered it to 500000 quality controlled SNPs that exist in all subpopulations. <br>More information about what I did is here:<br>https://github.com/EpistasisLab/regens/tree/main<br>
DOI: 10.6084/m9.figshare.13269680
title: dataset 1.xlsx

description: glioma molecular
DOI: 10.6084/m9.figshare.13269680.v1
title: dataset 1.xlsx

description: glioma molecular
DOI: 10.6084/m9.figshare.13265540
title: 1.xlsx

description: gbmmolecular<br>
DOI: 10.6084/m9.figshare.13265540.v3
title: 1.xlsx

description: gbmmolecular<br>
DOI: 10.6084/m9.figshare.13265540.v2
title: 1.xlsx

description: gbmmolecular<br>
DOI: 10.5519/qd.xnqshxgh
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1001 records)
DOI: 10.6084/m9.figshare.13269722
title: Exploring the industrial potential of Lactobacillus delbrueckii subsp. bulgaricus by population genomics and genome-wide association study (GWAS) analysis - supplement table

description: <i>Lactobacillus</i> <i>delbrueckii</i> subsp. <i>bulgaricus</i> (<i>L. delbrueckii</i> subsp.<i> bulgaricus</i>) is one of the most commonly used starter-cultures for yoghurt production. However, how its genetic background affects the acid production capacity is unclear. This study investigated the industrial potential of <i>L. delbrueckii</i> subsp.<i> bulgaricus</i> by population genomics and genome-wide association study (GWAS) analysis. To meet our goal, population genetics and functional genomics analyses were performed on 188 newly sequenced <i>L. delbrueckii</i> subsp.<i> bulgaricus</i> strains isolated from naturally fermented dairy products together with 19 genome sequences retrieved from the NCBI database. Four distinct clusters were identified, and they were correlated with the geographical sites where the samples were collected. The GWAS analysis about acidification fermentation result with sucrose fortified reconstituted skimmed milk revealed significant association between the L-lactate dehydrogenase (<i>lld</i>D; Ldb2036) and the bacterial acid production rate. Our study has broadened the understanding of the population structure and genetic diversity of <i>L. delbrueckii</i> subsp.<i> bulgaricus </i>by identifying potential targets for further research, development, and utilization of lactic acid bacteria in the dairy industry.