cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21223/fo7kpo/7kiz5k
title: 04_PTGlycoalkaloids062019_HCHO_exp1_data_dictionary.tab

description: :unav
DOI: 10.21223/fo7kpo/l6dsdl
title: 02_PTGlycoalkaloids062019_HCHO_exp1_material_list.xlsx

description: :unav
DOI: 10.21223/fo7kpo/boosyk
title: 01_PTGlycoalkaloids062019_HCHO_exp1_data.tab

description: :unav
DOI: 10.21223/fo7kpo/jmpnqk
title: 02_PTGlycoalkaloids062019_HCHO_exp1_installation.tab

description: :unav
DOI: 10.34943/d0ac0caa-fb85-447b-8346-3566c57a6e3f
title: Chatham Sound Hydrophone Deployed 2018-10-26

description: The Ocean Sonics icListen HF Hydrophone 1354 was deployed on 2018-10-26 at Chatham Sound. Chatham Sound is a sound on the North Coast of British Columbia, located between Dundas and Stephens Islands and the Tsimpsean Peninsula near Prince Rupert. This device is a Hydrophone. Hydrophones are devices containing transducers that convert underwater sound waves into electrical signals. They are acoustic instruments that can process data while they are being collected to produce calibrated waveform data. Hydrophones are typically used to study vocalizations of marine mammals, ship traffic and ambient noise. It was deployed on a fixed platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 2.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.25346/s6/izbuqf/h5oqzo
title: fish_reference_collection.tab
DOI: 10.25346/s6/izbuqf
title: Mangaia fish comparative collection

description: Comparative collection of Mangaia fishes obtained by Virginia Butler in 1991.
DOI: 10.5281/zenodo.5722304
title: Chamaetylas choloensis decontaminated FSCR

description: .
DOI: 10.5281/zenodo.5722303
title: Chamaetylas choloensis decontaminated FSCR

description: .
DOI: 10.25502/d6c8-1m40/d
title: Response of entomopathogenic fungi to Relative humidity 

description: Many entomopathogenic fungi have been screened for the development of myco-insecticide product against various insect pests but thier failure in the field have been attributed to environmental challenges. Thefore, development as microbial biopesticide products require selection of virulence strains which can tolerate abiotic factors known to be detrimental for fungi either during industrial production or in the field against target host pest. Our study was performed to investigate the effect of some environmental parameters on growth of two Cameroonian isolate, one M. anisopliae and one B. bassiana. Colony formation, mycelia growth and conidia production were assessed on PDA media at fourteen pH level (2 – 14), three photoperiods (continuous light, continuous dark, and 12h light/12h dark), five exposure time under UV light and four relative humidity levels (35, 50, 75 and 99 %). Results revealed that Cameroonian isolates successfully grew at acidic to alkaline medium with wide pH tolerance and optimum ranges. Light regimes affected mostly mycelia growth and fungi pigmentation which increased with the photophase extension. Cameroonian fungal isolates also exhibited high UVB tolerance with Beauveria isolate remaining active during all exposure time while Metarhium isolate did not form colonies at 45 min exposure under UVB light even when incubation time was extended by 6 days. Exposure to UV light pulse duration 30 min stimulated mycelia growth and conidia production for BIITAC6.2.2 and only mycelia growth for MIITAC11.3.4. Fungi development was found effective at low to high humidity with preference of moderate humidity environment for both strains. Climate change mitigation for the development of biopesticide is here discussed.
DOI: 10.5281/zenodo.5722366
title: Supplementary materials

description: This zip file contains all excel sheets developed for the framework and demonstrates cases that represent all the inputs present in the figures.
DOI: 10.5281/zenodo.5722365
title: Supplementary materials

description: This zip file contains all excel sheets developed for the framework and demonstrates cases that represent all the inputs present in the figures.
DOI: 10.5281/zenodo.5722216
title: Alethe castanea decontaminated FSCR

description: .
DOI: 10.5281/zenodo.5722215
title: Alethe castanea decontaminated FSCR

description: .
DOI: 10.6084/m9.figshare.17064287
title: Chevalier_SA_COVID19_Thresholds

description: Dataset containing 1% COVID-19 import thresholds by Rt and recipient country prevalence. Compiled from the calculations in COVID19 Threshold Calculations excel dataset.
DOI: 10.6084/m9.figshare.17064227
title: Chevalier_Nichols_COVID19_Threshold_Calculations

description: This excel dataset relies on COVID-19 SEIR model output obtained from covid19-scenarios.org. Output varies by the number of infectious COVID-19 imports per day to generate the change in incidence over a one-month period under different levels of the effective reproductive number and starting prevalence. This output was used to calculate theoretical thresholds of infectious imports into a country that would lead to no more than a 1% increase in community transmission.
DOI: 10.6084/m9.figshare.17064227.v1
title: Chevalier_Nichols_COVID19_Threshold_Calculations

description: This excel dataset relies on COVID-19 SEIR model output obtained from covid19-scenarios.org. Output varies by the number of infectious COVID-19 imports per day to generate the change in incidence over a one-month period under different levels of the effective reproductive number and starting prevalence. This output was used to calculate theoretical thresholds of infectious imports into a country that would lead to no more than a 1% increase in community transmission.
DOI: 10.6084/m9.figshare.17064287.v1
title: Chevalier_SA_COVID19_Thresholds

description: Dataset containing 1% COVID-19 import thresholds by Rt and recipient country prevalence. Compiled from the calculations in COVID19 Threshold Calculations excel dataset.
DOI: 10.25502/4sh2-rp30/d
title: Response of entomopathogenic fungi to pH levels

description: Many entomopathogenic fungi have been screened for the development of myco-insecticide product against various insect pests but thier failure in the field have been attributed to environmental challenges. Thefore, development as microbial biopesticide products require selection of virulence strains which can tolerate abiotic factors known to be detrimental for fungi either during industrial production or in the field against target host pest. Our study was performed to investigate the effect of some environmental parameters on growth of two Cameroonian isolate, one M. anisopliae and one B. bassiana. Colony formation, mycelia growth and conidia production were assessed on PDA media at fourteen pH level (2 – 14), three photoperiods (continuous light, continuous dark, and 12h light/12h dark), five exposure time under UV light and four relative humidity levels (35, 50, 75 and 99 %). Results revealed that Cameroonian isolates successfully grew at acidic to alkaline medium with wide pH tolerance and optimum ranges. Light regimes affected mostly mycelia growth and fungi pigmentation which increased with the photophase extension. Cameroonian fungal isolates also exhibited high UVB tolerance with Beauveria isolate remaining active during all exposure time while Metarhium isolate did not form colonies at 45 min exposure under UVB light even when incubation time was extended by 6 days. Exposure to UV light pulse duration 30 min stimulated mycelia growth and conidia production for BIITAC6.2.2 and only mycelia growth for MIITAC11.3.4. Fungi development was found effective at low to high humidity with preference of moderate humidity environment for both strains. Climate change mitigation for the development of biopesticide is here discussed.
DOI: 10.34943/5f572daa-eb2d-47cf-8a08-b9315c3574c5
title: Strait of Georgia East Hydrophone Deployed 2018-10-01

description: The Ocean Sonics icListen HF Hydrophone 1560 was deployed on 2018-10-01 at Strait of Georgia East. Strait of Georgia East refers to an eastern area of the Strait of Georgia. This device is a Hydrophone. Hydrophones are devices containing transducers that convert underwater sound waves into electrical signals. They are acoustic instruments that can process data while they are being collected to produce calibrated waveform data. Hydrophones are typically used to study vocalizations of marine mammals, ship traffic and ambient noise. It was deployed on a fixed platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 2.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.21223/5tqsrm/jo04ja
title: 03_PTGlycoalkaloids062019_HCO_exp1_material_list.tab

description: :unav
DOI: 10.21223/5tqsrm/c0qjlb
title: 01_PTGlycoalkaloids062019_HCO_exp1_data.tab

description: :unav
DOI: 10.21223/5tqsrm/sxld58
title: 03_PTGlycoalkaloids062019_HCO_exp1_data_dictionary.tab

description: :unav
DOI: 10.21223/5tqsrm/gf4pkq
title: 02_PTGlycoalkaloids062019_HCO_exp1_material-list.xlsx

description: :unav
DOI: 10.5281/zenodo.5721781
title: Reference dataset for comparison of cloud detection algorithms for Sentinel-2 imagery

description: Sentinel-2 cloud mask reference dataset generated and analyzed as part of Tarrio, K., Tang, X., Masek, J.G., Claverie, M., Ju, J., Qiu, S., Zhu, Z. and Woodcock, C.E., 2020. Comparison of cloud detection algorithms for Sentinel-2 imagery. Science of Remote Sensing, 2, p.100010. [https://www.sciencedirect.com/science/article/pii/S2666017220300092](https://www.sciencedirect.com/science/article/pii/S2666017220300092) <strong>1. Reference masks</strong> <strong>Algorithms:</strong> Fmask 1.x Fmask 2.x Fmask 4.x Tmask Sen2Cor MAJA LaSRC <strong>Locations:</strong> South Africa (35JPM) Senegal (28PDC) Switzerland (32TLT) France (31TCJ, 31TFJ) Morocco (29RNQ) <strong>Standardized legend:</strong> Original algorithm outputs were standardized to the same categorical legend. 0 = clear land 1 = clear water 2 = cloud shadow 3 = snow/ice 4 = cloud All reference masks processed to both 10m and 30m resolution, with the exception of Tmask, which is available only at a 30m resolution. <strong>Mask naming convention:</strong> All processed masks are named according to the following convention:<br> M&lt;*resolution*&gt;&lt;*S2 MGRS tile ID*&gt;&lt;*YYYY*&gt;&lt;*DOY*&gt;&lt;*algorithm*&gt;<br> e.g. **M30T28PDC2016351TMASK** <br> <strong>2. Interpreted sample points</strong> Sample points were selected based on agreement among different map products. This record includes a shapefile with the final interpretations for each of the sampled sites. (See publication for additional information.)
description: This research was funded by NASA through both the Harmonized Landsat Sentinel effort and the Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, as well as the USGS through the Landsat Science Team.
description: {"references": ["Tarrio et al. (2020)"]}
DOI: 10.5281/zenodo.5721780
title: Reference dataset for comparison of cloud detection algorithms for Sentinel-2 imagery

description: Sentinel-2 cloud mask reference dataset generated and analyzed as part of Tarrio, K., Tang, X., Masek, J.G., Claverie, M., Ju, J., Qiu, S., Zhu, Z. and Woodcock, C.E., 2020. Comparison of cloud detection algorithms for Sentinel-2 imagery. Science of Remote Sensing, 2, p.100010. [https://www.sciencedirect.com/science/article/pii/S2666017220300092](https://www.sciencedirect.com/science/article/pii/S2666017220300092) <strong>1. Reference masks</strong> <strong>Algorithms:</strong> Fmask 1.x Fmask 2.x Fmask 4.x Tmask Sen2Cor MAJA LaSRC <strong>Locations:</strong> South Africa (35JPM) Senegal (28PDC) Switzerland (32TLT) France (31TCJ, 31TFJ) Morocco (29RNQ) <strong>Standardized legend:</strong> Original algorithm outputs were standardized to the same categorical legend. 0 = clear land 1 = clear water 2 = cloud shadow 3 = snow/ice 4 = cloud All reference masks processed to both 10m and 30m resolution, with the exception of Tmask, which is available only at a 30m resolution. <strong>Mask naming convention:</strong> All processed masks are named according to the following convention:<br> M&lt;*resolution*&gt;&lt;*S2 MGRS tile ID*&gt;&lt;*YYYY*&gt;&lt;*DOY*&gt;&lt;*algorithm*&gt;<br> e.g. **M30T28PDC2016351TMASK** <br> <strong>2. Interpreted sample points</strong> Sample points were selected based on agreement among different map products. This record includes a shapefile with the final interpretations for each of the sampled sites. (See publication for additional information.)
description: This research was funded by NASA through both the Harmonized Landsat Sentinel effort and the Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, as well as the USGS through the Landsat Science Team.
description: {"references": ["Tarrio et al. (2020)"]}
DOI: 10.21223/2d2nav/gguota
title: 04_PTGlycoalkaloids062019_CIPSRM-2_exp1_Dictionary.tab

description: :unav
DOI: 10.21223/2d2nav/jhd18y
title: 01_PTGlycoalkaloids062019_CIPSRM-2_exp1_Data.tab

description: :unav
DOI: 10.21223/2d2nav/hscgzp
title: 02_PTGlycoalkaloids062019_CIPSRM-2_exp1_Material_list.xlsx

description: :unav
DOI: 10.21223/2d2nav/jctwte
title: 03_PTGlycoalkaloids062019_CIPSRM-2_exp1_Installation.tab

description: :unav
DOI: 10.6084/m9.figshare.12794075
title: G-RUN ENSEMBLE

description: G-RUN ENSEMBLE (pronounced GeRUN) consists in a multi-forcing global reanalysis of monthly runoff rates created by means of machine learning and a global collection of river discharge observations. G-RUN ENSEMBLE allows for an unprecedented view on global terrestrial water dynamics on time scales ranging from months to a full century. Quantification of the uncertainty stemming from the atmospheric forcing data makes G-RUN ENSEMBLE the ideal candidate for reliable and robust water resources assessments.<br>------------------------------------------------------------------------------<b>File description </b><b><br></b>- <i>G-RUN_ENSEMBLE_MMM.nc </i>covers<i> </i>the time period from 1902 to 2019 and provide the<i> </i>median of the G-RUN ENSEMBLE members. If you want to rely on one single estimate this is likely the file you are interested in.<br>- <i>G-RUN_ENSEMBLE_MEMBERS.zip </i>contains ensemble mean reconstructions for 21 different atmospheric forcing datasets. The time range depends on the considered forcing.<br>- Each remaining file called <i>G-RUN_ENSEMBLE_*.zip (</i><i></i>where * denotes the acronym of the atmospheric forcing dataset used to force the model)<i>, </i>contains 25 runoff reconstructions obtained by training models on different subsets of the available runoff observations.<br><b> </b>------------------------------------------------------------------------------<br><b>References</b><br>- Ghiggi, G., Humphrey, V., Seneviratne, S. I., & Gudmundsson, L. (2021). G-RUN ENSEMBLE: A multi-forcing observation-based global runoff reanalysis. Water Resources Research, 57(5), e2020WR028787. https://doi.org/10.1029/2020WR028787<br><br>- Ghiggi, G., Humphrey, V., Seneviratne, S. I., & Gudmundsson, L. (2019). GRUN: an observation-based global gridded runoff dataset from 1902 to 2014. <i>Earth System Science Data</i>, <i>11</i>(4), 1655–1674. https://doi.org/10.5194/essd-11-1655-2019 <br>
DOI: 10.21227/xn4t-qq23
title: 5K EPP Dataset

description: The 5K EPP Dataset includes 5007 photos classified in 13 categories.
DOI: 10.5517/ccdc.csd.cc286nx2
title: CCDC 2091784: Experimental Crystal Structure Determination

description: Related Article: Chamila P. Manankandayalage, Daniel K. Unruh, Clemens Krempner|2021|Chem.Commun.|57|12528|doi:10.1039/D1CC05673E
DOI: 10.5517/ccdc.csd.cc286ny3
title: CCDC 2091785: Experimental Crystal Structure Determination

description: Related Article: Chamila P. Manankandayalage, Daniel K. Unruh, Clemens Krempner|2021|Chem.Commun.|57|12528|doi:10.1039/D1CC05673E
DOI: 10.5517/ccdc.csd.cc286nz4
title: CCDC 2091786: Experimental Crystal Structure Determination

description: Related Article: Chamila P. Manankandayalage, Daniel K. Unruh, Clemens Krempner|2021|Chem.Commun.|57|12528|doi:10.1039/D1CC05673E
DOI: 10.5517/ccdc.csd.cc286p06
title: CCDC 2091787: Experimental Crystal Structure Determination

description: Related Article: Chamila P. Manankandayalage, Daniel K. Unruh, Clemens Krempner|2021|Chem.Commun.|57|12528|doi:10.1039/D1CC05673E
DOI: 10.5517/ccdc.csd.cc286p17
title: CCDC 2091788: Experimental Crystal Structure Determination

description: Related Article: Chamila P. Manankandayalage, Daniel K. Unruh, Clemens Krempner|2021|Chem.Commun.|57|12528|doi:10.1039/D1CC05673E
DOI: 10.5517/ccdc.csd.cc286p28
title: CCDC 2091789: Experimental Crystal Structure Determination

description: Related Article: Chamila P. Manankandayalage, Daniel K. Unruh, Clemens Krempner|2021|Chem.Commun.|57|12528|doi:10.1039/D1CC05673E
DOI: 10.5517/ccdc.csd.cc286p39
title: CCDC 2091790: Experimental Crystal Structure Determination

description: Related Article: Chamila P. Manankandayalage, Daniel K. Unruh, Clemens Krempner|2021|Chem.Commun.|57|12528|doi:10.1039/D1CC05673E
DOI: 10.5517/ccdc.csd.cc286p4b
title: CCDC 2091791: Experimental Crystal Structure Determination

description: Related Article: Chamila P. Manankandayalage, Daniel K. Unruh, Clemens Krempner|2021|Chem.Commun.|57|12528|doi:10.1039/D1CC05673E
DOI: 10.5281/zenodo.5722384
title: Polar mesospheric clouds from the Balloon Lidar Experiment (BOLIDE) during the PMC Turbo balloon mission

description: This dataset contains BOLIDE lidar data obtained during the PMC Turbo balloon mission that was launched on 7 July 2018 from Esrange, Sweden and landed in Nunavut, Canada on 14 July 2018. The mission was designed to study small-scale atmospheric dynamics induced by breaking atmospheric gravity waves within the polar mesospheric cloud layer at ~82 km altitude. PMC Turbo floated at around 40 km altitude and carried seven digital cameras to image the polar mesospheric cloud layer and the first Rayleigh lidar to successfully operate from a balloon. The lidar data consists of volume backscatter coefficients of polar mesospheric clouds, available at 20 m vertical and 10 s temporal resolution, contained in a compressed netcdf file. The magnitude of volume backscatter coefficients scales with<br> the brightness of clouds imaged by the PMC Turbo cameras. The netcdf file further includes floating altitude, rotator angle (azimuth) as well as latitude and longitude of the lidar beam at 82 km altitude. Users are encouraged to contact us for discussion when using BOLIDE data. Data contact: natalie.kaifler@dlr.de References: PMC Turbo camera videos: https://svs.gsfc.nasa.gov/13073 NASA Space Physics Data Facility: https://cdaweb.gsfc.nasa.gov/index.html/, select PMC Turbo DLR Institute mission database: https://halo-db.pa.op.dlr.de/mission/112 Kaifler, N., Kaifler, B., Rapp, M., Fritts, D.C. The polar mesospheric cloud dataset of the Balloon Lidar Experiment BOLIDE. Earth System Science Data. In preparation. Kaifler, B., Rempel, D., Roßi, P., Büdenbender, C., Kaifler, N., and Baturkin, V.: A technical<br> description of the Balloon Lidar Experiment (BOLIDE), Atmos. Meas. Tech., 13, 5681–5695,<br> https://doi.org/10.5194/amt-13-5681-2020, 2020. Fritts, D. C., Miller, A. D., Kjellstrand, C. B., Geach, C., Williams, B. P., Kaifler, B.,<br> et al. (2019). PMC Turbo: Studying gravity wave and instability dynamics in the summer mesosphere<br> using polar mesospheric cloud imaging and profiling from a stratospheric balloon. Journal of<br> Geophysical Research: Atmospheres, 124, 6423– 6443. https://doi.org/10.1029/2019JD030298
DOI: 10.18739/a2sq8qj7t
title: The climate envelope of Alaska’s northern treelines: implications for controlling factors and future treeline advance. Primary data and analyses 2019 - 2021

description: Understanding the key mechanisms that control northern treelines is important to accurately predict biome shifts and terrestrial feedbacks to climate. At a global scale, it has long been observed that elevational and latitudinal treelines occur at similar mean growing season air temperature (GSAT) isotherms, inspiring the growth limitation hypothesis (GLH) that cold GSAT limits aboveground growth of treeline trees, with mean treeline GSAT ~6-7 degrees celsius (°C). Treelines with mean GSAT warmer than 6-7 °C may indicate other limiting factors. Many treelines globally are not advancing despite warming, and other climate variables are rarely considered at broad scales. Our goals were to test whether current boreal treelines in northern Alaska correspond with the GLH isotherm, determine which environmental factors are most predictive of treeline presence, and to identify areas beyond the current treeline where advance is most likely. We digitized ~12,400 kilometers (km) of treelines (greater than 26K points) and computed seasonal climate variables across northern Alaska. We then built a generalized additive model predicting treeline presence to identify key factors determining treeline. Two metrics of mean GSAT at Alaska’s northern treelines were consistently warmer than the 6-7 °C isotherm (means of 8.5 °C and 9.3 °C), indicating that direct physiological limitation from low GSAT is unlikely to explain the position of treelines in northern Alaska. Our final model included cumulative growing degree-days, near-surface (≤ 1 meters (m)) permafrost probability, and growing season total precipitation, which together may represent the importance of soil temperature. Our results indicate that mean GSAT may not be the primary driver of treeline in northern Alaska or that its effect is mediated by other more proximate, and possibly non-climatic, controls. Our model predicts treeline potential in several areas beyond current treelines, pointing to possible routes of treeline advance if unconstrained by non-climatic factors.
DOI: 10.5281/zenodo.4688394
title: Transmission, infectivity, and neutralization of a spike L452R SARS-CoV-2 variant

description: This dataset contains the following: genome fasta files from viral whole-genome sequencing of SARS-CoV-2 positive nasal/nasopharyngeal swabs codes used to analyze epidemiologic data (i.e. logistic growth curves, transmission rate, doubling time) and generate plots and figures codes used to perform statistical analysis and visualization of viral loads phylogenetic analyses description, trees, and logs supplementary tables with relevant sample information
DOI: 10.5281/zenodo.4688393
title: Transmission, infectivity, and neutralization of a spike L452R SARS-CoV-2 variant

description: This dataset contains the following: genome fasta files from viral whole-genome sequencing of SARS-CoV-2 positive nasal/nasopharyngeal swabs codes used to analyze epidemiologic data (i.e. logistic growth curves, transmission rate, doubling time) and generate plots and figures codes used to perform statistical analysis and visualization of viral loads phylogenetic analyses description, trees, and logs supplementary tables with relevant sample information
DOI: 10.3886/e155261
title: Nonlinear Class Size Effects on Cognitive and Noncognitive Development of Young Children

description: The data and programs replicate tables and figures from Nonlinear Class Size Effects on Cognitive and Noncognitive Development of Young Children, by Marie Connolly and Catherine Haeck, to be published in the Journal of Labor Economics. Please see the ReadMe file for additional details.<br>
DOI: 10.3886/e155261v1
title: Nonlinear Class Size Effects on Cognitive and Noncognitive Development of Young Children

description: The data and programs replicate tables and figures from Nonlinear Class Size Effects on Cognitive and Noncognitive Development of Young Children, by Marie Connolly and Catherine Haeck, to be published in the Journal of Labor Economics. Please see the ReadMe file for additional details.<br>