cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5066/f76h4gbf
title: Climate Impact Meteorological Stations (CLIM-MET) Data from The Mojave National Preserve, California and Canyonlands National Park, Utah 1998-2016.

description: The CLIM-MET stations are meteorological/geological stations that are designed to function in remote areas for long periods of time without human intervention. These stations measure meteorological and wind-erosion parameters under varying climatic and land-use conditions to detect and describe ongoing landscape changes. Combined with historic and other data, CLIM-MET data can provide inputs into regional climatic models that describe how the Southwest will respond to future climatic conditions.
DOI: 10.5066/p9qt9v3k
title: Greenhouse gas fluxes, dissolved gas concentrations, and water properties of laboratory mesocosms

description: These data describe greenhouse gas (GHG) emissions, dissolved GHG concentrations, and mesocosm conditions (vegetation, hydrologic phases, ambient conditions) of a controlled, laboratory study.
DOI: 10.5066/p9svpk0n
title: Comparisons of Gambel's quail survey methods conducted in 2016 within the Mohave Desert of California with results and summaries

description: Data includes results collected in the Mohave Desert during 2016 using three survey methods (acoustic recording units, camera traps, and call-count surveys) to detect gamebirds, primarily Gambel's quail (Callipepla gambelii) but also including records of mourning dove (Zenaida macroura), Eurasian collared-dove (Streptopelia decaocto), and chukar (Alectoris chukar). ARU data includes the Selections (portions of the recording identified as potentially containing a Gambel's quail call) and Detections (quail calls confirmed following audible and visual verification of the recording and spectrogram). ARU data provided is summarized into hourly segments where selections occurred among five study sites. Camera trap data includes summary of the number of individuals and number of individual photos that game cameras recorded when deployed at four wildlife guzzlers for 5 species of animal that were detected: Gambel's quail, mourning dove, coyote (Canis latrans), Agassiz's desert tortoise (Gopherus agassizii), and red-tailed hawk (Buteo jamaicensis). Call-count data consists of individual station visit records, including non-detections, among 10 roadside transects containing 12 equally spaced survey stations and include time of detection, estimated distance and direction to the call/observation and number of individuals detected (primarily related to visual detections). These data support the following publication: Overton, C.T., Casazza, M.L., Connelley, D., Gardner, S., 2020. Gambel's quail survey variability and implications for survey design in the Mohave Desert. Wildlife Society Bulletin, In-press.
DOI: 10.5255/ukda-sn-854681
title: Longitudinal Ecosystem Service Data from Preah Vihear Province, Cambodia, 2019-2020

description: We are interested in people’s use of nature to benefit themselves and their households (both directly and indirectly). We are conducting surveys through mobile phones to understand who uses nature’s benefits, how and why they use it, when they use it and where they use it most. We hope to have participants from two locations in Cambodia: Phnom Penh as our urban case study and Preah Vihear as a rural case study. The participants will become our citizen scientists and collect the data via an app, or by receiving phone calls, though the information they provide will be kept strictly confidential (as outlined below). We hope that the data generated will enable us to create a map of the most important places in these regions to the local people, and understand what problems people face in maximising their benefits from nature. The project will take 12 months to complete, with participants receiving small data and talk-time for weekly participation as well as building up credit to ultimately own the phone once the survey is complete (details below). The project will include multiple short tasks each week. Each task will have a set number of points. These points build up and each week you will be given data and talk time corresponding to the number of tasks you have completed that week. It is important to remember that you will not be penalised for failing to complete a task. You can gain the credits later by participating as much as possible in all remaining tasks. Each task completed credit will also gain credit towards owning the phone at the end of the year, when the project is completed. The tasks focus on food, culture, water, wild goods, wellbeing, demographics, income, poverty and natural hazards
DOI: 10.5281/zenodo.1323475
title: Seismic And Meteorological Records From Trakarding-Trambau Glacier System, Nepal Himalaya (October 21 - November 9, 2017)

description: The following geophysical field data (collected between October 21 and November 9, 2017) at Trakarding-Trambau Glacier system in Nepal Himalaya is provided in this dataset: 

 

(1) Hourly air-temperature measured at four sites (AWS, T1, T2 and T3). Units are degrees Celcius. Local time.

[file name] Hourly_Air_temperature_AWS_T1_T2_T3_degC.csv

 

(2) Hourly wind speed measured at the AWS site. Units are meters per second. Local time.

[file name] Hourly_Wind_speed_AWS_MperS.csv

 

The aforementioned stations had the following dGPS-derived coordinates:

{Station, lat [deg], lon [deg], elevation [m] }

AWS, 27.84356265, 86.4867231, 4805.582206

T1, 27.84575824, 86.49189978, 4591.4429

T2, 27.82976456, 86.51989785, 4768.8465

T3, 27.855872, 86.531197, 5390.48

 

(3) Raw seismic data (vertical component) recorded at five locations (DAM, C1, C2, C3, and C4) by four data-loggers (AKX, AKS, AKT, AKU) with a sampling frequency of 400 Hz. The file format (*.pri0) corresponds to a standard [.mseed]-format. Units are counts representing velocity. UTC time.

 

The aforementioned stations had the following dGPS-derived coordinates:

{StationName-Logger, lat [deg], lon [deg], elevation [m] }

 

DAM-AKX, 27.870954, 86.463243, 4375.030762

C1-AKS, 27.84546885, 86.49247316, 4594.0462

C2-AKT, 27.82939476, 86.52001441, 4777.5211

C3-AKU, 27.83593425, 86.53248327, 5288.3311

C4-AKX, 27.87962877, 86.542076, 5555.2085
description: The field work was supported by JSPS-KAKENHI grants 26257202 and 17H1621.
DOI: 10.5281/zenodo.2525663
title: Clay content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution

description: Clay content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. Based on machine learning predictions from global compilation of soil profiles and samples. Processing steps are described in detail <strong>here</strong>. Antarctica is not included. To access and visualize maps use: OpenLandMap.org If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels: Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/issues General questions and comments: https://disqus.com/home/forums/landgis/ All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: sol = theme: soil, clay.wfraction = variable: sand weight fraction, usda.3a1a1a = determination method: laboratory method code, m = mean value, 250m = spatial resolution / block support: 250 m, b10..10cm = vertical reference: 10 cm depth below surface, 1950..2017 = time reference: period 1950-2017, v0.2 = version number: 0.2,
description: {"references": ["USDA-NRCS, (2014) Laboratory Methods Manual (SSIR 42). U.S. Department of Agriculture, Natural Resources Conservation Service, National Soil Survey Center.", "Hengl T, Mendes de Jesus J, Heuvelink GBM, Ruiperez Gonzalez M, Kilibarda M, Blagoti\u0107 A, et al. (2017) SoilGrids250m: Global gridded soil information based on machine learning. PLoS ONE 12(2): e0169748.", "Hengl, T., MacMillan, R.A., (2019). Predictive Soil Mapping with R. OpenGeoHub foundation, Wageningen, the Netherlands, 370 pages, www.soilmapper.org, ISBN: 978-0-359-30635-0."]}
DOI: 10.5676/dwd_gpcc/clim_m_v2018_025
title: GPCC Precipitation Climatology Version 2018 at 0.25°: Monthly Land-Surface Precipitation Climatology for Every Month and the Total Year from Rain-Gauges built on GTS-based and Historic Data
title: Globally Gridded Monthly Totals

description: The new Global Precipitation Climatology is focussing on the period 1951-2000 and consists of data from ca. 80,000 stations.
			The climatology comprises normals collected by WMO (CLINOs), delivered by the countries to GPCC or calculated from time-series of monthly data (with at least 10 complete years of data) available in our data base.
			In case that time series of sufficient length (more than 40 years) for the period 1951-2000 were not available from a specific station, then climatological normals have also been calculated for 30-year reference periods 1961-1990, 1951-1980 or 1971-2000 with at least 20 years of data.
			If even this was not possible for a station, then normals have been calculated for the period 1931-1960, or for any other period with at least 10 complete years of data.
			This climatology is available on a regular latitude/longitude grid with a spatial resolution of 0.25° x 0.25°, 0.5° x 0.5°, 1.0° x 1.0°, and 2.5° x 2.5°.
			This GPCC product has been developed as a background field, for superposition of an interpolated anomaly field according to the period of interest.
			All other GPCC products utilize the background fields of the GPCC Climatology to grid the monthly data at the corresponding resolution with the anomaly interpolation method.
description: General user recommendations: Users are referred to following gridded Global Precipitation Climate Centre (GPCC) products: GPCC Climatology 1951-2000 (GPCC_CLIM) with high representativeness, for application as a reference, and for utilization of the anomaly interpolation method. GPCC Full Data Reanalysis (GPCC_FD) with high accuracy, for, e.g., verification of models, or for analysis of historic global precipitation, or for research concerning the global water cycle, e.g., trend and teleconnection analyses. GPCC Monitoring Product (GPCC_MP) with high availability and reasonable timeliness, for applications like calibration of satellite data, or early annual reporting. GPCC First Guess Product (GPCC_FG) with high timeliness, to be applied for, e.g., drought monitoring.
DOI: 10.6084/m9.figshare.2758129.v2
title: A Methodology for Exploring, Documenting, and Improving Humanitarian Service Learning in the University

description: <i>Published in the Journal of Humanitarian Engineering.</i><i><br></i><b>Abstract</b>Through the use of service learning in higher education, universities hope to both provide real benefit to the partnering community and allow students to develop a greater understanding of course curriculum, their discipline, and their personal positioning within society. Through these educational activities, service learning seeks to engage students in critical thinking processes while simultaneously achieving a greater sense of civic and social responsibility through targeted participation in meaningful community service activities. However, in practice, service learning can take a variety of forms predicated on technical, cultural, societal, and political constraints. Thus, while some work shows positive effects on students’ attitudes, social behaviour, and academic performance, less research has demonstrated long-term community impact. Nor has much research shown that participation in service learning has a long-term impact on students' ethical perspectives and frameworks, and whether those ethical frames carry on to their professional careers. Moreover, as institutions partner with such humanitarian service groups as Engineers Without Borders USA, we know considerably less about the institutional cultures and climates that are developed through such partnerships and how sustainable they are, given those inherent technical, political and cultural limitations. As a first step towards these goals, this paper proposes a methodology for investigating the impacts of service learning activities on both the students and communities involved.<br>
DOI: 10.6084/m9.figshare.840464
title: Gaussian Job Archive for C7H16O3

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.840465
title: Gaussian Job Archive for C7H16O3

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.841757
title: Gaussian Job Archive for C13H26LiNO

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.842572
title: Gaussian Job Archive for C13H26LiNO

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.845578
title: Gaussian Job Archive for C17H34LiNO2

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.845579
title: Gaussian Job Archive for C17H34LiNO2

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.15485/1498798
title: Estimated Darcy Velocities Using Temperature Time Series for Meander A of East River, Colorado

description: Heat was used as a tracer to quantify seasonal changes in vertical Darcy velocity through the streambed at three different locations about Meander A of East River, Colorado. The file Raw_Temp.csv represents raw temperature data (in degrees Celsius) collected from the three locations, A, B, and C, at depths of 5, 10, 20, and 35 cm below the sediment-water interface. Darcy velocity estimates were calculated from these data using 1DTempPro and are shown in 1DTempPro_estimates.csv file. Positive values represent upwelling conditions, while negative values indicate downwelling conditions.
DOI: 10.17632/jyxknn3gmr.1
title: MPK3- and MPK6-Mediated ICE1 Phosphorylation Negatively Regulates ICE1 Stability and Freezing Tolerance in Arabidopsis

description: Low temperatures affect plant growth, development, productivity, and ecological distribution. Expression of the C-repeat-binding factor (CBF) transcription factors is induced by cold stress, which in turn activates downstream cold-responsive (COR) genes that are required for the acquisition of freezing tolerance. Inducer of CBF expression 1 (ICE1) is a master regulator of CBFs, and ICE1 stability is crucial for its function. However, the regulation of ICE1 is not well understood. Here, we report that mitogen-activated protein kinase 3 (MPK3) and MPK6 interact with and phosphorylate ICE1, which reduces its stability and transcriptional activity. Consistently, the mpk3 and mpk6 single mutants and the mpk3 mpk6 double mutant show enhanced freezing tolerance, whereas MPK3/MPK6 activation attenuates freezing tolerance. Phosphor-inactive mutations of ICE1 complement freezing sensitivity in the ice1-2 mutant. These combined results indicate that MPK3/MPK6 phosphorylate and destabilize ICE1, which negatively regulates CBF expression and freezing tolerance in plants.
DOI: 10.34881/kyap6j
title: Barriers to academic collaboration with industry and community

description: In the beginning of 2021, a quantitative survey was conducted among academics in Icelandic universities to gather information about their involvement in Third Mission activities and barriers to collaboration. The target group consisted of all academics, including adjunct, assistant, associate, and full professors, working at any of the seven Icelandic universities. The majority of participants (n = 674) were affiliated with the University of Iceland, while the remaining (n = 360) were associated with other universities at the time of data collection. The survey questions were designed based on insights from a literature review, as well as drawing inspiration from previous studies on barriers to university-industry collaboration. Before the survey was administered, it underwent a pre-test phase involving various academics and university staff members, resulting in necessary adjustments. Email addresses of potential participants were obtained from the universities’ public websites. The survey itself was conducted using QuestionPro and remained open for a period of 21 days, with two reminders being sent following the initial invitation to participate. A total of 183 responses were collected, although not all participants completed the entire questionnaire. Consequently, the response rate amounted to 17.7%.
DOI: 10.5281/zenodo.2525676
title: Silt content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution

description: Silt content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. Based on machine learning predictions from global compilation of soil profiles and samples. Processing steps are described in detail <strong>here</strong>. Antarctica is not included. To access and visualize maps use: OpenLandMap.org If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels: Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/issues General questions and comments: https://disqus.com/home/forums/landgis/ All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: sol = theme: soil, silt.wfraction = variable: silt weight fraction, usda.3a1a1a = determination method: laboratory method code, m = mean value, 250m = spatial resolution / block support: 250 m, b10..10cm = vertical reference: 10 cm depth below surface, 1950..2017 = time reference: period 1950-2017, v0.2 = version number: 0.2,
description: {"references": ["Hengl, T., MacMillan, R.A., (2019). Predictive Soil Mapping with R. OpenGeoHub foundation, Wageningen, the Netherlands, 370 pages, www.soilmapper.org, ISBN: 978-0-359-30635-0."]}
DOI: 10.5281/zenodo.4322984
title: MINDS-Libras Dataset (RGB-D sensor data)

description: Brazilian Sign Language (Libras) data set with 20 signs for sign language and gesture recognition benchmark:<br> <br> - Acontecer (To happen)<br> - Aluno (Student)<br> - Amarelo (Yellow)<br> - América (America)<br> - Aproveitar (To enjoy)<br> - Bala (Candy)<br> - Banco (Bank)<br> - Banheiro (Bathroom)<br> - Barulho (Noise)<br> - Cinco (Five)<br> - Conhecer (To know)<br> - Espelho (Mirror)<br> - Esquina (Corner)<br> - Filho (Son)<br> - Maçã (Apple)<br> - Medo (Fear)<br> - Ruim (Bad)<br> - Sapo (Frog)<br> - Vacina (Vaccine)<br> - Vontade (Will)<br> <br> Each one of the signs was recorded 5 times by 12 signers, using a Chroma Key background. Among the signers are men and women with basic to advanced knowledge in Libras. The RGB-D sensor (kinect v2) available the RGB videos (1920 x 1080) and depth videos (640 x 480) in "mp4" format, and the body points and face data in "txt" file. The body file has seven different information (Position X, Y and Z; Orientation X, Y and Z; TrackingState; LeftHandState; RightHandState; ColorPosition X and Y; and DepthPosition X and Y) about the 25 points: (1) Spine Base, (2) Spine Mid, (3) Neck, (4) Head, (5) Shoulder Left, (6) Elbow Left, (7) Wrist Left, (8) Hand Left, (9) Shoulder Right, (10) Elbow Right, (11) Wrist Right, (12) Hand Right, (13) Hip Left, (14) Knee Left, (15) Ankle Left, (16) Foot Left, (17) Hip Right, (18) Knee Right, (19) Ankle Right, (20) Foot Right, (21) Spine Shoulder, (22) Hand Tip Left, (23) Thumb Left, (24) Hand Tip Right and (25) Thumb Right. There are 13 lines (or data) for each frame. This order is repeated sequentially up to 1950 lines (13 lines $\times$ 150 frames), representing the sign video. Regarding to the face data, the same organisation was adopted. In this case, we have seven information (FaceBox, FaceRotation, HeadPivot, AnimationUnit, FaceModel X, Y and Z; ColorFaceModel X and Y; and DepthFaceModel X and Y), describing 11 data, distributed in 1650 (11 lines $\times$ 150 frames) lines in the ``.txt'' file. (Former name: Libras-20)
DOI: 10.5284/1000120
title: West African pottery decorated using roulettes

description: In 2008, an interdisciplinary and international team of archaeologists, ethnographers and museologists was convened in Oxford and Dakar by Dr Anne Haour and Dr Katie Manning (Sainsbury Research Unit [SRU], University of East Anglia), in order to debate the definition and characteristics of a type of pottery-decorating tool called 'roulettes'.
DOI: 10.5967/m0gm85fg
title: Steady and transient sand bedform evolution at Clear Run, North Carolina, September 2009
DOI: 10.6073/pasta/00e0b5842fb0a64a2191a4afb93031cc
title: Witness Tree Database for 68 Townships in Northern Wisconsin, Level 0

description: This database is a collation and digitization of witness tree attributes at 6,609 PLS corners in 68 scattered townships over nine counties (Ashland County east to Florence County and south to Rush County) in northern Wisconsin. These are the same townships used in Manies, K. L., Mladenoff, D. J., and Nordheim, E. V. 2001. Assessing large-scale surveyor variability in the historic forest data of the original US Public Land Survey. Canadian Journal of Forest Research 31(10): 1719-1730.
DOI: 10.6073/pasta/ab7ff96c75a62e0d05b6efb8c02eae29
title: Witness Tree Database for 4 Survey Districts in Southeastern Ohio, Level 0

description: This database is a collation and digitization of witness tree attributes at PLS corners in four survey districts in southeastern Ohio: Old Seven Ranges, North of the Seven Ranges, United States Military Tract, Ohio Land Company.
DOI: 10.6084/m9.figshare.840490
title: Gaussian Job Archive for C8H17N3O

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.15485/1504779
title: Depth-resolved seasonal porewater chemistry measurements from 3 locations around Meander A of the East River, Colorado.

description: Depth-resolved pore water samples were recovered from 3 locations around Meander A in the East River near Crested Butte, CO, USA for a suite of physiochemical analyses. Supplemental_Information.csv contains water quality measurements including pH, conductivity, temperature, ORP, TDS, O2 concentration, metal concentrations, carbon chemistry, and isotopic signatures; Coordinates.csv contains information regarding sampling locations; and Analysis_Notes.csv contains information regarding instrumentation and detection limits.