cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.34740/kaggle/dsv/3974578
title: Air_Condition_data(A/C)
DOI: 10.5281/zenodo.6885661
title: Cacatoblastsis_cactorum_cleaned_data

description: Cleaned database for <em>Cactoblastis cactorum</em> occurrences downloaded from the GBIF repository on 09/25/2021
DOI: 10.5281/zenodo.6885660
title: Cacatoblastsis_cactorum_cleaned_data

description: Cleaned database for <em>Cactoblastis cactorum</em> occurrences downloaded from the GBIF repository on 09/25/2021
DOI: 10.6084/m9.figshare.20361840
title: S_tragus_fastQ106

description: FastQ files from site 106
DOI: 10.6084/m9.figshare.20361840.v2
title: S_tragus_fastQ106

description: FastQ files from site 106
DOI: 10.6084/m9.figshare.20361840.v1
title: S_tragus_fastQ106

description: FastQ files from site 106
DOI: 10.34740/kaggle/dsv/3971903
title: Monkeypox Skin Images Dataset (MSID)
DOI: 10.5281/zenodo.6885966
title: Pareas carinatus zanfona 1

description: .
DOI: 10.5281/zenodo.6885965
title: Pareas carinatus zanfona 1

description: .
DOI: 10.17909/f8p1-e696
title: JWST/NIRCam CAR-32 data
DOI: 10.6084/m9.figshare.20361864.v1
title: S_tragus_fastQ161

description: FastQ sequences for site 161
DOI: 10.6084/m9.figshare.20361864
title: S_tragus_fastQ161

description: FastQ sequences for site 161
DOI: 10.6073/pasta/7e4d2de5b39244e200f194c2ec20ef90
title: Model Simulations of The Effects of Shifts in High-frequency Weather Variability (With a Long-term Trend) on Carbon Loss from Land to the Atmosphere, Toolik Lake, Alaska, 2022-2122

description: Climate change is increasing extreme weather events, but effects on high-frequency weather variability and the resultant impacts on ecosystem function are poorly understood. We assessed ecosystem responses of arctic tundra to changes in day-to-day weather variability using a biogeochemical model and stochastic simulations of daily temperature, precipitation, and light. Changes in weather variability altered ecosystem carbon, nitrogen, and phosphorus stocks and cycling rates. Some responses of processes (e.g., respiration) were inconsistent with expectations, indicating that whole-ecosystem interactions and feedbacks moderate or even reverse responses to weather variability. More weather variability led to greater carbon losses from land to atmosphere, and less variability led to higher carbon sequestration on land. The magnitude of response to weather variability was similar to that predicted from climate mean trend effects. This dataset consists of the MEL parameter file, driver files and output files for simulations with a long-term weather trend.
DOI: 10.5281/zenodo.6886007
title: Pseudoxenodon macrops zanfona 1

description: .
DOI: 10.5281/zenodo.6886006
title: Pseudoxenodon macrops zanfona 1

description: .
DOI: 10.34740/kaggle/dsv/3974647
title: Greater Gcode
DOI: 10.6073/pasta/ad095b6847b756b2dca6bdfb1fcb3edf
title: Shifts in High-frequency Weather Variability (No Long Term Weather Trend) Control Carbon Loss from Land to the Atmosphere, Toolik Lake, Alaska, 2022-2122

description: Climate change is increasing extreme weather events, but effects on high-frequency weather variability and the resultant impacts on ecosystem function are poorly understood. We assessed ecosystem responses of arctic tundra to changes in day-to-day weather variability using a biogeochemical model and stochastic simulations of daily temperature, precipitation, and light. Changes in weather variability altered ecosystem carbon, nitrogen, and phosphorus stocks and cycling rates. Some responses of processes (e.g., respiration) were inconsistent with expectations, indicating that whole-ecosystem interactions and feedbacks moderate or even reverse responses to weather variability. More weather variability led to greater carbon losses from land to atmosphere, and less variability led to higher carbon sequestration on land. The magnitude of response to weather variability was similar to that predicted from climate mean trend effects. This dataset consists of the MEL parameter file, driver files and output files for simulations without a long term weather trend.
DOI: 10.34740/kaggle/ds/2351793
title: Greater Gcode
DOI: 10.34740/kaggle/dsv/3974654
title: Greater Gcode
DOI: 10.5281/zenodo.6406967
title: Dataset and code for "Modelling the potential for peat-block transplants to restore industrially contaminated Sphagnum peatlands"

description: Code to run simulations and dataset from HYDRUS-1D simulations used in the revised manuscript titled "Modelling the potential for peat-block transplants to restore industrially contaminated Sphagnum peatlands" submitted to Ecological Engineering
DOI: 10.5281/zenodo.6406966
title: Dataset and code for "Modelling the potential for peat-block transplants to restore industrially contaminated Sphagnum peatlands"

description: Code to run simulations and dataset from HYDRUS-1D simulations used in the revised manuscript titled "Modelling the potential for peat-block transplants to restore industrially contaminated Sphagnum peatlands" submitted to Ecological Engineering
DOI: 10.34683/sr643
title: StoneRounds Case: SR643
title: Type 2 Usher Syndrome

description: StoneRounds Case SR643 was diagnosed with Type 2 Usher Syndrome likely caused by mutations in the gene: USH2A.
DOI: 10.5281/zenodo.6886195
title: Pseudoxyrhopus heterurus zanfona 1

description: .
DOI: 10.5281/zenodo.6886194
title: Pseudoxyrhopus heterurus zanfona 1

description: .
DOI: 10.6084/m9.figshare.20361975
title: Data of cellular nutrients and macromolecules for Chlorella vulgaris samples collected on day 8.

description: Data of cellular nutrients and macromolecules for Chlorella vulgaris samples collected on day 8.
DOI: 10.6084/m9.figshare.20361975.v1
title: Data of cellular nutrients and macromolecules for Chlorella vulgaris samples collected on day 8.

description: Data of cellular nutrients and macromolecules for Chlorella vulgaris samples collected on day 8.
DOI: 10.5281/zenodo.6886225
title: Dataset for A Revision of the Formation Conditions of the Vredefort Crater.

description: Dataset associated with Allen et al. 2022 - <em>A Revision of the Formation Conditions of the Vredefort Crater. </em>Contained are all of our input asteroid.inp files, along with their associated material.inp file (the same of which was used for all of the simulations), the full jdata.dat file for our Model A, and a movie of our three discussed models over the full time of the simulation. All simulations were created using iSALE (https://isale-code.github.io/), and some python code showing potential visualization techniques for the data can be found at https://github.com/natalieallen/iSALE_scripts.
DOI: 10.5281/zenodo.6886247
title: Sibynophis collaris zanfona 1

description: .
DOI: 10.5281/zenodo.6886246
title: Sibynophis collaris zanfona 1

description: .
DOI: 10.5281/zenodo.6886306
title: Simalia boeleni zanfona 1

description: .
DOI: 10.5281/zenodo.6886305
title: Simalia boeleni zanfona 1

description: .
DOI: 10.34740/kaggle/ds/2358905
title: 1000 scientific papers from PlosONE
DOI: 10.6073/pasta/da8886a7490ef2fa7edf1590e3a5f2c9
title: Model Simulations of The Effects of Shifts in High-frequency Weather Variability (No Long-term Weather Trend) Control Carbon Loss from Land to the Atmosphere, Toolik Lake, Alaska, 2022-2122

description: Climate change is increasing extreme weather events, but effects on high-frequency weather variability and the resultant impacts on ecosystem function are poorly understood. We assessed ecosystem responses of arctic tundra to changes in day-to-day weather variability using a biogeochemical model and stochastic simulations of daily temperature, precipitation, and light. Changes in weather variability altered ecosystem carbon, nitrogen, and phosphorus stocks and cycling rates. Some responses of processes (e.g., respiration) were inconsistent with expectations, indicating that whole-ecosystem interactions and feedbacks moderate or even reverse responses to weather variability. More weather variability led to greater carbon losses from land to atmosphere, and less variability led to higher carbon sequestration on land. The magnitude of response to weather variability was similar to that predicted from climate mean trend effects. This dataset consists of the MEL parameter file, driver files and output files for simulations without a long term weather trend.
DOI: 10.6084/m9.figshare.20362008
title: Higher priority of N than of P for controlling phytoplankton growth: Inapplicability of the growth-rate hypothesis to Chorella vulgaris under low nutrient status

description: dataset 1.csv: Figure 1a and b <br> dataset 2.csv: Figure 1c and d, and Figures 2 ~ 6 <br> SEM.csv: data for SEM analysis in R <br> SEM.R: R script for SEM analysis <br> loessspline.csv: data for nonparametric analysis <br> nonpara.R: R script for nonparametric analysis
DOI: 10.6084/m9.figshare.20362008.v1
title: Higher priority of N than of P for controlling phytoplankton growth: Inapplicability of the growth-rate hypothesis to Chorella vulgaris under low nutrient status

description: dataset 1.csv: Figure 1a and b <br> dataset 2.csv: Figure 1c and d, and Figures 2 ~ 6 <br> SEM.csv: data for SEM analysis in R <br> SEM.R: R script for SEM analysis <br> loessspline.csv: data for nonparametric analysis <br> nonpara.R: R script for nonparametric analysis
DOI: 10.6073/pasta/ca1abeb0a32b8f9018bfc088ef9412de
title: Model Simulations of The Effects of Shifts in High-frequency Weather Variability (With a Long-term Trend) on Carbon Loss from Land to the Atmosphere, Toolik Lake, Alaska, 2022-2122

description: Climate change is increasing extreme weather events, but effects on high-frequency weather variability and the resultant impacts on ecosystem function are poorly understood. We assessed ecosystem responses of arctic tundra to changes in day-to-day weather variability using a biogeochemical model and stochastic simulations of daily temperature, precipitation, and light. Changes in weather variability altered ecosystem carbon, nitrogen, and phosphorus stocks and cycling rates. Some responses of processes (e.g., respiration) were inconsistent with expectations, indicating that whole-ecosystem interactions and feedbacks moderate or even reverse responses to weather variability. More weather variability led to greater carbon losses from land to atmosphere, and less variability led to higher carbon sequestration on land. The magnitude of response to weather variability was similar to that predicted from climate mean trend effects. This dataset consists of the MEL parameter file, driver files and output files for simulations with a long-term weather trend.
DOI: 10.6084/m9.figshare.20362035
title: S_tragus_fastQ49

description: fastQ files for site 49
DOI: 10.6084/m9.figshare.20362035.v1
title: S_tragus_fastQ49

description: fastQ files for site 49
DOI: 10.34740/kaggle/dsv/3974829
title: Country Data
DOI: 10.22003/xfel.eu-data-900257-00
title: SCS commissioning
DOI: 10.22003/xfel.eu-data-900270-00
title: SASE2 Instrumentation Commissioning
DOI: 10.13012/b2idb-6560908_v1
title: Raw data and code for the paper: Do Nearctic hover flies (Diptera: Syrphidae) engage in long-distance migration? An assessment of evidence and mechanisms

description: This dataset is for the publication "Do Nearctic hover flies (Diptera: Syrphidae) engage in long-distance migration? An assessment of evidence and mechanisms." It consists of 11 Excel spreadsheets and 4 R scripts which correspond to the analyses which were conducted. Paper abstract: Long-distance insect migration is poorly understood despite its tremendous ecological and economic importance. As a group, Nearctic hover flies (Diptera: Syrphidae: Syrphinae), which are crucial pollinators as adults and biological control agents as larvae, are almost entirely unrecognized as migratory despite examples of highly migratory behavior among several Palearctic species. Here, we examined evidence and mechanisms of migration for four hover fly species (Allograpta obliqua, Eupeodes americanus, Syrphus rectus, and Syrphus ribesii) common throughout eastern North America using stable hydrogen isotope (δ2H) measurements of chitinous tissue, morphological assessments, abundance estimations, and cold-tolerance assays. While further studies are needed, non-local isotopic values obtained from hover fly specimens collected in central Illinois support the existence of long-distance fall migratory behavior in Eu. americanus, and to a lesser extent S. ribesii and S. rectus. Elevated abundance of Eu. americanus during the expected autumn migratory period further supports the existence of such behavior. Moreover, high phenotypic plasticity of morphology associated with dispersal coupled with significant differences between local and non-local specimens suggest that Eu. americanus exhibits a unique suite of morphological traits that decrease costs associated with long-distance flight. Finally, compared to the ostensibly non-migratory A. obliqua, Eu. americanus was less cold tolerant, a factor that may be associated with migratory behavior. Collectively, our findings imply that fall migration occurs in Nearctic hover flies, but we consider methodological limitations of our study in addition to potential ecological and economic consequences of these novel findings.
DOI: 10.6084/m9.figshare.20362152
title: Integrated forward and reverse cross-docking with vehicle routing and scheduling problem: two inspection strategies and a genetic-mathematic algorithm

description: The detailed data of all instances for both modes.
DOI: 10.6073/pasta/9a1f33fdc75e3ec0c3ac4fe79081f749
title: Snow depth data for Saddle grid, 1992 - ongoing.

description: The depth of snow was measured at 88 points on the saddle grid. The 500 x 350 m study area (17.5 ha) consisted of a grid of 8 rows of stakes in an east/west direction and 11 rows of stakes in a north/south direction (for a total of 88 stakes). The stakes were located 50 m apart. Each stake was given a point identification number starting with 1 in the southwest corner and progressing in an easterly direction for each of the east/west rows so that if head of this file represented the north compass point and the tail represented the south compass point, then the grid would look like this: 71 72 73 74 75 76 77 78 79 80 801(=80A) 61 62 63 64 65 66 67 68 69 70 701(=70A) 51 52 53 54 55 56 57 58 59 60 601(=60A) 41 42 43 44 45 46 47 48 49 50 501(=50A) 31 32 33 34 35 36 37 38 39 40 401(=40A) 21 22 23 24 25 26 27 28 29 30 301(=30A) 11 12 13 14 15 16 17 18 19 20 201(=20A) 1 2 3 4 5 6 7 8 9 10 101(=10A) Note that stakes along the east boundary of the grid, i.e. those ending with the "A", were given new designations to facilitate incorporation of the data into the Saddle GIS. Snow depths at each of the stakes were recorded on a weekly to biweekly basis throughout the period during which snow accumulation existed on the Saddle.
DOI: 10.5281/zenodo.6862159
title: LSSTC AGN Data Challenge 2021

description: This repository hosts the dataset used in the LSSTC AGN Data Challenge (DC) 2021 (PI: Gordon Richards). More information about the data challenge can be found in the DC GitHub repository @ https://github.com/RichardsGroup/AGN_DataChallenge. <strong>Dataset Versions: </strong> <strong>1.0: </strong>The initial dataset used in the DC, as well as the blinded dataset (ObjectTable_Blinded.parquet) that was used to evaluate submissions. Note that the image cutouts are not included here due to the large size, but the script used to generate those cutouts using SDSS archive services is included in the DC GitHub repository. <strong>1.1: </strong>The same dataset as in v1.0 but with the following updates: Uncovered the true coordinates of each source in the dataset Added E(B-V) for every source using the SFD1998 dust map Added spectrum source information (i.e., SDSS fiber, plate, mjd) if available. <strong>Caveat:</strong> The optical (grizY) and NIR photometry of sources in the XMM-LSS field is a product of the HSC/VISTA pixel-level joint processing initiative led by Raphael Shirley and Manda Banerji. Thus, it is an early prototype dataset and is still subject to testing and characterization. <strong>Citation:</strong> The DC dataset released here is a compilation of data from various sources. If you find the DC dataset useful for your research and would like to acknowledge it, please also reference the original sources of the data. Below is a list of publications that you should consider citing. <em>X-ray in XMM-LSS (XMM-SERVS): </em>2018MNRAS.478.2132C <em>UV Photometry (GALEX): </em>2017ApJS..230...24B <em>Optical Photometry (in the object/source tables): </em> DES: 2021ApJS..255...20A SDSS Stripe 82 Coadd: 2014ApJ...794..120A HSC DR2: 2019PASJ...71..114A <em>Optical Light Curves (in the ForcedSource table):</em> SDSS DR7: 2009ApJS..182..543A SDSS II Supernova Survey: 2008AJ....135..338F <em>Astrometry (i.e., parallax, proper motion):</em> Gaia EDR3: 2021A&A...649A...1G NOIRLab Source Catalog DR2: 2021AJ....161..192N <em>NIR in XMM-LSS (VISTA/VIDEO): </em>2013MNRAS.428.1281J <em>NIR in Stripe 82 (UKIDSS):</em> 2006MNRAS.367..454H 2007MNRAS.379.1599L 2008MNRAS.384..637H 2009MNRAS.394..675H <em>Optical u-band in XMM-LSS (CFHTLS):</em> 2012yCat.2317....0H <em>MIR in XMM-LSS </em>(Spitzer DeepDrill): 2021MNRAS.501..892L <em>MIR in Stripe 82 </em>(SpIES): 2016ApJS..225....1T <em>FIR</em> (Hershel/HELP): 2019MNRAS.490..634S <em>Radio</em> (FIRST): 1994ASPC...61..165B <em>HighZ QSOs: </em> 2016ApJ...819...24W 2016ApJ...829...33Y <em>SDSS Spectroscopy:</em> SDSS DR16: 2020ApJS..249....3A SDSS DR16 Quasar Catalog: 2020ApJS..250....8L
DOI: 10.5281/zenodo.6878414
title: LSSTC AGN Data Challenge 2021

description: This repository hosts the dataset used in the LSSTC AGN Data Challenge (DC) 2021 (PI: Gordon Richards). More information about the data challenge can be found in the DC GitHub repository @ https://github.com/RichardsGroup/AGN_DataChallenge. <strong>Dataset Versions: </strong> <strong>1.0: </strong>The initial dataset used in the DC, as well as the blinded dataset (ObjectTable_Blinded.parquet) that was used to evaluate submissions. Note that the image cutouts are not included here due to the large size, but the script used to generate those cutouts using SDSS archive services is included in the DC GitHub repository. <strong>1.1: </strong>The same dataset as in v1.0 but with the following updates: Uncovered the true coordinates of each source in the dataset Added E(B-V) for every source using the SFD1998 dust map Added spectrum source information (i.e., SDSS fiberid, plate, mjd) if available. <strong>Caveat:</strong> The optical (grizY) and NIR photometry of sources in the XMM-LSS field is a product of the HSC/VISTA pixel-level joint processing initiative led by Raphael Shirley and Manda Banerji. Thus, it is an early prototype dataset and is still subject to testing and characterization. <strong>Citation:</strong> The DC dataset released here is a compilation of data from various sources. If you find the DC dataset useful for your research and would like to acknowledge it, please also reference the original sources of the data. Below is a list of publications that you should consider citing. <em>X-ray in XMM-LSS (XMM-SERVS): </em>2018MNRAS.478.2132C <em>UV Photometry (GALEX): </em>2017ApJS..230...24B <em>Optical Photometry (in the object/source tables): </em> DES: 2021ApJS..255...20A SDSS Stripe 82 Coadd: 2014ApJ...794..120A HSC DR2: 2019PASJ...71..114A <em>Optical Light Curves (in the ForcedSource table):</em> SDSS DR7: 2009ApJS..182..543A SDSS II Supernova Survey: 2008AJ....135..338F <em>Astrometry (i.e., parallax, proper motion):</em> Gaia EDR3: 2021A&A...649A...1G NOIRLab Source Catalog DR2: 2021AJ....161..192N <em>NIR in XMM-LSS (VISTA/VIDEO): </em>2013MNRAS.428.1281J <em>NIR in Stripe 82 (UKIDSS):</em> 2006MNRAS.367..454H 2007MNRAS.379.1599L 2008MNRAS.384..637H 2009MNRAS.394..675H <em>Optical u-band in XMM-LSS (CFHTLS):</em> 2012yCat.2317....0H <em>MIR in XMM-LSS </em>(Spitzer DeepDrill): 2021MNRAS.501..892L <em>MIR in Stripe 82 </em>(SpIES): 2016ApJS..225....1T <em>FIR</em> (Hershel/HELP): 2019MNRAS.490..634S <em>Radio</em> (FIRST): 1994ASPC...61..165B <em>HighZ QSOs: </em> 2016ApJ...819...24W 2016ApJ...829...33Y <em>SDSS Spectroscopy:</em> SDSS DR16: 2020ApJS..249....3A SDSS DR16 Quasar Catalog: 2020ApJS..250....8L
DOI: 10.5281/zenodo.6862158
title: LSSTC AGN Data Challenge 2021

description: This repository hosts the dataset used in the LSSTC AGN Data Challenge (DC) 2021 (PI: Gordon Richards). More information about the data challenge can be found in the DC GitHub repository @ https://github.com/RichardsGroup/AGN_DataChallenge. <strong>Dataset Versions: </strong> <strong>1.0: </strong>The initial dataset used in the DC, as well as the blinded dataset (ObjectTable_Blinded.parquet) that was used to evaluate submissions. Note that the image cutouts are not included here due to the large size, but the script used to generate those cutouts using SDSS archive services is included in the DC GitHub repository. <strong>1.1: </strong>The same dataset as in v1.0 but with the following updates: Uncovered the true coordinates of each source in the dataset Added E(B-V) for every source using the SFD1998 dust map Added spectrum source information (i.e., SDSS fiberid, plate, mjd) if available. <strong>Caveat:</strong> The optical (grizY) and NIR photometry of sources in the XMM-LSS field is a product of the HSC/VISTA pixel-level joint processing initiative led by Raphael Shirley and Manda Banerji. Thus, it is an early prototype dataset and is still subject to testing and characterization. <strong>Citation:</strong> The DC dataset released here is a compilation of data from various sources. If you find the DC dataset useful for your research and would like to acknowledge it, please also reference the original sources of the data. Below is a list of publications that you should consider citing. <em>X-ray in XMM-LSS (XMM-SERVS): </em>2018MNRAS.478.2132C <em>UV Photometry (GALEX): </em>2017ApJS..230...24B <em>Optical Photometry (in the object/source tables): </em> DES: 2021ApJS..255...20A SDSS Stripe 82 Coadd: 2014ApJ...794..120A HSC DR2: 2019PASJ...71..114A <em>Optical Light Curves (in the ForcedSource table):</em> SDSS DR7: 2009ApJS..182..543A SDSS II Supernova Survey: 2008AJ....135..338F <em>Astrometry (i.e., parallax, proper motion):</em> Gaia EDR3: 2021A&A...649A...1G NOIRLab Source Catalog DR2: 2021AJ....161..192N <em>NIR in XMM-LSS (VISTA/VIDEO): </em>2013MNRAS.428.1281J <em>NIR in Stripe 82 (UKIDSS):</em> 2006MNRAS.367..454H 2007MNRAS.379.1599L 2008MNRAS.384..637H 2009MNRAS.394..675H <em>Optical u-band in XMM-LSS (CFHTLS):</em> 2012yCat.2317....0H <em>MIR in XMM-LSS </em>(Spitzer DeepDrill): 2021MNRAS.501..892L <em>MIR in Stripe 82 </em>(SpIES): 2016ApJS..225....1T <em>FIR</em> (Hershel/HELP): 2019MNRAS.490..634S <em>Radio</em> (FIRST): 1994ASPC...61..165B <em>HighZ QSOs: </em> 2016ApJ...819...24W 2016ApJ...829...33Y <em>SDSS Spectroscopy:</em> SDSS DR16: 2020ApJS..249....3A SDSS DR16 Quasar Catalog: 2020ApJS..250....8L
DOI: 10.6084/m9.figshare.20362164
title: S_tragus_fastQ89

description: FastQ files for site 89
DOI: 10.6084/m9.figshare.20362164.v1
title: S_tragus_fastQ89

description: FastQ files for site 89
DOI: 10.6073/pasta/22d8cbaa21c2cf5211b37adcb5fd1a6f
title: Snow water equivalent data for Niwot Ridge and Green Lakes Valley, 1993 - ongoing.

description: Snow pits were excavated at various locations on Niwot Ridge and within the Green Lakes Valley. Temperature and snow density were measured at various depths throughout the snow cover profiles to characterize the temperature and snow water equivalent (SWE) of the snowpack throughout the year. Snow density was measured at 10-cm intervals using a 1000-ml cutter. Data on snow grain qualities were collected beginning in the 1994-95 snow season.