cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.14698794
title: Dataset for shifting long term N impacts on plant productivity

description: Although some long-term studies have been conducted to quantify nitrogen (N) impacts on plant productivity, uncertainties remain regarding whether these impacts change over time and the underlying mechanisms. By overlooking this, we may over- or under-estimate N impacts on terrestrial ecosystems. Our goal was to determine whether N impacts on plant productivity change in the long term and the controls. We synthesized 63 N addition studies with duration ≥ 8 years in natural terrestrial ecosystems. Our results show shifting temporal N impacts on plant productivity in terrestrial ecosystems, and the direction of this change varies with biome type (forests: decrease; grasslands and shrublands: increase). The temporal pattern of N impacts is mostly regulated by mean annual precipitation (MAP), mean annual temperature (MAT), and initial soil pH, which account for 24%, 19%, and 19% of the variation, respectively. Our findings indicate that temporally dynamic impacts of long-term N addition on plant productivity is generally observed among studies in terrestrial ecosystems, and climate variables shape the temporal pattern of long-term N impacts on plant productivity. Therefore, not only the magnitude of N impacts on plant productivity, but also their temporal dynamics, should be considered in future experimental and model research.
DOI: 10.15468/7kaz3b
title: Two new enigmatic genera of huntsman spiders from Madagascar (Araneae Sparassidae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Jäger, Peter (2021): Two new enigmatic genera of huntsman spiders from Madagascar (Araneae Sparassidae). Zootaxa 4984 (1): 335-346, DOI: https://doi.org/10.11646/zootaxa.4984.1.24
DOI: 10.25345/c5w556
title: MassIVE MSV000087984 - Untarget metabolomic analyses of P. aeruginosa, knockout mutants and co-culture with A. fumigatus
title: Untarget metabolomic analyses of P. aeruginosa, knockout phZA1 and phZA2 mutants and co-culture with A. fumigatus.
DOI: 10.5063/sq8xts
title: Salmon age, sex, and length data from Arctic-Yukon-Kuskokwim Region of Alaska, 1960-2017

description: Age, sex and length data provide population dynamics information that can indicate how populations trends occur and may be changing. These data can help researchers estimate population growth rates, age-class distribution and population demographics. Knowing population demographics, growth rates and trends is particularly valuable to fisheries managers who must perform population assessments to inform management decisions. These data are therefore particularly important in valuable fisheries like the salmon fisheries of Alaska. This dataset includes age, sex and length data compiled from annual sampling of commercial and subsistence salmon harvests and research projects in the Arctic-Yukon-Kuskokwim region of Alaska. It includes data on five salmon species: chinook, chum, coho, pink and sockeye. There is some data on non-salmon species (whitefish, sheefish, longnose sucker, Dolly Varden) in the Yukon dataset. There is an additional data table for more specific information about the locations where samples were collected. Age estimates were made by examining scales or bony structures (e.g. otoliths - ear bones). Scales were removed from the side of the fish; usually the left side above the lateral line. Scales or bony structures were then mounted on gummed cards and pressed on acetate to make an impression. The number of freshwater and saltwater annuli (i.e. rings) was counted to estimate age in years. Fish sex was determined by either examining external morphology (eg. head and belly shape) or internal sex organ. Length was measured in millimeters, generally from mid-eye to the fork of the tail. This data package includes the original data files, reformatting scripts that reformat the original data files into a consistent format, and the reformatted datasets.
DOI: 10.5281/zenodo.5196676
title: Density-driven flow experiment data

description: This data is the measured flux of MEG in sand through image processing technique. The experiment is designed to recover the density-driven instability and following convection process in the geological CO2 storage.
DOI: 10.5281/zenodo.5196675
title: Density-driven flow experiment data

description: This data is the measured flux of MEG in sand through image processing technique. The experiment is designed to recover the density-driven instability and following convection process in the geological CO2 storage.
DOI: 10.5517/ccdc.csd.cc26853s
title: CCDC 2033634: Experimental Crystal Structure Determination

description: Related Article: Niklas Rinn, Lukas Guggolz, Han Yu Hou, Stefanie Dehnen|2021|Chem.-Eur.J.|27|11167|doi:10.1002/chem.202101139
DOI: 10.5517/ccdc.csd.cc26854t
title: CCDC 2033635: Experimental Crystal Structure Determination

description: Related Article: Niklas Rinn, Lukas Guggolz, Han Yu Hou, Stefanie Dehnen|2021|Chem.-Eur.J.|27|11167|doi:10.1002/chem.202101139
DOI: 10.5517/ccdc.csd.cc26856w
title: CCDC 2033637: Experimental Crystal Structure Determination

description: Related Article: Niklas Rinn, Lukas Guggolz, Han Yu Hou, Stefanie Dehnen|2021|Chem.-Eur.J.|27|11167|doi:10.1002/chem.202101139
DOI: 10.5517/ccdc.csd.cc26855v
title: CCDC 2033636: Experimental Crystal Structure Determination

description: Related Article: Niklas Rinn, Lukas Guggolz, Han Yu Hou, Stefanie Dehnen|2021|Chem.-Eur.J.|27|11167|doi:10.1002/chem.202101139
DOI: 10.5517/ccdc.csd.cc2688f6
title: CCDC 2033737: Experimental Crystal Structure Determination

description: Related Article: Niklas Rinn, Lukas Guggolz, Han Yu Hou, Stefanie Dehnen|2021|Chem.-Eur.J.|27|11167|doi:10.1002/chem.202101139
DOI: 10.5517/ccdc.csd.cc2688g7
title: CCDC 2033738: Experimental Crystal Structure Determination

description: Related Article: Niklas Rinn, Lukas Guggolz, Han Yu Hou, Stefanie Dehnen|2021|Chem.-Eur.J.|27|11167|doi:10.1002/chem.202101139
DOI: 10.5517/ccdc.csd.cc2688h8
title: CCDC 2033739: Experimental Crystal Structure Determination

description: Related Article: Niklas Rinn, Lukas Guggolz, Han Yu Hou, Stefanie Dehnen|2021|Chem.-Eur.J.|27|11167|doi:10.1002/chem.202101139
DOI: 10.5517/ccdc.csd.cc268870
title: CCDC 2033731: Experimental Crystal Structure Determination

description: Related Article: Niklas Rinn, Lukas Guggolz, Han Yu Hou, Stefanie Dehnen|2021|Chem.-Eur.J.|27|11167|doi:10.1002/chem.202101139
DOI: 10.5517/ccdc.csd.cc26xvcc
title: CCDC 2053513: Experimental Crystal Structure Determination

description: Related Article: Niklas Rinn, Lukas Guggolz, Han Yu Hou, Stefanie Dehnen|2021|Chem.-Eur.J.|27|11167|doi:10.1002/chem.202101139
DOI: 10.5517/ccdc.csd.cc2686j7
title: CCDC 2033678: Experimental Crystal Structure Determination

description: Related Article: Niklas Rinn, Lukas Guggolz, Han Yu Hou, Stefanie Dehnen|2021|Chem.-Eur.J.|27|11167|doi:10.1002/chem.202101139
DOI: 10.5517/ccdc.csd.cc2687vl
title: CCDC 2033719: Experimental Crystal Structure Determination

description: Related Article: Niklas Rinn, Lukas Guggolz, Han Yu Hou, Stefanie Dehnen|2021|Chem.-Eur.J.|27|11167|doi:10.1002/chem.202101139
DOI: 10.5517/ccdc.csd.cc27n7w1
title: CCDC 2075043: Experimental Crystal Structure Determination

description: Related Article: Ferenc Najóczki, Mária Szabó, Norbert Lihi, Antal Udvardy, István Fábián|2021|Molecules|26|3632|doi:10.3390/molecules26123632
DOI: 10.5517/ccdc.csd.cc27n7x2
title: CCDC 2075044: Experimental Crystal Structure Determination

description: Related Article: Ferenc Najóczki, Mária Szabó, Norbert Lihi, Antal Udvardy, István Fábián|2021|Molecules|26|3632|doi:10.3390/molecules26123632
DOI: 10.5517/ccdc.csd.cc27n7y3
title: CCDC 2075045: Experimental Crystal Structure Determination

description: Related Article: Ferenc Najóczki, Mária Szabó, Norbert Lihi, Antal Udvardy, István Fábián|2021|Molecules|26|3632|doi:10.3390/molecules26123632
DOI: 10.5517/ccdc.csd.cc27n7z4
title: CCDC 2075046: Experimental Crystal Structure Determination

description: Related Article: Ferenc Najóczki, Mária Szabó, Norbert Lihi, Antal Udvardy, István Fábián|2021|Molecules|26|3632|doi:10.3390/molecules26123632
DOI: 10.5517/ccdc.csd.cc27p065
title: CCDC 2075766: Experimental Crystal Structure Determination

description: Related Article: Laura E. Bickerton, Andrew Docker, Alistair J. Sterling, Heike Kuhn, Fernanda Duarte, Paul D. Beer, Matthew J. Langton|2021|Chem.-Eur.J.|27|11738|doi:10.1002/chem.202101681
DOI: 10.5517/ccdc.csd.cc27p076
title: CCDC 2075767: Experimental Crystal Structure Determination

description: Related Article: Laura E. Bickerton, Andrew Docker, Alistair J. Sterling, Heike Kuhn, Fernanda Duarte, Paul D. Beer, Matthew J. Langton|2021|Chem.-Eur.J.|27|11738|doi:10.1002/chem.202101681
DOI: 10.5063/p26wjj
title: Daily salmon escapement counts from the OceanAK database, Alaska, 1921-2017

description: As salmon migrate from the marine environment to freshwater streams to spawn, a certain number are allowed to pass through, or escape the salmon fishery (frequently located near the mouth of the freshwater stream) as a management strategy to preserve the species. Escapement data are the enumeration of these migrating fish as they pass upstream, and represent an index of the total abundance of spawning salmon in that particular stream. These data are important for fisheries management, since most salmon harvest occurs in or near freshwater rivers during this migration. Escapement data are collected in a variety of ways. Stationary projects utilize observers stationed along freshwater corridors who count salmon as they pass upriver through weirs or past elevated towers. Sonar equipment placed in the river can also give a stationary escapement count. These counts usually represent a sample, and are expanded to represent a 24h period. Escapement data can also be collected using aerial surveys, where observers in an aircraft provide an index to estimate escapement. In general, escapement counts do not represent total abundance, but instead an index of abundance. Surveys are usually timed to coincide with peak spawning activity, generally in the summer, but in the case of Coho salmon in the fall as well. This dataset contains salmon escapement data extracted from the Alaska Department of Fish and Game's OceanAK database, and contains data from multiple regions in Alaska.
DOI: 10.5281/zenodo.5154004
title: The role of polyphosphates in the metabolism of multicellular cable bacteria

description: Datasets used for chapter 5 of the PhD thesis of N.M.J. Geerlings. The ecophysiology of multicellular cable bacteria:<br> insights from single cell imaging techniques Dataset 1: document (.dac) with the 18O atom fraction for each region of interest defined as a poly-P granule. These values were used for Model 1 and to construct figure 5.5 A-C. Dataset 2: document (.dac) with the 18O atom fraction of each region of interest defined as a poly-P granule and the corresponding 13C atom fraction of the cytoplasm. These fractions are only determined for the nanoSIMS data where the 12C14N- ion count was measured correctly. These values were used for Model 2 and to construct figure 5.6 and A5.3A-B.
DOI: 10.5281/zenodo.5154003
title: The role of polyphosphates in the metabolism of multicellular cable bacteria

description: Datasets used for chapter 5 of the PhD thesis of N.M.J. Geerlings. The ecophysiology of multicellular cable bacteria:<br> insights from single cell imaging techniques Dataset 1: document (.dac) with the 18O atom fraction for each region of interest defined as a poly-P granule. These values were used for Model 1 and to construct figure 5.5 A-C. Dataset 2: document (.dac) with the 18O atom fraction of each region of interest defined as a poly-P granule and the corresponding 13C atom fraction of the cytoplasm. These fractions are only determined for the nanoSIMS data where the 12C14N- ion count was measured correctly. These values were used for Model 2 and to construct figure 5.6 and A5.3A-B.
DOI: 10.25345/c5rc1h
title: MassIVE MSV000087985 - Production and composition of group B streptococcal membrane vesicles varies across diverse lineages
title: Comparative proteomics of Group B Streptococcus membrane vesicles.
DOI: 10.24431/rw1k5ai
title: Microzooplankton abundance and carbon biomass signature timeseries for the NGA LTER site, from Seward Line Cruises 2011-2018 and NGA-LTER research cruises in the Gulf of Alaska, 2018-present

description: This dataset consists of microzooplankton abundance and carbon biomass measurements obtained during Seward Line Cruises from Spring and Fall in 2011 to 2018 and NGA-LTER research cruises conducted in the Gulf of Alaska during spring, summer and fall, starting in 2018 through the present. Included in this dataset are the carbon biomass (µg/L) and abundance (cells/L) measurements of the total microzooplankton community and from the Ciliate and Dinoflagellate portions of the total community for each sample at various water depths. The data were collected to determine whether microzooplankton abundance and composition varies with time (season) and location. Acid Lugol’s fixed samples were evaluated microscopically to categorize and measure individual cells. Excel spreadsheets were converted to CSV files for preservation. These data are part of the Northern Gulf of Alaska Long Term Ecological Research (NGA LTER) program. The LTER program is a National Science Foundation–funded network of 28 sites nationwide that focus on the influence of long-term and large-scale phenomenon on ecosystems. Additional funding for sampling is provided by the North Pacific Research Board (NPRB), the Alaska Ocean Observing System (AOOS), and the Exxon Valdez Oil Spill Trustee Council (EVOS) via the Gulf Watch Alaska program.
DOI: 10.6084/m9.figshare.15127776
title: Desert v. Vinegar Flies: General Allometry

description: To calculate the statistics and generate the figures from our manuscript, do the following:<br><br>1. Unzip baja.zip, melanogaster.zip, and mojavensis.zip producing folders of the same name. In these folders are micrographs of each species, divided into male and female subfolders. In each folder is also a numpy dataset (*.npy) generated by human input by running the file ./tracker.py in each subfolder.<br><br>2. Make sure Python 3.6+ and the following packages are installed (try pip):<br> numpy<br> matplotlib<br> PIL<br> pandas<br><br>3. In the terminal, cd into this directory and run the following to display figures and print statistics, saving the figures into the project folder:<br> ~$ python3 plot.py.<br><br>The resulting statistics are saved under ./stats.txt and summarized in the spreadsheet ./stats.csv.
DOI: 10.6084/m9.figshare.15127776.v1
title: Desert v. Vinegar Flies: General Allometry

description: To calculate the statistics and generate the figures from our manuscript, do the following:<br><br>1. Unzip baja.zip, melanogaster.zip, and mojavensis.zip producing folders of the same name. In these folders are micrographs of each species, divided into male and female subfolders. In each folder is also a numpy dataset (*.npy) generated by human input by running the file ./tracker.py in each subfolder.<br><br>2. Make sure Python 3.6+ and the following packages are installed (try pip):<br> numpy<br> matplotlib<br> PIL<br> pandas<br><br>3. In the terminal, cd into this directory and run the following to display figures and print statistics, saving the figures into the project folder:<br> ~$ python3 plot.py.<br><br>The resulting statistics are saved under ./stats.txt and summarized in the spreadsheet ./stats.csv.
DOI: 10.6084/m9.figshare.15130422
title: Desert v. Vinegar Flies: Psychophysics

description: To generate the plots and statistics for the three psychophysics experiments under three lighting levels:<br> 1. Unzip all the .zip files generating folders of the same name.<br> 2. Combine the contents of the folders MEL and MEL (2) into a signel folder called MEL.<br> 3. Make sure to have Python 3.6+ and the following python packages installed:<br> -matplotlib<br> -numpy<br> -scikit-learn<br> -scipy<br> -seaborn<br> -statsmodels<br> 4. In the terminal, cd to this folder and run:<br> ~! python3 analysis.py<br> This will display and save plots including those used in the publication.<br> 5. To plot the summary line plots, run:<br> ~! python3 light_conditions.py<br><br>
DOI: 10.6084/m9.figshare.15130422.v1
title: Desert v. Vinegar Flies: Psychophysics

description: To generate the plots and statistics for the three psychophysics experiments under three lighting levels:<br> 1. Unzip all the .zip files generating folders of the same name.<br> 2. Combine the contents of the folders MEL and MEL (2) into a signel folder called MEL.<br> 3. Make sure to have Python 3.6+ and the following python packages installed:<br> -matplotlib<br> -numpy<br> -scikit-learn<br> -scipy<br> -seaborn<br> -statsmodels<br> 4. In the terminal, cd to this folder and run:<br> ~! python3 analysis.py<br> This will display and save plots including those used in the publication.<br> 5. To plot the summary line plots, run:<br> ~! python3 light_conditions.py<br><br>
DOI: 10.5281/zenodo.5196712
title: Instance data: open-shop scheduling problems with any regular minsum objective

description: Open shop scheduling instances used in the working paper Emde, S. & Lysgaard, J. (2021). Branch-cut-and-price for open-shop scheduling problems with any regular minsum objective. The instances in file <em>Brucker_instances.csv</em> are based on the test data for the classic [O||Cmax] problem from Brucker, P., Hurink, J., Jurisch, B., & Wöstmann, B. (1997). A branch & bound algorithm for the open-shop problem. <em>Discrete Applied Mathematics</em>, <em>76</em>(1-3), 43-59. They are enriched with release and due dates as well as machine-pair dependent transportation delays. The instances in file <em>random_instances.csv </em>are new. The files contain comma-separated values. Each line (except for the header) stands for one instance. The columns labels are: ID: label (identifier) of the instance n: number of jobs m: number of machines p: processing times; each square bracket stands for one machine, the values inside the brackets for the processing times of the jobs on the respective machine t: transfer times between machines; note that the dummy machine 0 where all jobs originate and end is the last machine, i.e., it has the highest index r: release dates for each job d: due dates for each job. Mj: set of machines on which the jobs must be processed; each square bracket stands for one job, values inside the brackets for machine indices. Note that indices are zero-based. In instance set <em>Brucker_instances.csv</em>, every machine is visited by every job, therefore there is no column Mj. Conversely, due dates are immaterial for the random instances because of their quadratic completion time objective. Hence, they are omitted from the table.
DOI: 10.5281/zenodo.5196711
title: Instance data: open-shop scheduling problems with any regular minsum objective

description: Open shop scheduling instances used in the working paper Emde, S. & Lysgaard, J. (2021). Branch-cut-and-price for open-shop scheduling problems with any regular minsum objective. The instances in file <em>Brucker_instances.csv</em> are based on the test data for the classic [O||Cmax] problem from Brucker, P., Hurink, J., Jurisch, B., & Wöstmann, B. (1997). A branch & bound algorithm for the open-shop problem. <em>Discrete Applied Mathematics</em>, <em>76</em>(1-3), 43-59. They are enriched with release and due dates as well as machine-pair dependent transportation delays. The instances in file <em>random_instances.csv </em>are new. The files contain comma-separated values. Each line (except for the header) stands for one instance. The columns labels are: ID: label (identifier) of the instance n: number of jobs m: number of machines p: processing times; each square bracket stands for one machine, the values inside the brackets for the processing times of the jobs on the respective machine t: transfer times between machines; note that the dummy machine 0 where all jobs originate and end is the last machine, i.e., it has the highest index r: release dates for each job d: due dates for each job. Mj: set of machines on which the jobs must be processed; each square bracket stands for one job, values inside the brackets for machine indices. Note that indices are zero-based. In instance set <em>Brucker_instances.csv</em>, every machine is visited by every job, therefore there is no column Mj. Conversely, due dates are immaterial for the random instances because of their quadratic completion time objective. Hence, they are omitted from the table.
DOI: 10.5063/j964t6
title: Salmon brood tables, Westward Region, Alaska

description: Brood tables, also called run reconstructions, utilize annual estimates of the total run (commercial catch plus escapement), and samples of ages, to estimate the number of recruits per age class. These data are useful for salmon biologists to understand salmon productivity and salmon life histories. These data can come in a number of different formats, but generally follow the pattern of: rows for each brood year, and columns for the estimated number of fish in each age class. This dataset consists of reformated brood tables from the Westward Region of Alaska. These datasets were gathered as part of the State of Alaskan Salmon and People project (https://alaskasalmonandpeople.org/). Age classes are given in European Notation, where the first number is the number of winters spent in freshwater before going to sea (1 winter in freshwater = age-1.X), and the second number is the number of winters spent at sea (3 winters at sea = age-X.3). For chinook salmon, only total age (age X) is given.
DOI: 10.6073/pasta/7d09292c9eda083096b6e878d04dd06b
title: Lagrangian Water Age trajectories initiated from the coastal 500m isobath and derived from surface velocities obtained from satellite observations

description: We conduct a Lagrangian particle trajectory analysis of surface velocities. We define an “offshore water age” as the time taken by a water parcel to be advected backward in time from its current position along its trajectory until it crosses the 500 m isobath. The rationale of this diagnostic is to detect filaments of coastal water advected offshore by horizontal transport and to estimate the time for water parcels in the filament o leave the coastal area. For example, a value of “20 days” assigned to a pixel means that the water parcel in that area was in the coastal area approximately 20 days before, where it was likely enriched in nutrients.
DOI: 10.15468/6c5wht
title: Malgacheliodes martensi spec. nov. (Acari, Oribatida, Licnodamaeidae) from South Africa

description: This dataset contains the digitized treatments in Plazi based on the original journal article Ermilov, Sergey G., Hugo-Coetzee, Elizabeth A., Khaustov, Alexander A. (2021): Malgacheliodes martensi spec. nov. (Acari, Oribatida, Licnodamaeidae) from South Africa. Zootaxa 4984 (1): 357-367, DOI: https://doi.org/10.11646/zootaxa.4984.1.26
DOI: 10.6084/m9.figshare.7531682
title: [Material suplementario] Sexo y su relación con el impacto académico en investigadores de Latinoamérica

description: Las mujeres están subrepresentadas en la producción científica. Diversos estudios demuestran que las mujeres tienen menor participación en la autoría de artículos científicos. El objetivo del presente estudio fue determinar si el sexo se asocia con el impacto académico en investigadores de países latinoamericanos.
DOI: 10.6084/m9.figshare.7531682.v2
title: [Material suplementario] Sexo y su relación con el impacto académico en investigadores de Latinoamérica

description: Las mujeres están subrepresentadas en la producción científica. Diversos estudios demuestran que las mujeres tienen menor participación en la autoría de artículos científicos. El objetivo del presente estudio fue determinar si el sexo se asocia con el impacto académico en investigadores de países latinoamericanos.