cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7402539
title: Block-level population projections in Washington state under different SSPs from 2020 to 2040

description: This shapefile contains block-level population projections in Washington state under different Shared Socioeconomic Pathways (SSPs) for every decade from 2020 to 2040. CONTENTS: Pop_proj_block.7z contains block-level rural/urban/total population projections under SSP2, SSP3, and SSP5 for 2020, 2030, and 2040. In the shape file, each population projection is stored in one column, with the first two character of the column name representing SSPs ("S2": SSP2; "S3":SSP3; "S5":SSP5), the third character indicating population type ("R": rural; "U": urban; "T": total), and the year information is attached in the end. For example, "S2R2040" represents rural population projection for year 2040 under SSP2. README.txt Illustrates population projection downscaling methods.
DOI: 10.5281/zenodo.7406280
title: Block-level population projections in Washington state under different SSPs from 2020 to 2040

description: This shapefile contains block-level population projections in Washington state under different Shared Socioeconomic Pathways (SSPs) for every decade from 2020 to 2040. 


CONTENTS:


Pop_proj_block.7z



	
contains block-level rural/urban/total population projections under SSP2, SSP3, and SSP5 for 2020, 2030, and 2040. In the shape file, each population projection is stored in one column, with the first two character of the column name representing SSPs ("S2": SSP2; "S3":SSP3; "S5":SSP5), the third character indicating population type ("R": rural; "U": urban; "T": total), and the year information is attached in the end. For example, "S2R2040" represents rural population projection for year 2040 under SSP2.



README.txt



	
Illustrates population projection downscaling methods.
DOI: 10.6084/m9.figshare.25138820
title: Experimental Samples of Dynamic Creep Test

description: A set of 118 experiment samples of dynamic creep tests was collected from the Asphalt Mixtures and Bitumen Research Center at the Iran University of Science and Technology.
DOI: 10.5519/qd.86eghtnw
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1411 records)
DOI: 10.34740/kaggle/dsv/7827778
title: Time Terror
DOI: 10.5281/zenodo.10790809
title: Data for "Leveraging a Disdrometer Network to Develop a Probabilistic Precipitation Phase Model in Eastern Canada"

description: Abstract. This study presents a probabilistic model that partitions the precipitation phase based on hourly measurements from a network of radar-based disdrometers in eastern Canada. The network consists of 27 meteorological stations located in a boreal climate for the years 2020-2023. Precipitation phase observations showed a 2-m air temperature interval between 0-4°C where probabilities of occurrence of solid, liquid, or mixed precipitation significantly overlapped. Single-phase precipitation was also found to occur more frequently than mixed-phase precipitation. Probabilistic phase-guided partitioning (PGP) models of increasing complexity using random forest algorithms were developed. The PGP models classified the precipitation phase and partitioned the precipitation accordingly into solid and liquid amounts. PGP_basic is based on 2-m air temperature and site elevation, while PGP_hydromet integrates relative humidity. PGP_full includes all the above data plus atmospheric reanalysis data. The PGP models were compared to benchmark precipitation phase partitioning methods. These included a single temperature threshold model set at 1.5°C, a linear transition model with dual temperature thresholds of –0.38 and 5°C, and a psychrometric balance model. Among the benchmark models, the single temperature threshold had the best classification performance due to a low count of mixed-phase events. The other benchmark models tended to over-predict mixed-phase precipitation in order to decrease partitioning error. All PGP models showed significant phase classification improvement by reproducing the observed overlapping precipitation phases based on 2-m air temperature. In terms of partitioning error, PGP_full had the lowest RMSE and the least variability in performance. The RMSE of the single temperature threshold model was the highest and showed the greatest performance variability. The improvement of mixed-phase prediction remains a challenge. This study establishes a basis for integrating automated phase observations into a hydrometeorological observation network and developing probabilistic precipitation phase models.
DOI: 10.5281/zenodo.10790810
title: Data for "Leveraging a Disdrometer Network to Develop a Probabilistic Precipitation Phase Model in Eastern Canada"

description: Abstract. This study presents a probabilistic model that partitions the precipitation phase based on hourly measurements from a network of radar-based disdrometers in eastern Canada. The network consists of 27 meteorological stations located in a boreal climate for the years 2020-2023. Precipitation phase observations showed a 2-m air temperature interval between 0-4°C where probabilities of occurrence of solid, liquid, or mixed precipitation significantly overlapped. Single-phase precipitation was also found to occur more frequently than mixed-phase precipitation. Probabilistic phase-guided partitioning (PGP) models of increasing complexity using random forest algorithms were developed. The PGP models classified the precipitation phase and partitioned the precipitation accordingly into solid and liquid amounts. PGP_basic is based on 2-m air temperature and site elevation, while PGP_hydromet integrates relative humidity. PGP_full includes all the above data plus atmospheric reanalysis data. The PGP models were compared to benchmark precipitation phase partitioning methods. These included a single temperature threshold model set at 1.5°C, a linear transition model with dual temperature thresholds of –0.38 and 5°C, and a psychrometric balance model. Among the benchmark models, the single temperature threshold had the best classification performance due to a low count of mixed-phase events. The other benchmark models tended to over-predict mixed-phase precipitation in order to decrease partitioning error. All PGP models showed significant phase classification improvement by reproducing the observed overlapping precipitation phases based on 2-m air temperature. In terms of partitioning error, PGP_full had the lowest RMSE and the least variability in performance. The RMSE of the single temperature threshold model was the highest and showed the greatest performance variability. The improvement of mixed-phase prediction remains a challenge. This study establishes a basis for integrating automated phase observations into a hydrometeorological observation network and developing probabilistic precipitation phase models.
DOI: 10.22002/4m0ye-fw429
title: D-SPIN constructs gene regulatory network models from multiplexed scRNA-seq data revealing organizing principles of cellular perturbation response

description: Gene regulatory networks within cells modulate the expression of the genome in response to signals and changing environmental conditions. Reconstructions of gene regulatory networks can reveal the information processing and control principles used by cells to maintain homeostasis and execute cell-state transitions. Here, we introduce a computational framework, D-SPIN, that generates quantitative models of generegulatory networks from single-cell mRNA-seq data sets collected across thousands of distinct perturbation conditions. D-SPIN models the cell as a collection of interacting gene-expression programs, and constructs a probabilistic model to infer regulatory interactions between gene-expression programs and external perturbations. Using large Perturb-seq and drug-response datasets, we demonstrate that D-SPIN models reveal the organization of cellular pathways, sub-functions of macromolecular complexes, and the logic of cellular regulation of transcription, translation, metabolism, and protein degradation in response to gene knockdown perturbations. D-SPIN can also be applied to dissect drug response mechanisms in heterogeneous cell populations, elucidating how combinations of immunomodulatory drugs can induce novel cell states through additive recruitment of gene expression programs. D-SPIN provides a computational framework for constructing interpretable models of gene-regulatory networks to reveal principles of cellular information processing and physiological control.
DOI: 10.25625/iu7w3j/ydvmyb
title: NeAr-400-2-hr.csv

description: :unav
DOI: 10.25625/iu7w3j/zkfqwx
title: He-600-2-hr.csv

description: :unav
DOI: 10.25625/iu7w3j/8pej1z
title: 2022-07-05_171728_H2O_Ne_ps300mbar_pb4mbar_d2.5mm_P15W_sw100μm_gt1350_wl650nm_scan-3-92.tab

description: :unav
DOI: 10.25625/iu7w3j/zdiqkf
title: NeAr-400-1-hr.csv

description: :unav
DOI: 10.25625/iu7w3j/qiiv6t
title: He-600-2a-lr.csv

description: :unav
DOI: 10.25625/iu7w3j/sgm9uw
title: 2022-07-05_070527_H2O_He_ps600mbar_pb11mbar_d1mm_P15W_sw100μm_gt1350_wl650nm_24x300s.tab

description: :unav
DOI: 10.25625/iu7w3j/tuumj3
title: 2022-07-04_182714_2022-07-04_H2O_He_ps600mbar_pb11mbar_d2mm_P15W_sw100μm_gt1350_wl650nm_144x300s.tab

description: :unav
DOI: 10.25625/iu7w3j/eshxxe
title: He-600-1-lr-bg.csv

description: :unav
DOI: 10.25625/iu7w3j/zvtq92
title: NeAr-400-2.5-hr.csv

description: :unav
DOI: 10.25625/iu7w3j/uco8qz
title: 2022-07-26_225115_H2O_He_ps605mbar_pb11mbar_d1.5mm_P18W_sw100μm_gt500_wl639nm_38x300s.tab

description: :unav
DOI: 10.25625/iu7w3j/3g40h8
title: He-600-2c-lr_l.csv

description: :unav
DOI: 10.25625/iu7w3j/xruw93
title: 2022-07-12_131619_H2O_90Ne_10Ar_ps410mbar_5mbar_d1mm_P18W_sw100μm_gt1350_wl650nm_15x300s.tab

description: :unav
DOI: 10.25625/iu7w3j/gni3bd
title: 2022-10-05_113805_bias_1000x18x100ms_raw.tab

description: :unav
DOI: 10.25625/iu7w3j/6g19ks
title: 2022-07-12_144855_H2O_90Ne_10Ar_ps410mbar_5mbar_d750µm_P18W_sw100μm_gt1350_wl650nm_24x300s.tab

description: :unav
DOI: 10.25625/iu7w3j/ltmiin
title: He-600-2c-lr-bias.csv

description: :unav
DOI: 10.25625/iu7w3j/886xl9
title: 2022-10-06_112159_empty_ps600mbar_pb11mbar_d2mm_P18W_sw100μm_gt1350_wl650nm_5x900s_raw.tab

description: :unav
DOI: 10.25625/iu7w3j/wgagn2
title: NeAr-400-0.75-hr.csv

description: :unav
DOI: 10.25625/iu7w3j/gqliiy
title: 2022-07-26_182620_H2O_He_ps605mbar_pb11mbar_d2mm_P18W_sw100μm_gt500_wl639nm_6x300s.tab

description: :unav
DOI: 10.25625/iu7w3j/ifesti
title: 2022-07-19_233016_H2O_Ne_ps200mbar_pb3mbar_d2mm_P18W_sw100μm_gt1350_wl650nm_32x300s.tab

description: :unav
DOI: 10.25625/iu7w3j/4o2sge
title: He-600-2c-lr-bg.csv

description: :unav
DOI: 10.25625/iu7w3j/t6sui2
title: 2022-10-06_044453_H2O_He_ps600mbar_pb11mbar_d2mm_P18W_sw100μm_gt1350_wl650nm_20x900s_raw.tab

description: :unav
DOI: 10.25625/iu7w3j/atubmu
title: 2022-07-12_173451_H2O_90Ne_10Ar_ps410mbar_pb5mbar_d2mm_P18W_sw100μm_gt1350_wl650nm_scans-3-92.tab

description: :unav
DOI: 10.25625/iu7w3j/gbsar6
title: 2022-09-14_022428_H2O_He_ps600mbar_pb10mbar_d2mm_P18W_sw100μm_gt500_wl639nm_120x300s.tab

description: :unav
DOI: 10.25625/iu7w3j/3yg9sx
title: 2022-09-12_162429_H2O_He_ps595mbar_pb11mbar_d2mm_P18W_sw100μm_gt500_wl639nm_14x300s.tab

description: :unav
DOI: 10.25625/iu7w3j/rpkxzu
title: 2022-07-14_150857_Ne_ps300mbar_pb4mbar_d2.5mm_P18W_sw100μm_gt1350_wl650nm_180x300s_water-frames.tab

description: :unav
DOI: 10.25625/iu7w3j/z8qxtd
title: 2022-10-06_132200_H2O_He_ps600mbar_pb11mbar_d2mm_P18W_sw100μm_gt1350_wl650nm_15x900s_raw.tab

description: :unav
DOI: 10.25625/iu7w3j/6selfg
title: 2022-07-28_115727_He_ps606mbar_pb11mbar_d1mm_P18W_sw100μm_gt500_wl639nm_6x300.tab

description: :unav
DOI: 10.25625/iu7w3j/twpwqy
title: 2022-07-26_190730_H2O_He_ps605mbar_pb11mbar_d2mm_P18W_sw100μm_gt500_wl639nm_42x300s.tab

description: :unav
DOI: 10.25625/iu7w3j/awk762
title: He-600-1-hr.csv

description: :unav
DOI: 10.25625/iu7w3j/txucug
title: 2022-07-05_171728_H2O_Ne_ps300mbar_pb4mbar_d2.5mm_P15W_sw100μm_gt1350_wl650nm_scan-95-184.tab

description: :unav
DOI: 10.25625/iu7w3j/k7ipja
title: 2022-07-27_022030_H2O_He_ps605mbar_pb11mbar_d1mm_P18W_sw100μm_gt500_wl639nm_44x180s.tab

description: :unav
DOI: 10.25625/iu7w3j/2tvolb
title: 2022-09-13_203451_H2O_He_ps600mbar_pb10mbar_d2mm_P18W_sw100μm_gt500_wl639nm_70x300s.tab

description: :unav
DOI: 10.25625/iu7w3j/nhgh58
title: Ne-200-2-hr.csv

description: :unav
DOI: 10.25625/iu7w3j/he2qdd
title: He-600-1.5-lr.csv

description: :unav
DOI: 10.25625/iu7w3j/zouaqy
title: 2022-09-13_140923_H2O_He_ps600mbar_pb10mbar_d2mm_P18W_sw100μm_gt500_wl639nm_25x300s.tab

description: :unav
DOI: 10.25625/iu7w3j/yjs8n9
title: 2022-07-20_065209_H2O_Ne_ps200mbar_pb3mbar_d2mm_P18W_sw100μm_gt1350_wl650nm_50x300s.tab

description: :unav
DOI: 10.25625/iu7w3j/iugyo8
title: 2022-09-12_154805_H2O_He_ps595mbar_pb11mbar_d2mm_P18W_sw100μm_gt500_wl639nm_6x300s.tab

description: :unav
DOI: 10.25625/iu7w3j/oiypla
title: 2022-10-07_070655_H2O_He_ps600mbar_pb11mbar_d2mm_P18W_sw100μm_gt1350_wl650nm_23x900s_raw.tab

description: :unav
DOI: 10.25625/iu7w3j/3yf1ez
title: 2022-09-13_171436_H2O_He_ps600mbar_pb10mbar_d2mm_P18W_sw100μm_gt500_wl639nm_40x300s.tab

description: :unav
DOI: 10.25625/iu7w3j/6zc4d0
title: He-600-1-lr.csv

description: :unav
DOI: 10.25625/iu7w3j/uunmic
title: 2022-07-28_123844_He_ps606mbar_pb11mbar_d1mm_P18W_sw100μm_gt500_wl639nm_8x300.tab

description: :unav
DOI: 10.25625/iu7w3j/bhv98j
title: 2022-07-12_173451_H2O_90Ne_10Ar_ps410mbar_pb5mbar_d2mm_P18W_sw100μm_gt1350_wl650nm_scans-95-103.tab

description: :unav
DOI: 10.25625/iu7w3j/ubgksj
title: 2022-07-28_132128_He_ps606mbar_pb11mbar_d2mm_P18W_sw100μm_gt500_wl639nm_25x300.tab

description: :unav
DOI: 10.25625/iu7w3j/kqop89
title: 2022-10-07_125701_bias_1000x18x100ms_raw.tab

description: :unav
DOI: 10.25625/iu7w3j/dqjqlj
title: He-600-2c-lr_h.csv

description: :unav
DOI: 10.25625/iu7w3j/nzxn8w
title: He-600-2b-lr_l.csv

description: :unav
DOI: 10.25625/iu7w3j/atatgm
title: 2022-10-05_222907_H2O_He_ps600mbar_pb11mbar_d2mm_P18W_sw100μm_gt1350_wl650nm_25x900s_raw.tab

description: :unav
DOI: 10.25625/iu7w3j/c38uuw
title: 2022-09-14_122348_H2O_He_ps600mbar_pb10mbar_d2mm_P18W_sw100μm_gt500_wl639nm_24x300s.tab

description: :unav
DOI: 10.25625/iu7w3j/vwh90z
title: Ne-300-2.5-hr.csv

description: :unav
DOI: 10.25625/iu7w3j/biofou
title: 2022-10-06_172230_H2O_He__ps600mbar_pb11mbar_d2mm_P18W_sw100μm_gt1350_wl650nm_55x900s_raw.tab

description: :unav
DOI: 10.25625/iu7w3j/so48ac
title: Ne-300-2.5-hr-bg.csv

description: :unav
DOI: 10.25625/iu7w3j/ezudkv
title: 2022-10-06_102639_bias_1000x18x100ms_raw.tab

description: :unav
DOI: 10.25625/iu7w3j/csw56l
title: 2022-09-13_161446_H2O_He_ps600mbar_pb10mbar_d2mm_P18W_sw100μm_gt500_wl639nm_10x300s.tab

description: :unav
DOI: 10.25625/iu7w3j/41akt8
title: 2022-09-12_135738_H2O_He_ps595mbar_pb11mbar_d2mm_P18W_sw100μm_gt500_wl639nm_20x300s.tab

description: :unav
DOI: 10.25625/iu7w3j/z9ujzs
title: He-600-2b-lr_m.csv

description: :unav
DOI: 10.25625/iu7w3j/cu9uct
title: 2022-07-13_022026_H2O_90Ne_10Ar_ps410mbar_pb5mbar_d2.5mm_P18W_sw100μm_gt1350_wl650nm_90x300.tab

description: :unav
DOI: 10.25625/iu7w3j/il4leo
title: He-600-2-lr-bg.csv

description: :unav
DOI: 10.25625/iu7w3j/8ohfxw
title: 2022-09-13_122605_H2O_He_ps600mbar_pb10mbar_d2mm_P18W_sw100μm_gt500_wl639nm_19x300s.tab

description: :unav
DOI: 10.25625/iu7w3j/cdumsj
title: 2022-07-20_022205_H2O_Ne_ps200mbar_pb3mbar_d2mm_P18W_sw100μm_gt1350_wl650nm_51x300s.tab

description: :unav
DOI: 10.25625/iu7w3j/jumdbw
title: He-600-2b-lr_h.csv

description: :unav
DOI: 10.25625/iu7w3j
title: Linear Raman Spectra of Small Water Clusters in Jet-Expansions

description: This dataset contains Raman jet-spectra of small water clusters prepared in rare gas expansions, targeting the region of the OH stretching fundamental vibrations and OH bending overtone (about 3100 to 3700 cm-1).
DOI: 10.5281/zenodo.10810695
title: Causes and multiyear predictability of the rapid acceleration of U.S. Southeast Sea level rise after 2010

description: Authors: Liping Zhang1*,2, Thomas L. Delworth1, Xiaosong Yang1, Fanrogn Zeng1, Qinxue Gu1,3 and Shouwei Li1,3

                        1NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey, USA

           2University Corporation for Atmospheric Research, Boulder, Colorado, USA

            3The Program in Atmospheric and oceanic Sciences, Princeton University, Princeton, NJ, USA

 

 

Replication of paper entitled "Causes and multiyear predictability of the rapid acceleration of U.S. Southeast Sea level rise after 2010".

This file contains datasets used to replicate figures in a paper published in npj Climate and Atmospheric Science. These datasets are in .mat format, which can be easily read by Matlab software using load command.
DOI: 10.5281/zenodo.10810696
title: Causes and multiyear predictability of the rapid acceleration of U.S. Southeast Sea level rise after 2010

description: Authors: Liping Zhang1*,2, Thomas L. Delworth1, Xiaosong Yang1, Fanrogn Zeng1, Qinxue Gu1,3 and Shouwei Li1,3

                        1NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey, USA

           2University Corporation for Atmospheric Research, Boulder, Colorado, USA

            3The Program in Atmospheric and oceanic Sciences, Princeton University, Princeton, NJ, USA

 

 

Replication of paper entitled "Causes and multiyear predictability of the rapid acceleration of U.S. Southeast Sea level rise after 2010".

This file contains datasets used to replicate figures in a paper published in npj Climate and Atmospheric Science. These datasets are in .mat format, which can be easily read by Matlab software using load command.
DOI: 10.34740/kaggle/dsv/7827906
title: Daily Public Opinion on Israel-Palestine War
DOI: 10.34740/kaggle/dsv/7827915
title: Public Opinion on Climate Change (Updated Daily)
DOI: 10.5281/zenodo.10810665
title: Open dataset of 20 interviews with senior service designers and managers for the Empathy Business research project

description: The aim of the Empathy Business research project (2023-2024) led by the University of Lapland focused on how to digitalize services and business prototyping through creativity. The research named challenges as well as design methods for developing digital tools for meeting the future needs of service design and business development.In total, 20 interviews among senior service designers and managers located in Europe, Latin America and Asia were conducted during spring 2023. The interviews provide perspectives related to the future of service design as a practise, the skills required and further issues of relevance for professionals in the field. Based on affinity diagramming eight main clusters were named: Sustainability, Business compatibility, New tools, Designer’s skills, Art-based methods, People in the centre, Online workshops, and Physical workshops.This data set includes an anonymized list of the interviewees, affinity diagram post-it notes of the interviews, short descriptions of the main clusters and an internet link to the online affinity diagram on Miro board.The materials provided initial insights for developing Proof-of-Concepts for digitized interfaces, such as suitable plugins, 3D-based photorealistic solutions, or an application with the potential to be used in service design and service prototyping contexts as well as in other development processes within and across organizations.
DOI: 10.5281/zenodo.10810666
title: Open dataset of 20 interviews with senior service designers and managers for the Empathy Business research project

description: The aim of the Empathy Business research project (2023-2024) led by the University of Lapland focused on how to digitalize services and business prototyping through creativity. The research named challenges as well as design methods for developing digital tools for meeting the future needs of service design and business development.In total, 20 interviews among senior service designers and managers located in Europe, Latin America and Asia were conducted during spring 2023. The interviews provide perspectives related to the future of service design as a practise, the skills required and further issues of relevance for professionals in the field. Based on affinity diagramming eight main clusters were named: Sustainability, Business compatibility, New tools, Designer’s skills, Art-based methods, People in the centre, Online workshops, and Physical workshops.This data set includes an anonymized list of the interviewees, affinity diagram post-it notes of the interviews, short descriptions of the main clusters and an internet link to the online affinity diagram on Miro board.The materials provided initial insights for developing Proof-of-Concepts for digitized interfaces, such as suitable plugins, 3D-based photorealistic solutions, or an application with the potential to be used in service design and service prototyping contexts as well as in other development processes within and across organizations.
DOI: 10.6084/m9.figshare.25381441
title: PECA2 Prior

description: PECA2 prior files
DOI: 10.6084/m9.figshare.25381441.v1
title: PECA2 Prior

description: PECA2 prior files
DOI: 10.5281/zenodo.10810713
title: Data associated with "Deforestation-driven increases in shallow clouds are greatest in drier, low-aerosol regions of Southeast Asia"

description: This .zip file contains the semi-processed data required to reproduce the figures in "Deforestation-driven increases in shallow clouds are greatest in drier, low-aerosol regions in Southeast Asia" (Leung, Grant, and van den Heever 2024; pre-print doi: 10.5194/egusphere-2023-1722). 

Inside the compressed folder are:



base_forest_data.pq: parquet file (readable in Python, see associated code) containing the processed UMD Global Forest Cover and EC-JRC Global Surface Water data scaled to same ~1km resolution as MODIS grid

matched_points/: directory containing parquet files of each deforestation event with cloud properties before and after deforestation, as well as mean cloud properties before/after deforestation for corresponding control pixels (see Supporting Information of above manuscript for full descriptions)

deforest_effect/bootstrapped/: directory containing .csv files for points in Figures 2-4 of above manuscript
DOI: 10.6084/m9.figshare.25395565
title: Immersion calorimetry of nanoZIF-8 particles

description: Immersion calorimetry measurements of ZIF-8 nanoparticles is different solvents.
DOI: 10.6084/m9.figshare.25395565.v1
title: Immersion calorimetry of nanoZIF-8 particles

description: Immersion calorimetry measurements of ZIF-8 nanoparticles is different solvents.
DOI: 10.5281/zenodo.10809795
title: IODP Expedition 378 Alkalinity and pH

description: Alkalinity was determined by Gran titration with an autotitrator (Metrohm 794 basic Titrino) using 0.1 M HCl at 20 degrees C. Report includes alkalinity, correction factor (if applicable), and pH.
DOI: 10.5281/zenodo.10809796
title: IODP Expedition 378 Alkalinity and pH

description: Alkalinity was determined by Gran titration with an autotitrator (Metrohm 794 basic Titrino) using 0.1 M HCl at 20 degrees C. Report includes alkalinity, correction factor (if applicable), and pH.
DOI: 10.6084/m9.figshare.25395418
title: <i>Apis cerana </i>Simulation Data

description: Simulation data that models an invasive social insect population with a sex locus moving along a single spatial dimension. Each colony is treated as an individual, listing (for each generation) a colonies spatial position, queens sex alleles, queens mates sex alleles and resultant fitness. This is parameterized and compared to simulations in which the modeled alleles have now impact on fitness.
DOI: 10.6084/m9.figshare.25395418.v1
title: <i>Apis cerana </i>Simulation Data

description: Simulation data that models an invasive social insect population with a sex locus moving along a single spatial dimension. Each colony is treated as an individual, listing (for each generation) a colonies spatial position, queens sex alleles, queens mates sex alleles and resultant fitness. This is parameterized and compared to simulations in which the modeled alleles have now impact on fitness.