cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.14606105
title: Evaluating the performance of CE-QUAL-W2 version 4.5 sediment diagenesis model  (Manuscript related material: input data and source code)

description: This archive includes materials related to the manuscript Evaluating the Performance of the CE-QUAL-W2 Version 4.5 Sediment Diagenesis Model, such as the model code, pre-/post-processing scripts, and model input/output data. The contents are organized as follows:

i)       Model forcing datasets and scripts

Loadest: input files: est.inp; Calib.inp; control.inp; header.inp

                   executable files: loadest.exe; 

                    Manual: Load Estimator (LOADEST) Manual.pdf

Support_Vector_Regressor: input files in folder input files

                   script: Torrão_SVR_hyper_run_py39.py (replace the input file name for each constituent in the script lines: data = pd.read_excel('Input_chla.xlsx', index_col=0, header=0) and dataR = pd.read_excel('unseen_chla.xlsx', index_col=0, header=0)        

XGboost: input files in folder input files

                   script: Torrão_XG_boost_hyper_run_py39.py (replace the input file name for each constituent in the script lines: data = pd.read_excel('Input_chla.xlsx', index_col=0, header=0) and dataR = pd.read_excel('unseen_chla.xlsx', index_col=0, header=0)

 

ii)    CE-QUAL-W2 Model documentation

Model user manual and sedimentt diagenesis model documentation

 

iii)  CE-QUAL-W2 input files and .exe files for each model run:

W2_v4.5_0_order model: runs SOD: 0.5, 1.0, 1.5, 2.0, 2.5 and 3.0 g O2/m2/day

W2_v4.5_sediment_diagenesis model: runs 1 to 6

W2_v4.5 PON sensitivity analysis: runs 7 to 15

W2_v4.5 POP  sensitivity analysis: runs 16 to 24
DOI: 10.7483/opendata.delphi.sm56.gwle
title: DELPHI simulation data xs_hzha03pyth6156hinvmu_e199.5_m45_r99_1l_e1
DOI: 10.7483/opendata.delphi.j6v5.xtor
title: DELPHI simulation data xs_hsee_e202_m100.0_k99_1l_a1
DOI: 10.7483/opendata.delphi.uqhi.2kc1
title: DELPHI simulation data xs_hzha03pyth6156hinvmu_e199.5_m70_r99_1l_e1
DOI: 10.7483/opendata.delphi.sqjc.yja8
title: DELPHI simulation data xs_gpym_e200_l99_1l_a1
DOI: 10.7483/opendata.delphi.v5xk.y6ut
title: DELPHI simulation data xs_hzha03pyth6156ha4t_e199.5_m15_m150_c99_1l_e1
DOI: 10.7483/opendata.delphi.nmrf.rlku
title: DELPHI simulation data xs_hsee_e202_m110.0_k99_1l_a1
DOI: 10.7483/opendata.delphi.ujrl.188p
title: DELPHI simulation data xs_hzha03pyth6156ha4t_e199.5_m170_m15_c99_1l_e1
DOI: 10.7483/opendata.delphi.t8ke.xrkh
title: DELPHI simulation data xs_hzha03pyth6156hinvmu_e199.5_m50_r99_1l_e1
DOI: 10.7483/opendata.delphi.ot8l.zd7g
title: DELPHI simulation data xs_hzha03pyth6156hinvqq_e191.6_m105_r99_1l_e1
DOI: 10.7483/opendata.delphi.p1s2.ekrs
title: DELPHI simulation data xs_hsee_e202_m50.0_k99_1l_a1
DOI: 10.7483/opendata.delphi.syr4.mb4n
title: DELPHI simulation data xs_hzha03pyth6156hinvmu_e201.6_m40_r99_1l_e1
DOI: 10.7483/opendata.delphi.e41f.wvsp
title: DELPHI simulation data xs_gpym_e204_s99_1l_a1
DOI: 10.7483/opendata.delphi.5i8w.rzqt
title: DELPHI simulation data xs_ha20_e191.6_m95_c99_1l_a1
DOI: 10.6084/m9.figshare.28144010
title: Advancing diagnostic consistency: Statistical methods and interrater reliability in neuropsychological assessments with the MNB

description: In the field of neuropsychology, the accuracy of neuropsychological data interpretation has significant implications for both research and clinical practice. The process of test interpretation is fraught with challenges, and a lack of consensus among neuropsychologists can lead to discrepancies in assessment outcomes. Smith et al. (2020) identified this concerning lack of consensus in test score interpretation, raising questions about the standardization of interpretive practices within the field. Similarly, Tuokko and Gabriel (2006) reported moderate interrater agreement in identifying cognitive impairments, with a 77.7% consensus rate, using both history and all available information. The current study investigated the extent to which independent neuropsychologists could reach consistent diagnostic conclusions using only numeric data and the statistical methods available in the Meyers Neuropsychological Battery (MNB), without access to clinical history or contextual information. The results demonstrated a high degree of accuracy, with an overall agreement rate of 85.5% to 92.2% among the six raters. This finding underscores the potential of standardized statistical approaches to enhance the objectivity and consistency of neuropsychological assessments. The study advances neuropsychological assessment methodology by showing that statistical pattern-matching techniques can yield reliable and uniform diagnostic outcomes, even in the absence of additional clinical data.
DOI: 10.6084/m9.figshare.28144010.v1
title: Advancing diagnostic consistency: Statistical methods and interrater reliability in neuropsychological assessments with the MNB

description: In the field of neuropsychology, the accuracy of neuropsychological data interpretation has significant implications for both research and clinical practice. The process of test interpretation is fraught with challenges, and a lack of consensus among neuropsychologists can lead to discrepancies in assessment outcomes. Smith et al. (2020) identified this concerning lack of consensus in test score interpretation, raising questions about the standardization of interpretive practices within the field. Similarly, Tuokko and Gabriel (2006) reported moderate interrater agreement in identifying cognitive impairments, with a 77.7% consensus rate, using both history and all available information. The current study investigated the extent to which independent neuropsychologists could reach consistent diagnostic conclusions using only numeric data and the statistical methods available in the Meyers Neuropsychological Battery (MNB), without access to clinical history or contextual information. The results demonstrated a high degree of accuracy, with an overall agreement rate of 85.5% to 92.2% among the six raters. This finding underscores the potential of standardized statistical approaches to enhance the objectivity and consistency of neuropsychological assessments. The study advances neuropsychological assessment methodology by showing that statistical pattern-matching techniques can yield reliable and uniform diagnostic outcomes, even in the absence of additional clinical data.
DOI: 10.7483/opendata.delphi.1j54.yzji
title: DELPHI simulation data xs_ha02_e191.6_m85_c99_1l_a1
DOI: 10.7483/opendata.delphi.kbdk.o1uy
title: DELPHI simulation data xs_ha02_e200_m24_c99_1l_a1
DOI: 10.7483/opendata.delphi.30v0.njz9
title: DELPHI simulation data xs_ha02ttbb_e200_m50_c99_1l_a1
DOI: 10.15468/dl.4t4twz
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pagrus auratus (Forster, 1801)",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0058677-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7483/opendata.delphi.5a6u.kbmv
title: DELPHI simulation data xs_hzha03pyth6156ha4t_e199.5_m15_m25_c99_1l_e1
DOI: 10.7483/opendata.delphi.avbr.pfeg
title: DELPHI simulation data xs_ha02ttbb_e191.6_m70_c99_1l_a1
DOI: 10.7483/opendata.delphi.4i13.muvk
title: DELPHI simulation data xs_hsee_e202_m55.0_k99_1l_a1
DOI: 10.7483/opendata.delphi.l175.0l3d
title: DELPHI simulation data xs_ha02_e202_m30_c99_1l_a1
DOI: 10.7483/opendata.delphi.3gl9.m6n9
title: DELPHI simulation data xs_hzha03pyth6156hinvmu_e199.5_m75_r99_1l_e1
DOI: 10.25378/janelia.26511841
title: Manual nuclei segmentations in near-isotropic, reconstructed volume electron microscopy (FIB-SEM) of A431 epithelial cells (aic_desmosome-3)

description: <b>Annotation description</b>: Dense segmentations of nuclei in aic_desmosome-3 using Amira-Avizo (Thermo Fisher Scientific) and "Using Amira to manually segment organelles in vEM for machine learning V.3" protocol.<b>Annotation protocol</b>: Using Amira to manually segment organelles in vEM for machine learning V.3 (http://dx.doi.org/10.17504/protocols.io.bp2l61rb5vqe/v3)<b>Software</b>: Amira 3D 2021.1, Classic Segmentation Workroom<b>Classes annotated</b>: Nuclei<b>Source (EM) dataset ID</b>: aic_desmosome-3<b>Source (EM) voxel size (nm)</b>: 4 x 4 x 4 (X, Y, Z)<b>Source (EM) data dimensions (µm)</b>: 14.9 x 12.9 x 48.4 (X, Y, Z)<b>Source (EM) DOI</b>: https://doi.org/10.25378/janelia.22675078<b>Dataset URL</b>: s3://janelia-cosem-datasets/aic_desmosome-3/aic_desmosome-3.zarr/recon-1/labels/inference/segmentations/nuc/<b>Visualization website</b>: https://openorganelle.janelia.org/datasets/aic_desmosome-3
DOI: 10.25378/janelia.26511841.v1
title: Manual nuclei segmentations in near-isotropic, reconstructed volume electron microscopy (FIB-SEM) of A431 epithelial cells (aic_desmosome-3)

description: <b>Annotation description</b>: Dense segmentations of nuclei in aic_desmosome-3 using Amira-Avizo (Thermo Fisher Scientific) and "Using Amira to manually segment organelles in vEM for machine learning V.3" protocol.<b>Annotation protocol</b>: Using Amira to manually segment organelles in vEM for machine learning V.3 (http://dx.doi.org/10.17504/protocols.io.bp2l61rb5vqe/v3)<b>Software</b>: Amira 3D 2021.1, Classic Segmentation Workroom<b>Classes annotated</b>: Nuclei<b>Source (EM) dataset ID</b>: aic_desmosome-3<b>Source (EM) voxel size (nm)</b>: 4 x 4 x 4 (X, Y, Z)<b>Source (EM) data dimensions (µm)</b>: 14.9 x 12.9 x 48.4 (X, Y, Z)<b>Source (EM) DOI</b>: https://doi.org/10.25378/janelia.22675078<b>Dataset URL</b>: s3://janelia-cosem-datasets/aic_desmosome-3/aic_desmosome-3.zarr/recon-1/labels/inference/segmentations/nuc/<b>Visualization website</b>: https://openorganelle.janelia.org/datasets/aic_desmosome-3
DOI: 10.7483/opendata.delphi.5rnd.i7k8
title: DELPHI simulation data xs_hzha03pyth6156ha4t_e199.5_m15_m4_c99_1l_e1
DOI: 10.7483/opendata.delphi.rw2v.jnfw
title: DELPHI simulation data xs_hsee_e202_m60.0_k99_1l_a1
DOI: 10.7483/opendata.delphi.3zxw.90x2
title: DELPHI simulation data xs_gpym6203jpsi_e200_co_rd22c_c99_1l_e1
DOI: 10.7483/opendata.delphi.dal5.hh0p
title: DELPHI simulation data xs_hzha03pyth6156hinvqq_e191.6_m70_r99_1l_e1
DOI: 10.7483/opendata.delphi.dw1l.vx17
title: DELPHI simulation data xs_ha02ttbb_e191.6_m60_c99_1l_a1
DOI: 10.7483/opendata.delphi.2ogr.xwtp
title: DELPHI simulation data xs_hzha03pyth6156ha4t_e199.5_m170_m4_snt99_1l_e1
DOI: 10.6084/m9.figshare.28144016
title: Simultaneous Estimation of Multiple Treatment Effects from Observational Studies

description: Unmeasured confounding presents a significant challenge in causal inference from observational studies. Classical approaches often rely on collecting proxy variables, such as instrumental variables. However, in applications where the effects of multiple treatments are of simultaneous interest, finding a sufficient number of proxy variables for consistent estimation of treatment effects can be challenging. Various methods in the literature exploit the structure of multiple treatments to address unmeasured confounding. In this paper, we introduce a novel approach to causal inference with multiple treatments, assuming sparsity in the causal effects. Our procedure autonomously selects treatments with non-zero causal effects, thereby providing a sparse causal estimation. Comprehensive evaluations using both simulated and Genome-Wide Association Study (GWAS) datasets demonstrate the effectiveness and robustness of our method compared to alternative approaches.
DOI: 10.6084/m9.figshare.28144016.v1
title: Simultaneous Estimation of Multiple Treatment Effects from Observational Studies

description: Unmeasured confounding presents a significant challenge in causal inference from observational studies. Classical approaches often rely on collecting proxy variables, such as instrumental variables. However, in applications where the effects of multiple treatments are of simultaneous interest, finding a sufficient number of proxy variables for consistent estimation of treatment effects can be challenging. Various methods in the literature exploit the structure of multiple treatments to address unmeasured confounding. In this paper, we introduce a novel approach to causal inference with multiple treatments, assuming sparsity in the causal effects. Our procedure autonomously selects treatments with non-zero causal effects, thereby providing a sparse causal estimation. Comprehensive evaluations using both simulated and Genome-Wide Association Study (GWAS) datasets demonstrate the effectiveness and robustness of our method compared to alternative approaches.
DOI: 10.25378/janelia.26513071
title: Manual nuclei segmentations in near-isotropic, reconstructed volume electron microscopy (FIB-SEM) of HeLa cell with RUSH cargo (jrc_hela-22)

description: <b>Annotation description</b>: Dense segmentations of nuclei in jrc_hela-22 using Amira-Avizo (Thermo Fisher Scientific) and "Using Amira to manually segment organelles in vEM for machine learning V.3" protocol.<b>Annotation protocol</b>: Using Amira to manually segment organelles in vEM for machine learning V.3 (http://dx.doi.org/10.17504/protocols.io.bp2l61rb5vqe/v3)<b>Software</b>: Amira 3D 2021.1, Classic Segmentation Workroom<b>Classes annotated</b>: Nuclei<b>Source (EM) dataset ID</b>: jrc_hela-22<b>Source (EM) voxel size (nm)</b>: 8 x 8 x 8 (X, Y, Z)<b>Source (EM) data dimensions (µm)</b>: 3.36 x 8.46 x 33 (X, Y, Z)<b>Source (EM) DOI</b>: https://doi.org/10.25378/janelia.13469901<b>Dataset URL</b>: s3://janelia-cosem-datasets/jrc_hela-22/jrc_hela-22.zarr/recon-1/labels/inference/segmentations/nuc/<b>Visualization website</b>: https://openorganelle.janelia.org/datasets/jrc_hela-22
DOI: 10.25378/janelia.26513071.v1
title: Manual nuclei segmentations in near-isotropic, reconstructed volume electron microscopy (FIB-SEM) of HeLa cell with RUSH cargo (jrc_hela-22)

description: <b>Annotation description</b>: Dense segmentations of nuclei in jrc_hela-22 using Amira-Avizo (Thermo Fisher Scientific) and "Using Amira to manually segment organelles in vEM for machine learning V.3" protocol.<b>Annotation protocol</b>: Using Amira to manually segment organelles in vEM for machine learning V.3 (http://dx.doi.org/10.17504/protocols.io.bp2l61rb5vqe/v3)<b>Software</b>: Amira 3D 2021.1, Classic Segmentation Workroom<b>Classes annotated</b>: Nuclei<b>Source (EM) dataset ID</b>: jrc_hela-22<b>Source (EM) voxel size (nm)</b>: 8 x 8 x 8 (X, Y, Z)<b>Source (EM) data dimensions (µm)</b>: 3.36 x 8.46 x 33 (X, Y, Z)<b>Source (EM) DOI</b>: https://doi.org/10.25378/janelia.13469901<b>Dataset URL</b>: s3://janelia-cosem-datasets/jrc_hela-22/jrc_hela-22.zarr/recon-1/labels/inference/segmentations/nuc/<b>Visualization website</b>: https://openorganelle.janelia.org/datasets/jrc_hela-22
DOI: 10.7483/opendata.delphi.79ax.3hup
title: DELPHI simulation data xs_hsee_e202_m65.0_k99_1l_a1
DOI: 10.7483/opendata.delphi.cjrz.oku1
title: DELPHI simulation data xs_hzha03pyth6156hinvqq_e191.6_m100_r99_1l_e1
DOI: 10.7483/opendata.delphi.l01c.r9ht
title: DELPHI simulation data xs_ha02ttbb_e191.6_m80_c99_1l_a1
DOI: 10.7483/opendata.delphi.of30.x756
title: DELPHI simulation data xs_ha02_e191.6_m70_c99_1l_a1
DOI: 10.7483/opendata.delphi.acu1.hkjt
title: DELPHI simulation data xs_hzha03pyth6156ha4t_e199.5_m35_m15_c99_1l_e1
DOI: 10.7483/opendata.delphi.jhz2.nk93
title: DELPHI simulation data xs_ha02ttbb_e200_m40_c99_1l_a1
DOI: 10.7483/opendata.delphi.udkj.0ap6
title: DELPHI simulation data xs_ha20_e191.6_m50_c99_1l_a1
DOI: 10.7483/opendata.delphi.3v5s.l1gu
title: DELPHI simulation data xs_hsee_e202_m75.0_k99_1l_a1
DOI: 10.7483/opendata.delphi.3j7y.jky8
title: DELPHI simulation data xs_ha02ttbb_e196_m40_c99_1l_a1
DOI: 10.7483/opendata.delphi.rn0q.7hyh
title: DELPHI simulation data xs_hzha03pyth6156ha4t_e199.5_m35_m160_c99_1l_e1
DOI: 10.7483/opendata.delphi.qmg6.3v5s
title: DELPHI simulation data xs_hsee_e202_m70.0_k99_1l_a1
DOI: 10.7483/opendata.delphi.1uh5.0ydy
title: DELPHI simulation data xs_hzha03pyth6156hinvqq_e191.6_m45_r99_1l_e1
DOI: 10.7483/opendata.delphi.r90s.5qhz
title: DELPHI simulation data xs_ha02_e202_m24_c99_1l_a1
DOI: 10.7483/opendata.delphi.jsy7.etx7
title: DELPHI simulation data xs_gpym6203jpsi_e200_co_dr22_c99_1l_e1
DOI: 10.7483/opendata.delphi.vzgj.5eyf
title: DELPHI simulation data xs_hzha03pyth6156ha4t_e199.5_m35_m110_c99_1l_e1
DOI: 10.7483/opendata.delphi.uetz.6por
title: DELPHI simulation data xs_hsee_e202_m80.0_k99_1l_a1
DOI: 10.7483/opendata.delphi.8rjn.q4r9
title: DELPHI simulation data xs_hzha03pyth6156hinvmu_e201.6_m95_r99_1l_e1
DOI: 10.15468/dl.thp6xt
title: Occurrence Download

description: A dataset containing 23 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Octopus sinensis d'Orbigny, 1834",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 23 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0058678-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7483/opendata.delphi.6kch.a9rx
title: DELPHI simulation data xs_ha02_e191.6_m80_c99_1l_a1
DOI: 10.7483/opendata.delphi.bx5o.yui9
title: DELPHI simulation data xs_hzha03pyth6156ha4t_e199.5_m35_m4_c99_1l_e1
DOI: 10.7483/opendata.delphi.3ujx.io5n
title: DELPHI simulation data xs_ha20_e191.6_m90_c99_1l_a1
DOI: 10.7483/opendata.delphi.4fme.2xos
title: DELPHI simulation data xs_gpym6156jpsi_e200_c99_1l_a1
DOI: 10.7483/opendata.delphi.37k3.95j8
title: DELPHI simulation data xs_hsee_e202_m85.0_k99_1l_a1
DOI: 10.7483/opendata.delphi.hkia.7ubd
title: DELPHI simulation data xs_ha02ttbb_e200_m30_c99_1l_a1
DOI: 10.7483/opendata.delphi.e00z.nm69
title: DELPHI simulation data xs_hzha03pyth6156ha4t_e199.5_m35_m35_c99_1l_e1
DOI: 10.7483/opendata.delphi.j6mj.x94p
title: DELPHI simulation data xs_hsee_e202_m90.0_k99_1l_a1
DOI: 10.7483/opendata.delphi.wew2.3ty1
title: DELPHI simulation data xs_ha02ttbb_e191.6_m30_c99_1l_a1
DOI: 10.7483/opendata.delphi.jlwp.x7n6
title: DELPHI simulation data xs_hzha03pyth6156hinvmu_e201.6_m97.5_r99_1l_e1
DOI: 10.7483/opendata.delphi.09wu.luap
title: DELPHI simulation data xs_hzha03pyth6156ha4t_e199.5_m4_m110_c99_1l_e1
DOI: 10.7483/opendata.delphi.qjcz.ijw5
title: DELPHI simulation data xs_hsee_e202_m95.0_k99_1l_a1
DOI: 10.7483/opendata.delphi.3icj.oxxz
title: DELPHI simulation data xs_gpym6156c4_e200_l99_1l_a1
DOI: 10.7483/opendata.delphi.qazv.0c8a
title: DELPHI simulation data xs_ha02_e191.6_m40_c99_1l_a1
DOI: 10.7483/opendata.delphi.z28i.oqwt
title: DELPHI simulation data xs_hzha03pyth6156ha4t_e199.5_m4_m25_c99_1l_e1
DOI: 10.7483/opendata.delphi.0mx5.ung4
title: DELPHI simulation data xs_hzha03pyth6156hinvmu_e201.6_m85_r99_1l_e1
DOI: 10.7483/opendata.delphi.om3z.vmj4
title: DELPHI simulation data xs_hsee_e205_m100.0_k99_1l_a1
DOI: 10.7483/opendata.delphi.9prw.symh
title: DELPHI simulation data xs_ha02_e200_m40_c99_1l_a1
DOI: 10.7483/opendata.delphi.u09i.pbue
title: DELPHI simulation data xs_hzha03pyth6156hinvmu_e199.5_m55_r99_1l_e1
DOI: 10.7483/opendata.delphi.33sm.kpri
title: DELPHI simulation data xs_ha02ttbb_e200_m24_c99_1l_a1
DOI: 10.7483/opendata.delphi.uu3j.up06
title: DELPHI simulation data xs_hzha03pyth6156ha4t_e199.5_m4_m150_c99_1l_e1
DOI: 10.7483/opendata.delphi.xaxq.2h51
title: DELPHI simulation data xs_ha02ttbb_e191.6_m50_c99_1l_a1
DOI: 10.15468/dl.cmhj4w
title: Occurrence Download

description: A dataset containing 39 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Octopus vulgaris Cuvier, 1797",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 39 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0058679-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7483/opendata.delphi.z5pu.0oe7
title: DELPHI simulation data xs_hsee_e205_m105.0_k99_1l_a1
DOI: 10.7483/opendata.delphi.02uv.4rpq
title: DELPHI simulation data xs_hzha03pyth6156ha4t_e199.5_m4_m190_c99_1l_e1
DOI: 10.15468/dl.rksxu4
title: Occurrence Download

description: A dataset containing 79621318 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "DatasetKey is EOD – eBird Observation Dataset",
 "TaxonKey is Animalia",
 "Year 2014-2015"
 ]
}

 The dataset includes 79621318 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0058675-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7483/opendata.delphi.48lr.94ta
title: DELPHI simulation data xs_hsee_e205_m110.0_k99_1l_a1
DOI: 10.7483/opendata.delphi.9tf9.69fo
title: DELPHI simulation data xs_hzha03pyth6156hinvmu_e199.5_m120.0_r99_1l_e1
DOI: 10.7483/opendata.delphi.tf1h.gxbd
title: DELPHI simulation data xs_ha02_e191.6_m30_c99_1l_a1
DOI: 10.7483/opendata.delphi.akrz.72eo
title: DELPHI simulation data xs_hzha03pyth6156ha4t_e199.5_m4_m11_c99_1l_e1
DOI: 10.7483/opendata.delphi.7pco.l5zk
title: DELPHI simulation data xs_hzha03pyth6156hinvmu_e199.5_m90_r99_1l_e1
DOI: 10.7483/opendata.delphi.cn4l.94fd
title: DELPHI simulation data xs_hsee_e205_m50.0_k99_1l_a1
DOI: 10.7483/opendata.delphi.awq2.6l91
title: DELPHI simulation data xs_ha20_e191.6_m18_c99_1l_a1
DOI: 10.7483/opendata.delphi.bdtz.x82t
title: DELPHI simulation data xs_hzha03pyth6156hinvmu_e201.6_m92.5_r99_1l_e1
DOI: 10.7483/opendata.delphi.nz9y.8tv9
title: DELPHI simulation data xs_ha02ttbb_e191.6_m12_c99_1l_a1
DOI: 10.7483/opendata.delphi.70ht.adnl
title: DELPHI simulation data xs_hzha03pyth6156ha4t_e199.5_m50_m110_c99_1l_e1
DOI: 10.7483/opendata.delphi.zfvl.x4ev
title: DELPHI simulation data xs_gpym_e206.7_s99_1l_a1
DOI: 10.7483/opendata.delphi.goa4.gdvn
title: DELPHI simulation data xs_hsee_e205_m55.0_k99_1l_a1
DOI: 10.7483/opendata.delphi.k6zg.xhph
title: DELPHI simulation data xs_ha02ttbb_e200_m18_c99_1l_a1
DOI: 10.7483/opendata.delphi.bf5o.ihvd
title: DELPHI simulation data xs_ha02_e202_m18_c99_1l_a1
DOI: 10.7483/opendata.delphi.c0al.7fow
title: DELPHI simulation data xs_ha02ttbb_e196_m12_c99_1l_a1
DOI: 10.7483/opendata.delphi.16am.ntsa
title: DELPHI simulation data xs_hzha03pyth6156ha4t_e199.5_m4_m4_c99_1l_e1
DOI: 10.7483/opendata.delphi.hcfi.jbuo
title: DELPHI simulation data xs_hzha03pyth6156hinvmu_e199.5_m40_r99_1l_e1
DOI: 10.7483/opendata.delphi.s0gc.zkw9
title: DELPHI simulation data xs_hsee_e205_m60.0_k99_1l_a1
DOI: 10.7483/opendata.delphi.ydz0.xa45
title: DELPHI simulation data xs_ha02_e191.6_m50_c99_1l_a1