cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10700549
title: Sulfate affinity controls phosphate sorption and the proto-transformation of schwertmannite

description: Schwertmannite is a metastable sulfate-rich ferric iron Fe(III) (oxyhydr)oxide and a common mineral in acid mine drainage sites and acid sulfate soils. Schwertmannite is also used as a sorbent in various industrial applications, including phosphate removal for water treatment and environmental remediation. Phosphate sorption to schwertmannite, however, is complex and likely involves ligand exchange for inner- and outer-spherically coordinated sulfate groups, both on the surface and in the tunnel structure of the mineral. Here, we investigated phosphate sorption, concomitant sulfate release and their impact on the structure of schwertmannite as a function of pH and phosphate concentration. Kinetic and equilibrium batch experiments with synthetic schwertmannite were carried out at pH 3, 6, and 8, and the solid-phase was analyzed using scanning electron microscopy, Mössbauer spectroscopy, infra-red spectroscopy and X-ray diffraction spectroscopy. We found a strong correlation between phosphate sorption and sulfate release, with both following a two-step sorption model. Kinetics of phosphate sorption and sulfate release were faster at more alkaline pH. Maximum phosphate sorption was found at pH 6 (1.7 mmol PO43- g-1) , which decreased to 1.5 and 1.2 mmol PO43- g-1 at pH 3 and 8, respectively. Fourier transform infrared spectroscopy revealed a shift from inner- to outer-spherical coordination of sulfate with increasing pH. 57Mössbauer analyses of schwertmannite indicated a proto-transformation of schwertmannite at neutral to alkaline pH, characterized by a rise in a partially ordered sextet area. This change was interpreted as an increase in crystallinity resulting from the transition from Fe-SO4 to Fe-O domains. This pH-induced proto-transformation was inhibited in the presence of phosphate. We concluded that the phosphate sorption rate and maximum as well as the proto-transformation of schwertmannite were strongly affected by the mineral’s affinity for sulfate. Sorption to schwertmannite should primarily be regarded as a competitive exchange reaction between the sorbing oxyanion and the bound sulfate. This resulted in the highest phosphate sorption at circumneutral pH, a stark contrast to non-sulfate-containing Fe(III) (oxyhydr)oxides, where phosphate sorption is highest at acidic pH. Our results are important for a fundamental understanding of the sorption properties of schwertmannite in phosphate-rich environments as they point towards the central role of sulfate coordination for phosphate immobilization.
DOI: 10.5281/zenodo.10702202
title: Sulfate affinity controls phosphate sorption and the proto-transformation of schwertmannite

description: Schwertmannite is a metastable sulfate-rich ferric iron Fe(III) (oxyhydr)oxide and a common mineral in acid mine drainage sites and acid sulfate soils. Schwertmannite is also used as a sorbent in various industrial applications, including phosphate removal for water treatment and environmental remediation. Phosphate sorption to schwertmannite, however, is complex and likely involves ligand exchange for inner- and outer-spherically coordinated sulfate groups, both on the surface and in the tunnel structure of the mineral. Here, we investigated phosphate sorption, concomitant sulfate release and their impact on the structure of schwertmannite as a function of pH and phosphate concentration. Kinetic and equilibrium batch experiments with synthetic schwertmannite were carried out at pH 3, 6, and 8, and the solid-phase was analyzed using scanning electron microscopy, Mössbauer spectroscopy, infra-red spectroscopy and X-ray diffraction spectroscopy. We found a strong correlation between phosphate sorption and sulfate release, with both following a two-step sorption model. Kinetics of phosphate sorption and sulfate release were faster at more alkaline pH. Maximum phosphate sorption was found at pH 6 (1.7 mmol PO43- g-1) , which decreased to 1.5 and 1.2 mmol PO43- g-1 at pH 3 and 8, respectively. Fourier transform infrared spectroscopy revealed a shift from inner- to outer-spherical coordination of sulfate with increasing pH. 57Mössbauer analyses of schwertmannite indicated a proto-transformation of schwertmannite at neutral to alkaline pH, characterized by a rise in a partially ordered sextet area. This change was interpreted as an increase in crystallinity resulting from the transition from Fe-SO4 to Fe-O domains. This pH-induced proto-transformation was inhibited in the presence of phosphate. We concluded that the phosphate sorption rate and maximum as well as the proto-transformation of schwertmannite were strongly affected by the mineral’s affinity for sulfate. Sorption to schwertmannite should primarily be regarded as a competitive exchange reaction between the sorbing oxyanion and the bound sulfate. This resulted in the highest phosphate sorption at circumneutral pH, a stark contrast to non-sulfate-containing Fe(III) (oxyhydr)oxides, where phosphate sorption is highest at acidic pH. Our results are important for a fundamental understanding of the sorption properties of schwertmannite in phosphate-rich environments as they point towards the central role of sulfate coordination for phosphate immobilization.
DOI: 10.5281/zenodo.10802984
title: Sulfate affinity controls phosphate sorption and the proto-transformation of schwertmannite

description: Schwertmannite is a metastable sulfate-rich ferric iron Fe(III) (oxyhydr)oxide and a common mineral in acid mine drainage sites and acid sulfate soils. Schwertmannite is also used as a sorbent in various industrial applications, including phosphate removal for water treatment and environmental remediation. Phosphate sorption to schwertmannite, however, is complex and likely involves ligand exchange for inner- and outer-spherically coordinated sulfate groups, both on the surface and in the tunnel structure of the mineral. Here, we investigated phosphate sorption, concomitant sulfate release and their impact on the structure of schwertmannite as a function of pH and phosphate concentration. Kinetic and equilibrium batch experiments with synthetic schwertmannite were carried out at pH 3, 6, and 8, and the solid-phase was analyzed using scanning electron microscopy, Mössbauer spectroscopy, infra-red spectroscopy and X-ray diffraction spectroscopy. We found a strong correlation between phosphate sorption and sulfate release, with both following a two-step sorption model. Kinetics of phosphate sorption and sulfate release were faster at more alkaline pH. Maximum phosphate sorption was found at pH 6 (1.7 mmol PO43- g-1) , which decreased to 1.5 and 1.2 mmol PO43- g-1 at pH 3 and 8, respectively. Fourier transform infrared spectroscopy revealed a shift from inner- to outer-spherical coordination of sulfate with increasing pH. 57Mössbauer analyses of schwertmannite indicated a proto-transformation of schwertmannite at neutral to alkaline pH, characterized by a rise in a partially ordered sextet area. This change was interpreted as an increase in crystallinity resulting from the transition from Fe-SO4 to Fe-O domains. This pH-induced proto-transformation was inhibited in the presence of phosphate. We concluded that the phosphate sorption rate and maximum as well as the proto-transformation of schwertmannite were strongly affected by the mineral’s affinity for sulfate. Sorption to schwertmannite should primarily be regarded as a competitive exchange reaction between the sorbing oxyanion and the bound sulfate. This resulted in the highest phosphate sorption at circumneutral pH, a stark contrast to non-sulfate-containing Fe(III) (oxyhydr)oxides, where phosphate sorption is highest at acidic pH. Our results are important for a fundamental understanding of the sorption properties of schwertmannite in phosphate-rich environments as they point towards the central role of sulfate coordination for phosphate immobilization.
DOI: 10.5281/zenodo.10302800
title: Sulfate affinity controls phosphate sorption and the proto-transformation of schwertmannite

description: Schwertmannite is a metastable sulfate-rich ferric iron Fe(III) (oxyhydr)oxide and a common mineral in acid mine drainage sites and acid sulfate soils. Schwertmannite is also used as a sorbent in various industrial applications, including phosphate removal for water treatment and environmental remediation. Phosphate sorption to schwertmannite, however, is complex and likely involves ligand exchange for inner- and outer-spherically coordinated sulfate groups, both on the surface and in the tunnel structure of the mineral. Here, we investigated phosphate sorption, concomitant sulfate release and their impact on the structure of schwertmannite as a function of pH and phosphate concentration. Kinetic and equilibrium batch experiments with synthetic schwertmannite were carried out at pH 3, 6, and 8, and the solid-phase was analyzed using scanning electron microscopy, Mössbauer spectroscopy, infra-red spectroscopy and X-ray diffraction spectroscopy. We found a strong correlation between phosphate sorption and sulfate release, with both following a two-step sorption model. Kinetics of phosphate sorption and sulfate release were faster at more alkaline pH. Maximum phosphate sorption was found at pH 6 (1.7 mmol PO43- g-1) , which decreased to 1.5 and 1.2 mmol PO43- g-1 at pH 3 and 8, respectively. Fourier transform infrared spectroscopy revealed a shift from inner- to outer-spherical coordination of sulfate with increasing pH. 57Mössbauer analyses of schwertmannite indicated a proto-transformation of schwertmannite at neutral to alkaline pH, characterized by a rise in a partially ordered sextet area. This change was interpreted as an increase in crystallinity resulting from the transition from Fe-SO4 to Fe-O domains. This pH-induced proto-transformation was inhibited in the presence of phosphate. We concluded that the phosphate sorption rate and maximum as well as the proto-transformation of schwertmannite were strongly affected by the mineral’s affinity for sulfate. Sorption to schwertmannite should primarily be regarded as a competitive exchange reaction between the sorbing oxyanion and the bound sulfate. This resulted in the highest phosphate sorption at circumneutral pH, a stark contrast to non-sulfate-containing Fe(III) (oxyhydr)oxides, where phosphate sorption is highest at acidic pH. Our results are important for a fundamental understanding of the sorption properties of schwertmannite in phosphate-rich environments as they point towards the central role of sulfate coordination for phosphate immobilization.
DOI: 10.5281/zenodo.10302801
title: Sulfate affinity controls phosphate sorption and the proto-transformation of schwertmannite

description: Schwertmannite is a metastable sulfate-rich ferric iron Fe(III) (oxyhydr)oxide and a common mineral in acid mine drainage sites and acid sulfate soils. Schwertmannite is also used as a sorbent in various industrial applications, including phosphate removal in water treatment and environmental remediation. Phosphate sorption to schwertmannite, however, is complex and likely involves ligand exchange for inner- and outer-spherically coordinated sulfate groups, both on the surface and in the tunnel structure of the mineral. Here, we investigated phosphate sorption, concomitant sulfate release and their impact on the structure of schwertmannite as a function of pH and phosphate concentration. Kinetic and equilibrium batch experiments with synthetic schwertmannite were carried out at pH 3, 6, and 8, and the solid-phase was analyzed using a combination of microscopic, spectroscopic, and X-ray diffraction techniques. We found a strong correlation between phosphate sorption and sulfate release, with both following a two-step sorption model. The kinetics of phosphate sorption and sulfate release were faster at higher pH levels. Maximum phosphate sorption was found at pH 6 (1.7 mmol PO43− g−1), which decreased to 1.5 and 1.2 mmol PO43− g−1 at pH 3 and 8, respectively. Fourier transform infrared spectroscopy revealed a shift from inner- to outer-spherical coordination of sulfate with increasing pH. 57Fe-Mössbauer analyses of schwertmannite demonstrated an initial transformation of schwertmannite at neutral to alkaline pH, characterized by a rise in a partially ordered sextet area. This change was interpreted as an increase in crystallinity resulting from the transition from Fe-SO4 to FeO domains. The emerging phase differed from the original schwertmannite, but did not represent a complete change to a new crystalline phase, thus indicating a proto-transformation of schwertmannite. This pH-induced proto-transformation was inhibited in the presence of phosphate. We concluded that the phosphate sorption rate and maximum as well as the proto-transformation of schwertmannite were strongly affected by the mineral's affinity for sulfate. Sorption to schwertmannite should primarily be regarded as a competitive exchange reaction between the sorbing oxyanion and the bound sulfate. As a result, the highest phosphate sorption occurred at circumneutral pH, in stark contrast to non-sulfate-containing Fe(III) (oxyhydr)oxides, where phosphate sorption is highest at acidic pH. Our findings are important for a fundamental understanding of the sorption properties of schwertmannite in phosphate-rich environments as they point towards the central role of sulfate coordination for phosphate immobilization.
DOI: 10.21227/cj08-qn62
title: Container Handling Strategy Dataset: Efficiency Analysis in Dockyard Operations

description: The datasets comprise configurations of loading and unloading plans for container ships, generated under six distinct scenarios based on varying numbers of stacks and maximum stack heights of containers in each row. Considering typical container ship characteristics, scenarios encompass stack numbers ranging from 5 to 30 and maximum stack heights from 4 to 10. The dataset includes loading and unloading plans for dockyard containers, with sample plans provided for small ships. Each of the 5 datasets comprises 20 instances representing different container loading and unloading scenarios. Each instance is characterized by a specific strategy detailing the number of single cycles, dual cycles, rehandles, and operation time required. The dataset offers insights into the efficiency and performance of various strategies in handling container logistics within a dockyard setting.
DOI: 10.5281/zenodo.10827366
title: COREQ checklist: Focus group for 'Streamlining Concept Mapping for Clinical Data Enrichment: A Process-focused approach in medical Data Warehouses'

description: Presentation of the 32 items on the consolidated criteria for reporting qualitative research (COREQ) checklist. The information is used for the report on a focus group that was conducted as part of the preparation of a publication. The title of the article is (as of submission on 18.03.2024): 'Streamlining Concept Mapping for Clinical Data Enrichment: A Process-focused approach in Medical Data Warehouses'.
DOI: 10.5281/zenodo.10827367
title: COREQ checklist: Focus group for 'Streamlining Concept Mapping for Clinical Data Enrichment: A Process-focused approach in medical Data Warehouses'

description: Presentation of the 32 items on the consolidated criteria for reporting qualitative research (COREQ) checklist. The information is used for the report on a focus group that was conducted as part of the preparation of a publication. The title of the article is (as of submission on 18.03.2024): 'Streamlining Concept Mapping for Clinical Data Enrichment: A Process-focused approach in Medical Data Warehouses'.
DOI: 10.6084/m9.figshare.25425286
title: Supporting information_BpF5H.pdf

description: The file is a supporting material for a research article which is currently under revision.
DOI: 10.6084/m9.figshare.25425286.v1
title: Supporting information_BpF5H.pdf

description: The file is a supporting material for a research article which is currently under revision.
DOI: 10.3886/e199061
title: Extract for ``Singles, couples, and their labor supply: long-run trends and short-run fluctuations''

description: PSID data extract used in the paper ``Singles, couples, and their labor supply: long-run trends and short-run fluctuations''. <br>
DOI: 10.3886/e199061v1
title: Extract for ``Singles, couples, and their labor supply: long-run trends and short-run fluctuations''

description: PSID data extract used in the paper ``Singles, couples, and their labor supply: long-run trends and short-run fluctuations''. <br>
DOI: 10.5281/zenodo.10827300
title: DDA-GTN: large-scale drug repurposing on drug-gene-disease heterogenous association networks using graph transformers
DOI: 10.5281/zenodo.10827301
title: DDA-GTN: large-scale drug repurposing on drug-gene-disease heterogenous association networks using graph transformers
DOI: 10.5281/zenodo.10826914
title: DDA-GTN: large-scale drug repurposing on drug-gene-disease heterogenous association networks using graph transformers
DOI: 10.5281/zenodo.10826915
title: DDA-GTN: large-scale drug repurposing on drug-gene-disease heterogenous association networks using graph transformers
DOI: 10.57745/51aqhi
title: Raw_data_IE_Pst_Meyer_et_al._2024.tab

description: :unav
DOI: 10.57745/mlp0ze
title: Raw_data_LP_Pst_Meyer_et_al._2024.xlsx

description: :unav
DOI: 10.57745/wwafag
title: Raw data IE LP Pst Meyer et al. 2024.tab

description: Phenotypic data corresponding to two aggressiveness components (infection efficiency and latency period) of Puccinia striiformis f. sp. tritici assessed under warm and cold thermal regimes, allowing comparison of 10 European isolates collected between 2010 and 2020 in Europe with three 'old' reference isolates. Related publication: Meyer KJG, Leconte M, Vidal T, Goyeau H, Suffert F. 2024. Is thermal aptitude a pivotal driver in the establishment of recent Puccinia striiformis f. sp. tritici lineages in Europe? Journal of Plant Pathology / bioRxiv (https://doi.org/10.1101/2023.08.11.552914)
DOI: 10.57745/mkwsdr
title: Raw_data_LP_Pst_Meyer_et_al._2024.tab

description: :unav
DOI: 10.57745/z9tgjd
title: READ ME.txt

description: :unav
DOI: 10.6084/m9.figshare.24754104.v2
title: taunet

description: Noise Covariance for CMB
DOI: 10.5281/zenodo.10825365
title: Data for "A learned score function improves the power of mass spectrometry database search"

description: These data files are associated with the following publication:





Varun Ananth, Justin Sanders, Melih Yilmaz, Bo Wen, Sewoong Oh and William Stafford Noble. "A learned score function improves the power of mass spectrometry database search". Bioinformatics (Proceedings of the ISMB).  2024.



For the benchmarking data, we used a dataset that is publicly available on ProteomeXchange (PXD028735). The paper that introduced this dataset is:





Van Puyvelde, B., Daled, S., Willems, S., Gabriels, R., Gonzalez de Peredo, A., Chaoui, K., Mouton-Barbosa, E., Bouyssié, D., Boonen, K., Hughes, C. J., Gethings, L. A., Perez-Riverol, Y., Bloomfield, N., Tate, S., Schiltz, O., Martens, L., Deforce, D., & Dhaenens, M. (2022). A comprehensive LFQ benchmark dataset on modern day acquisition strategies in proteomics. In Scientific Data (Vol. 9, Issue 1). Springer Science and Business Media LLC. https://doi.org/10.1038/s41597-022-01216-6



More specifically, the following `.raw` files were downloaded:



LFQ_Orbitrap_DDA_Ecoli_01.raw

LFQ_Orbitrap_DDA_Human_01.raw

LFQ_Orbitrap_DDA_Yeast_01.raw


Those files can be accessed via FTP here.

We upload here the annotated .mgf files created from these .raw files, as described in our paper.

The human, yeast, and E. coli .fasta files used in all database searches were downloaded from UniProt on 11/6/23, 4:30 PM.





Bateman, A., Martin, M.-J., Orchard, S., Magrane, M., Ahmad, S., Alpi, E., Bowler-Barnett, E. H., Britto, R., Bye-A-Jee, H., Cukura, A., Denny, P., Dogan, T., Ebenezer, T., Fan, J., Garmiri, P., da Costa Gonzales, L. J., Hatton-Ellis, E., Hussein, A., … Zhang, J. (2022). UniProt: the Universal Protein Knowledgebase in 2023. In Nucleic Acids Research (Vol. 51, Issue D1, pp. D523–D531). Oxford University Press (OUP). https://doi.org/10.1093/nar/gkac1052



We include these files here, with only minor modifications to replace U amino acids with X so that all amino acids fall into Casanovo-DB's vocabulary.
DOI: 10.5281/zenodo.10825364
title: Data for "A learned score function improves the power of mass spectrometry database search"

description: These data files are associated with the following publication:





Varun Ananth, Justin Sanders, Melih Yilmaz, Bo Wen, Sewoong Oh and William Stafford Noble. "A learned score function improves the power of mass spectrometry database search". Bioinformatics (Proceedings of the ISMB).  2024.



For the benchmarking data, we used a dataset that is publicly available on ProteomeXchange (PXD028735). The paper that introduced this dataset is:





Van Puyvelde, B., Daled, S., Willems, S., Gabriels, R., Gonzalez de Peredo, A., Chaoui, K., Mouton-Barbosa, E., Bouyssié, D., Boonen, K., Hughes, C. J., Gethings, L. A., Perez-Riverol, Y., Bloomfield, N., Tate, S., Schiltz, O., Martens, L., Deforce, D., & Dhaenens, M. (2022). A comprehensive LFQ benchmark dataset on modern day acquisition strategies in proteomics. In Scientific Data (Vol. 9, Issue 1). Springer Science and Business Media LLC. https://doi.org/10.1038/s41597-022-01216-6



More specifically, the following `.raw` files were downloaded:



LFQ_Orbitrap_DDA_Ecoli_01.raw

LFQ_Orbitrap_DDA_Human_01.raw

LFQ_Orbitrap_DDA_Yeast_01.raw


Those files can be accessed via FTP here.

We upload here the annotated .mgf files created from these .raw files, as described in our paper.

The human, yeast, and E. coli .fasta files used in all database searches were downloaded from UniProt on 11/6/23, 4:30 PM.





Bateman, A., Martin, M.-J., Orchard, S., Magrane, M., Ahmad, S., Alpi, E., Bowler-Barnett, E. H., Britto, R., Bye-A-Jee, H., Cukura, A., Denny, P., Dogan, T., Ebenezer, T., Fan, J., Garmiri, P., da Costa Gonzales, L. J., Hatton-Ellis, E., Hussein, A., … Zhang, J. (2022). UniProt: the Universal Protein Knowledgebase in 2023. In Nucleic Acids Research (Vol. 51, Issue D1, pp. D523–D531). Oxford University Press (OUP). https://doi.org/10.1093/nar/gkac1052



We include these files here, with only minor modifications to replace U amino acids with X so that all amino acids fall into Casanovo-DB's vocabulary.