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1. Shakespeare and Company Project Dataset: Lending Library Books

Author(s):
Kotin, Joshua; Koeser, Rebecca Sutton; Adair, Carl; Alagappan, Serena; Bauer, Jean; Browne, Oliver J.; Budak, Nick; Calver, Harriet; Chow, Jin; Davis, Ian; Doroudian, Gissoo; Engel, Currie; Green, Elspeth; Hicks, Benjamin; Joelson, Madeleine E.; Kelly, Carolyn; Krolewski, Sara; Li, Xinyi; Maag, Ellie; Mahoney, Cate; McCarthy, Jesse D.; Naydan, Mary; Ruehl, Isabel; Thode, Sylvie; VanSant, Camey; Wulfman, Clifford E.
Abstract:
This dataset includes information about approximately 6,000 books and other items with bibliographic data as well as summary information about when the item circulated in the Shakespeare and Company lending library and the number of times an item was borrowed or purchased.
Type:
Dataset
Issue Date:
July 2020

2. Shakespeare and Company Project Dataset: Lending Library Books

Author(s):
Kotin, Joshua; Koeser, Rebecca Sutton; Adair, Carl; Alagappan, Serena; Allen, Paige; Bauer, Jean; Browne, Oliver J.; Budak, Nick; Calver, Harriet; Chow, Jin Yun; Davis, Ian; Doroudian, Gissoo; Engel, Currie; Gautreau, Violet; Gjaja, Alex; Green, Elspeth; Hart, Isaac; Hicks, Benjamin; Joelson, Madeleine E.; Kelly, Carolyn; Krolewski, Sara; Li, Xinyi; Maag, Ellie; Macksey, Elizabeth; Mahoney, Cate; Mancino, Francesca; McCarthy, Jesse D.; Naydan, Mary; Root, Sally; Ruehl, Isabel; Thode, Sylvie; Vandermel, Katherine; VanSant, Camey; Wulfman, Clifford E.
Abstract:
This dataset includes information about approximately 6,000 books and other items with bibliographic data as well as summary information about when the item circulated in the Shakespeare and Company lending library and the number of times an item was borrowed or purchased.
Type:
Dataset
Issue Date:
29 January 2021

3. Shakespeare and Company Project Dataset: Lending Library Books

Author(s):
Kotin, Joshua; Koeser, Rebecca Sutton; Adair, Carl; Aiyer, Priyanka; Alagappan, Serena; Allen, Paige; Bauer, Jean; Browne, Oliver J.; Budak, Nick; Calver, Harriet; Chiossi, Robert; Chow, Jin Yun; Coles, Iliyah; Davis, Ian; Doroudian, Gissoo; Engel, Currie; Gautreau, Violet; Gjaja, Alex; Green, Elspeth; Hart, Isaac; Hicks, Benjamin; Himmelfarb, Samuel; Joelson, Madeleine E.; Karmanov, Fedor; Kelly, Carolyn; Krolewski, Sara; Li, Xinyi; Maag, Ellie; Macksey, Elizabeth; Mahoney, Cate; Mancino, Francesca; McCarthy, Jesse D.; Naydan, Mary; Patch, Yvonne; Root, Sally; Ruehl, Isabel; Thode, Sylvie; Vandermel, Katherine; VanSant, Camey; Wulfman, Clifford E.
Abstract:
This dataset includes information about approximately 6,000 books and other items with bibliographic data as well as summary information about when the item circulated in the Shakespeare and Company lending library and the number of times an item was borrowed or purchased.
Type:
Dataset
Issue Date:
January 2022

4. Shakespeare and Company Project Dataset: Lending Library Events

Author(s):
Kotin, Joshua; Koeser, Rebecca Sutton; Adair, Carl; Alagappan, Serena; Bauer, Jean; Browne, Oliver J.; Budak, Nick; Calver, Harriet; Chow, Jin; Davis, Ian; Doroudian, Gissoo; Engel, Currie; Green, Elspeth; Hicks, Benjamin; Joelson, Madeleine E.; Kelly, Carolyn; Krolewski, Sara; Li, Xinyi; Maag, Ellie; Mahoney, Cate; McCarthy, Jesse D.; Naydan, Mary; Ruehl, Isabel; Thode, Sylvie; VanSant, Camey; Wulfman, Clifford E.
Abstract:
The events dataset includes information about approximately 33,700 lending library events including membership activities such as subscriptions, renewals and reimbursements and book-related activities such as borrowing and purchasing. For events related to lending library cards that are available as digital surrogates, IIIF links are provided.
Type:
Dataset
Issue Date:
July 2020

5. Shakespeare and Company Project Dataset: Lending Library Events

Author(s):
Kotin, Joshua; Koeser, Rebecca Sutton; Adair, Carl; Alagappan, Serena; Allen, Paige; Bauer, Jean; Browne, Oliver J.; Budak, Nick; Calver, Harriet; Chow, Jin Yun; Davis, Ian; Doroudian, Gissoo; Engel, Currie; Gautreau, Violet; Gjaja, Alex; Green, Elspeth; Hart, Isaac; Hicks, Benjamin; Joelson, Madeleine E.; Kelly, Carolyn; Krolewski, Sara; Li, Xinyi; Maag, Ellie; Macksey, Elizabeth; Mahoney, Cate; Mancino, Francesca; McCarthy, Jesse D.; Naydan, Mary; Root, Sally; Ruehl, Isabel; Thode, Sylvie; Vandermel, Katherine; VanSant, Camey; Wulfman, Clifford E.
Abstract:
The Shakespeare and Company Project: Lending Library Events dataset includes information about approximately 35,000 lending library events including membership activities such as subscriptions, renewals and reimbursements and book-related activities such as borrowing and purchasing. For events related to lending library cards that are available as digital surrogates, IIIF links are provided.
Type:
Dataset
Issue Date:
29 January 2021

6. Shakespeare and Company Project Dataset: Lending Library Events

Author(s):
Kotin, Joshua; Koeser, Rebecca Sutton; Adair, Carl; Aiyer, Priyanka; Alagappan, Serena; Allen, Paige; Bauer, Jean; Browne, Oliver J.; Budak, Nick; Calver, Harriet; Chiossi, Robert; Chow, Jin Yun; Coles, Iliyah; Davis, Ian; Doroudian, Gissoo; Engel, Currie; Gautreau, Violet; Gjaja, Alex; Green, Elspeth; Hart, Isaac; Hicks, Benjamin; Himmelfarb, Samuel; Joelson, Madeleine E.; Karmanov, Fedor; Kelly, Carolyn; Krolewski, Sara; Li, Xinyi; Maag, Ellie; Macksey, Elizabeth; Mahoney, Cate; Mancino, Francesca; McCarthy, Jesse D.; Naydan, Mary; Patch, Yvonne; Root, Sally; Ruehl, Isabel; Thode, Sylvie; Vandermel, Katherine; VanSant, Camey; Wulfman, Clifford E.
Abstract:
The Shakespeare and Company Project: Lending Library Events dataset includes information about approximately 35,000 lending library events including membership activities such as subscriptions, renewals and reimbursements and book-related activities such as borrowing and purchasing. For events related to lending library cards that are available as digital surrogates, IIIF links are provided.
Type:
Dataset
Issue Date:
January 2022

7. Shakespeare and Company Project Dataset: Lending Library Members

Author(s):
Kotin, Joshua; Koeser, Rebecca Sutton; Adair, Carl; Alagappan, Serena; Bauer, Jean; Browne, Oliver J.; Budak, Nick; Calver, Harriet; Chow, Jin; Davis, Ian; Doroudian, Gissoo; Engel, Currie; Green, Elspeth; Hicks, Benjamin; Joelson, Madeleine E.; Kelly, Carolyn; Krolewski, Sara; Li, Xinyi; Maag, Ellie; Mahoney, Cate; McCarthy, Jesse D.; Naydan, Mary; Ruehl, Isabel; Thode, Sylvie; VanSant, Camey; Wulfman, Clifford E.
Abstract:
The Shakespeare and Company Project: Lending Library Members dataset includes information about approximately 5,700 members of Sylvia Beach's Shakespeare and Company lending library.
Type:
Dataset
Issue Date:
July 2020

8. Shakespeare and Company Project Dataset: Lending Library Members

Author(s):
Kotin, Joshua; Koeser, Rebecca Sutton; Adair, Carl; Aiyer, Priyanka; Alagappan, Serena; Allen, Paige; Bauer, Jean; Browne, Oliver J.; Budak, Nick; Calver, Harriet; Chiossi, Robert; Chow, Jin Yun; Coles, Iliyah; Davis, Ian; Doroudian, Gissoo; Engel, Currie; Gautreau, Violet; Gjaja, Alex; Green, Elspeth; Hart, Isaac; Hicks, Benjamin; Himmelfarb, Samuel; Joelson, Madeleine E.; Karmanov, Fedor; Kelly, Carolyn; Krolewski, Sara; Li, Xinyi; Maag, Ellie; Macksey, Elizabeth; Mahoney, Cate; Mancino, Francesca; McCarthy, Jesse D.; Naydan, Mary; Patch, Yvonne; Root, Sally; Ruehl, Isabel; Thode, Sylvie; Vandermel, Katherine; VanSant, Camey; Wulfman, Clifford E.
Abstract:
The Shakespeare and Company Project: Lending Library Members dataset includes information about approximately 5,200 members of Sylvia Beach's Shakespeare and Company lending library.
Type:
Dataset
Issue Date:
January 2022

9. Shakespeare and Company Project Dataset: Lending Library Members

Author(s):
Kotin, Joshua; Koeser, Rebecca Sutton; Adair, Carl; Alagappan, Serena; Allen, Paige; Bauer, Jean; Browne, Oliver J.; Budak, Nick; Calver, Harriet; Chow, Jin Yun; Davis, Ian; Doroudian, Gissoo; Engel, Currie; Gautreau, Violet; Gjaja, Alex; Green, Elspeth; Hart, Isaac; Hicks, Benjamin; Joelson, Madeleine E.; Kelly, Carolyn; Krolewski, Sara; Li, Xinyi; Maag, Ellie; Macksey, Elizabeth; Mahoney, Cate; Mancino, Francesca; McCarthy, Jesse D.; Naydan, Mary; Root, Sally; Ruehl, Isabel; Thode, Sylvie; Vandermel, Katherine; VanSant, Camey; Wulfman, Clifford E.
Abstract:
The Shakespeare and Company Project: Lending Library Members dataset includes information about approximately 5,600 members of Sylvia Beach's Shakespeare and Company lending library.
Type:
Dataset
Issue Date:
29 January 2021

10. Shakespeare and Company Project Dataset: Lending Library Members, Books, Events

Author(s):
Kotin, Joshua; Koeser, Rebecca Sutton; Adair, Carl; Alagappan, Serena; Allen, Paige; Bauer, Jean; Browne, Oliver J.; Budak, Nick; Calver, Harriet; Chow, Jin Yun; Davis, Ian; Doroudian, Gissoo; Engel, Currie; Gautreau, Violet; Gjaja, Alex; Green, Elspeth; Hart, Isaac; Hicks, Benjamin; Joelson, Madeleine E.; Kelly, Carolyn; Krolewski, Sara; Li, Xinyi; Maag, Ellie; Macksey, Elizabeth; Mahoney, Cate; Mancino, Francesca; McCarthy, Jesse D.; Naydan, Mary; Root, Sally; Ruehl, Isabel; Thode, Sylvie; Vandermel, Katherine; VanSant, Camey; Wulfman, Clifford E.
Abstract:
The Shakespeare and Company Project makes three datasets available to download in CSV and JSON formats. The datasets provide information about lending library members; the books that circulated in the lending library; and lending library events, including borrows, purchases, memberships, and renewals. The datasets may be used individually or in combination site URLs are consistent identifiers across all three. The DOIs for each dataset are as follows: Members (https://doi.org/10.34770/nsa4-3t76); Books (https://doi.org/10.34770/079z-h206); Events (https://doi.org/10.34770/rtbp-kv40).
Type:
Dataset
Issue Date:
29 January 2021

11. Shakespeare and Company Project Dataset: Lending Library Members, Books, Events

Author(s):
Kotin, Joshua; Koeser, Rebecca Sutton; Adair, Carl; Alagappan, Serena; Bauer, Jean; Browne, Oliver J.; Budak, Nick; Calver, Harriet; Chow, Jin; Davis, Ian; Doroudian, Gissoo; Engel, Currie; Green, Elspeth; Hicks, Benjamin; Joelson, Madeleine E.; Kelly, Carolyn; Krolewski, Sara; Li, Xinyi; Maag, Ellie; Mahoney, Cate; McCarthy, Jesse D.; Naydan, Mary; Ruehl, Isabel; Thode, Sylvie; VanSant, Camey; Wulfman, Clifford E.
Abstract:
The Shakespeare and Company Project makes three datasets available to download in CSV and JSON formats. The datasets provide information about lending library members; the books that circulated in the lending library; and lending library events, including borrows, purchases, memberships, and renewals. The datasets may be used individually or in combination site URLs are consistent identifiers across all three. The DOIs for each dataset are as follows: Members (https://doi.org/10.34770/ht30-g395); Books (https://doi.org/10.34770/g467-3w07); Events (https://doi.org/10.34770/2r93-0t85).
Type:
Dataset
Issue Date:
July 2020

12. Shakespeare and Company Project Dataset: Lending Library Members, Books, Events

Author(s):
Kotin, Joshua; Koeser, Rebecca Sutton; Adair, Carl; Aiyer, Priyanka; Alagappan, Serena; Allen, Paige; Bauer, Jean; Browne, Oliver J.; Budak, Nick; Calver, Harriet; Chiossi, Robert; Chow, Jin Yun; Coles, Iliyah; Davis, Ian; Doroudian, Gissoo; Engel, Currie; Gautreau, Violet; Gjaja, Alex; Green, Elspeth; Hart, Isaac; Hicks, Benjamin; Himmelfarb, Samuel; Joelson, Madeleine E.; Karmanov, Fedor; Kelly, Carolyn; Krolewski, Sara; Li, Xinyi; Maag, Ellie; Macksey, Elizabeth; Mahoney, Cate; Mancino, Francesca; McCarthy, Jesse D.; Naydan, Mary; Patch, Yvonne; Root, Sally; Ruehl, Isabel; Thode, Sylvie; Vandermel, Katherine; VanSant, Camey; Wulfman, Clifford E.
Abstract:
The Shakespeare and Company Project makes three datasets available to download in CSV and JSON formats. The datasets provide information about lending library members; the books that circulated in the lending library; and lending library events, including borrows, purchases, memberships, and renewals. The datasets may be used individually or in combination site URLs are consistent identifiers across all three.
Type:
Dataset
Issue Date:
January 2022

14. Data for: "The origin of non-skeletal carbonate mud and implications for global climate"

Author(s):
Geyman, Emily C.; Wu, Ziman; Nadeau, Matthew D.; Edmonsond, Stacey; Turner, Andrew; Purkis, Sam J.; Howes, Bolton; Dyer, Blake; Ahm, Anne-Sofie C.; Yao, Nan; Deutsch, Curtis A.; Higgins, John A.; Stolper, Daniel A.; Maloof, Adam C.
Abstract:
Carbonate mud represents one of the most important geochemical archives for reconstructing ancient climatic, environmental, and evolutionary change from the rock record. Mud also represents a major sink in the global carbon cycle. Yet, there remains no consensus about how and where carbonate mud is formed. In this contribution, we present new geochemical data that bear on this problem, including stable isotope and minor and trace element data from carbonate sources in the modern Bahamas such as ooids, corals, foraminifera, and green algae.
Type:
Dataset
Issue Date:
16 June 2022

15. Femtosecond X-ray Diffraction of Laser-shocked Forsterite (Mg2SiO4) to 122 GPa

Author(s):
Kim, Donghoon; Tracy, Sally J.; Smith, Raymond F.; Gleason, Arianna E.; Bolme, Cindy A.; Prakapenka, Vitali B.; Appel, Karen; Speziable, Sergio; Wicks, June K.; Berryman, Eleanor J.; Han, Sirus K.; Schoelmerich, Markus O.; Lee, Hae Ja; Nagler, Bob; Cunningham, Eric F.; Akin, Minta C.; Asimow, Paul D.; Eggert, Jon H.; Duffy, Thomas S.
Abstract:
The behavior of forsterite, Mg2SiO4, under dynamic compression is of fundamental importance for understanding its phase transformations and high-pressure behavior. Here, we have carried out an in situ X-ray diffraction study of laser-shocked poly- and single-crystal forsterite (a-, b-, and c- orientations) from 19 to 122 GPa using the Matter in Extreme Conditions end-station of the Linac Coherent Light Source. Under laser-based shock loading, forsterite does not transform to the high-pressure equilibrium assemblage of MgSiO3 bridgmanite and MgO periclase, as was suggested previously. Instead, we observe forsterite and forsterite III, a metastable polymorph of Mg2SiO4, coexisting in a mixed-phase region from 33 to 75 GPa for both polycrystalline and single-crystal samples. Densities inferred from X-ray diffraction data are consistent with earlier gas-gun shock data. At higher stress, the behavior observed is sample-dependent. Polycrystalline samples undergo amorphization above 79 GPa. For [010]- and [001]-oriented crystals, a mixture of crystalline and amorphous material is observed to 108 GPa, whereas the [100]-oriented crystal adopts an unknown crystal structure at 122 GPa. The Q values of the first two sharp diffraction peaks of amorphous Mg2SiO4 show a similar trend with compression as those observed for MgSiO3 glass in both recent static and laser-compression experiments. Upon release to ambient pressure, all samples retain or revert to forsterite with evidence for amorphous material also present in some cases. This study demonstrates the utility of femtosecond free-electron laser X-ray sources for probing the time evolution of high-pressure silicates through the nanosecond-scale events of shock compression and release.
Type:
Dataset
Issue Date:
November 2020

16. Fast animal pose estimation using deep neural networks

Author(s):
Pereira, Talmo D.; Aldarondo, Diego E.; Willmore, Lindsay; Kislin, Mikhail; Wang, Samuel S.-H.; Murthy, Mala; Shaevitz, Joshua W.
Abstract:
Recent work quantifying postural dynamics has attempted to define the repertoire of behaviors performed by an animal. However, a major drawback to these techniques has been their reliance on dimensionality reduction of images which destroys information about which parts of the body are used in each behavior. To address this issue, we introduce a deep learning-based method for pose estimation, LEAP (LEAP Estimates Animal Pose). LEAP automatically predicts the positions of animal body parts using a deep convolutional neural network with as little as 10 frames of labeled data for training. This framework consists of a graphical interface for interactive labeling of body parts and software for training the network and fast prediction on new data (1 hr to train, 185 Hz predictions). We validate LEAP using videos of freely behaving fruit flies (Drosophila melanogaster) and track 32 distinct points on the body to fully describe the pose of the head, body, wings, and legs with an error rate of <3% of the animal's body length. We recapitulate a number of reported findings on insect gait dynamics and show LEAP's applicability as the first step in unsupervised behavioral classification. Finally, we extend the method to more challenging imaging situations (pairs of flies moving on a mesh-like background) and movies from freely moving mice (Mus musculus) where we track the full conformation of the head, body, and limbs.
Type:
Dataset
Issue Date:
30 May 2018

17. Data corresponding to our paper on "Chemotactic smoothing of collective migration"

Author(s):
Bhattacharjee, Tapomoy; Amchin, Daniel; Alert, Ricard; Ott, Jenna; Datta, Sujit
Abstract:
Collective migration -- the directed, coordinated motion of many self-propelled agents -- is a fascinating emergent behavior exhibited by active matter that has key functional implications for biological systems. Extensive studies have elucidated the different ways in which this phenomenon may arise. Nevertheless, how collective migration can persist when a population is confronted with perturbations, which inevitably arise in complex settings, is poorly understood. Here, by combining experiments and simulations, we describe a mechanism by which collectively migrating populations smooth out large-scale perturbations in their overall morphology, enabling their constituents to continue to migrate together. We focus on the canonical example of chemotactic migration of Escherichia coli, in which fronts of cells move via directed motion, or chemotaxis, in response to a self-generated nutrient gradient. We identify two distinct modes in which chemotaxis influences the morphology of the population: cells in different locations along a front migrate at different velocities due to spatial variations in (i) the local nutrient gradient and in (ii) the ability of cells to sense and respond to the local nutrient gradient. While the first mode is destabilizing, the second mode is stabilizing and dominates, ultimately driving smoothing of the overall population and enabling continued collective migration. This process is autonomous, arising without any external intervention; instead, it is a population-scale consequence of the manner in which individual cells transduce external signals. Our findings thus provide insights to predict, and potentially control, the collective migration and morphology of cell populations and diverse other forms of active matter.
Type:
Dataset
Issue Date:
24 June 2021

18. Derrida's Margins datasets

Author(s):
Chenoweth, Katie; Koeser, Rebecca Sutton; Altergott, Renée; Baron-Raiffe, Alexander; Bauer, Jean; Budak, Nick; Córdova, Chad; Hancock, Austin; Hicks, Benjamin; McElwee, Kevin; Vettier, Chloé
Abstract:
Derrida’s Margins <derridas-margins.princeton.edu> is a website and online research tool for annotations from the Library of Jacques Derrida, housed at Princeton University Library (PUL) <library.princeton.edu>. Jacques Derrida is one of the major figures of twentieth-century thought, and his library--which bears the traces of decades of close reading--represents a major intellectual archive. This project focused on annotations related to Derrida’s landmark 1967 work De la grammatologie (Of Grammatology).
Type:
Dataset
Issue Date:
29 October 2021

19. Dust and Starlight Maps for Galaxies in the KINGFISH Sample

Author(s):
Aniano, G.; Draine, B.T.; Hunt, L.K.; Sandstrom, K.; Calzetti, D.; Kennicutt, R.C.; Dale, D.A.; Galametz, M.; Gordon, K.D.; Leroy, A.K.; Smith, J.-D.T.; Roussel, H.; Sauvage, M.; Walter, F.; Armus, L.; Bolatto, A.D.; Boquien, M.; Crocker, A.; De Looze, I.; Donovan Meyer, J.; Helou, G.; Hinz, J.; Johnson, B.D.; Koda, J.; Miller, A.; Montiel, E.; Murphy, E.J.; Relano, M.; Rix, H.-W.; Schinnerer, E.; Skibba, R.; Wolfire, M.G.; Engelbracht, C.W.
Abstract:
Dust and starlight have been modeled for the KINGFISH project galaxies. For each pixel in each galaxy, we estimate: (1) dust surface density; (2) q_PAH, the dust mass fraction in PAHs; (3) distribution of starlight intensities heating the dust; (4) luminosity emitted by the dust; and (5) dust luminosity from regions with high starlight intensity. The modeling is as described in the paper "Modeling Dust and Starlight in Galaxies Observed by Spitzer and Herschel: The KINGFISH Sample", by G. Aniano, B.T. Draine, L.K. Hunt, K. Sandstrom, D. Calzetti, R.C. Kennicutt, D.A, Dale, and 26 other authors, accepted for publication in The Astrophysical Journal.
Type:
Dataset and Image

20. March Mathness: Effects of basketball on the brain

Author(s):
Antony, James; McDougle, Sam
Abstract:
Surprise signals a discrepancy between past and current beliefs. It is theorized to be linked to affective experiences, the creation of particularly resilient memories, and segmentation of the flow of experience into discrete perceived events. However, the ability to precisely measure naturalistic surprise has remained elusive. We used advanced basketball analytics to derive a quantitative measure of surprise and characterized its behavioral, physiological, and neural correlates in human subjects observing basketball games. We found that surprise was associated with segmentation of ongoing experiences, as reflected by subjectively perceived event boundaries and shifts in neocortical patterns underlying belief states. Interestingly, these effects differed by whether surprising moments contradicted or bolstered current predominant beliefs. Surprise also positively correlated with pupil dilation, activation in subcortical regions associated with dopamine, game enjoyment, and long-term memory. These investigations support key predictions from event segmentation theory and extend theoretical conceptualizations of surprise to real-world contexts.
Type:
Dataset
Issue Date:
2020

21. Competitive learning modulates memory consolidation during sleep

Author(s):
Antony, James W.; Cheng, Larry Y.; Brooks, Paula P.; Paller, Ken A.; Norman, Kenneth A.
Abstract:
Competition between memories can cause weakening of those memories. Here we investigated memory competition during sleep in human participants by presenting auditory cues that had been linked to two distinct picture-location pairs during wake. We manipulated competition during learning by requiring participants to rehearse picture-location pairs associated with the same sound either competitively (choosing to rehearse one over the other, leading to greater competition) or separately; we hypothesized that greater competition during learning would lead to greater competition when memories were cued during sleep. With separate-pair learning, we found that cueing benefited spatial retention. With competitive-pair learning, no benefit of cueing was observed on retention, but cueing impaired retention of well-learned pairs (where we expected strong competition). During sleep, post-cue beta power (16–30 Hz) indexed competition and predicted forgetting, whereas sigma power (11–16 Hz) predicted subsequent retention. Taken together, these findings show that competition between memories during learning can modulate how they are consolidated during sleep.
Type:
Dataset
Issue Date:
November 2018

22. Sleep spindle refractoriness segregates periods of memory reactivation

Author(s):
Antony, James W.; Piloto, Luis; Wang, Margaret; Brooks, Paula P.; Norman, Kenneth A.; Paller, Ken A.
Abstract:
The stability of long-term memories is enhanced by reactivation during sleep. Correlative evidence has linked memory reactivation with thalamocortical sleep spindles, although their functional role is not fully understood. Our initial study replicated this correlation and also demonstrated a novel rhythmicity to spindles, such that a spindle is more likely to occur approximately 3–6 s following a prior spindle. We leveraged this rhythmicity to test the role of spindles in memory by using real-time spindle tracking to present cues within versus just after the presumptive refractory period; as predicted, cues presented just after the refractory period led to better memory. Our findings demonstrate a precise temporal link between sleep spindles and memory reactivation. Moreover, they reveal a previously undescribed neural mechanism whereby spindles may segment sleep into two distinct substates: prime opportunities for reactivation and gaps that segregate reactivation events.
Type:
Dataset
Issue Date:
4 June 2018

23. Accessing Real-Life Episodic Information from Minutes versus Hours Earlier Modulates Hippocampal and High-Order Cortical Dynamics

Author(s):
Chen, Janice; Honey, Christopher; Simony, Erez; Arcaro, Michael; Norman, Kenneth; Hasson, Uri
Abstract:
It is well known that formation of new episodic memories depends on hippocampus, but in real-life settings (e.g., conversation), hippocampal amnesics can utilize information from several minutes earlier. What neural systems outside hippocampus might support this minutes-long retention? In this study, subjects viewed an audiovisual movie continuously for 25 min; another group viewed the movie in 2 parts separated by a 1-day delay. Understanding Part 2 depended on retrieving information from Part 1, and thus hippocampus was required in the day-delay condition. But is hippocampus equally recruited to access the same information from minutes earlier? We show that accessing memories from a few minutes prior elicited less interaction between hippocampus and default mode network (DMN) cortical regions than accessing day-old memories of identical events, suggesting that recent information was available with less reliance on hippocampal retrieval. Moreover, the 2 groups evinced reliable but distinct DMN activity timecourses, reflecting differences in information carried in these regions when Part 1 was recent versus distant. The timecourses converged after 4 min, suggesting a time frame over which the continuous-viewing group may have relied less on hippocampal retrieval. We propose that cortical default mode regions can intrinsically retain real-life episodic information for several minutes.
Type:
Dataset
Issue Date:
3 August 2015

25. Supporting data for Baldwin et al 2019 "Temporally Compound Heat Waves and Global Warming: An Emerging Hazard"

Author(s):
Baldwin, Jane W; Dessy, Jay Benjamin; Vecchi, Gabriel A; Oppenheimer, Michael; Jia, Liwei; Gudgel, Richard G; Paffendorf, Karen
Abstract:
This data is compiled to support a publication in the journal Earth's Future: Baldwin et al 2019 "Temporally Compound Heat Waves and Global Warming: An Emerging Hazard". The GCM GFDL CM2.5-FLOR was used to produce the raw climate model data. The model code for FLOR is freely available and can be downloaded at https://www.gfdl.noaa.gov/cm2-5-and-flor/. Code used to calculate the derived heat wave statistics data and produce figures in the paper is available at https://github.com/janewbaldwin/Compound-Heat-Waves The heat wave statistics derived output for only one definition is provided (daily minimum temperature, 90th percentile threshold, temporal structure 3114) which is the definition used the most in the paper figures. Statistics for the other definitions can be created by running the HWSTATS code provided in the corresponding github folder, which includes python scripts which do the analysis and PBS job scheduling and submission scripts which show how to run the python scripts. For more information on this, please see the github readme.
Type:
Dataset
Issue Date:
17 February 2019

26. Derrida's Margins datasets

Author(s):
Chenoweth, Katie; Baron-Raiffe, Alexander; Sutton Koeser, Rebecca
Abstract:
Derrida’s Margins <derridas-margins.princeton.edu> is a website and online research tool for annotations from the Library of Jacques Derrida, housed at Princeton University Library (PUL) <library.princeton.edu>. Jacques Derrida is one of the major figures of twentieth-century thought, and his library--which bears the traces of decades of close reading--represents a major intellectual archive. This project focused on annotations related to Derrida’s landmark 1967 work De la grammatologie (Of Grammatology).
Type:
Dataset
Issue Date:
15 October 2021

27. CrvA and CrvB form a curvature-inducing module sufficient to induce cell shape complexity in Gram-negative bacteria

Author(s):
Martin, Nicholas R; Blackman, Edith; Bratton, Benjamin P; Chase, Katelyn J; Bartlett, Thomas M; Gitai, Zemer
Abstract:
Bacterial species have diverse cell shapes that enable motility, colonization, and virulence. The cell wall defines bacterial shape and is primarily built by two cytoskeleton-guided synthesis machines, the elongasome and the divisome. However, the mechanisms producing complex shapes, like the curved-rod shape of Vibrio cholerae, are incompletely defined. Previous studies have reported that species-specific regulation of cytoskeleton-guided machines enables formation of complex bacterial shapes such as cell curvature and cellular appendages. In contrast, we report that CrvA and CrvB are sufficient to induce complex cell shape autonomously of the cytoskeleton in V. cholerae. The autonomy of the CrvAB module also enables it to induce curvature in the Gram-negative species Escherichia coli, Pseudomonas aeruginosa, Caulobacter crescentus, and Agrobacterium tumefaciens. Using inducible gene expression, quantitative microscopy, and biochemistry we show that CrvA and CrvB circumvent the need for patterning via cytoskeletal elements by regulating each other to form an asymmetrically-localized, periplasmic structure that directly binds to the cell wall. The assembly and disassembly of this periplasmic structure enables dynamic changes in cell shape. Bioinformatics indicate that CrvA and CrvB may have diverged from a single ancestral hybrid protein. Using fusion experiments in V. cholerae, we find that a synthetic CrvA/B hybrid protein is sufficient to induce curvature on its own, but that expression of two distinct proteins, CrvA and CrvB, promotes more rapid curvature induction. We conclude that morphological complexity can arise independently of cell shape specification by the core cytoskeleton-guided synthesis machines.
Type:
Dataset
Issue Date:
2021

28. Supporting GFDL data for Southern Ocean Freshwater release model experiments Initiative (SOFIA)

Author(s):
Griffies, Stephen M; Beadling, Rebecca L; Krasting, John P; Hurlin, William J
Abstract:
This output was produced in coordination with the Southern Ocean Freshwater release model experiments Initiative (SOFIA) and is the Tier 1 experiment where freshwater is delivered in a spatially and temporally uniform pattern at the surface of the ocean at sea surface temperature in a 1-degree latitude band extending from Antarctica’s coastline. The total additional freshwater flux imposed as a monthly freshwater flux entering the ocean is 0.1 Sv. Users are referred to the methods section of Beadling et al. (2022) for additional details on the meltwater implementation in CM4 and ESM4. The datasets in this collection contain model output from the coupled global climate model, CM4, and Earth System Model, ESM4, both developed at the Geophysical Fluid Dynamics Laboratory (GFDL) of the National Oceanic and Atmospheric Administration (NOAA). The ocean_monthly_z and ocean_annual_z output are provided as z depth levels in meters as opposed to the models native hybrid vertical ocean coordinate which consists of z* (quasi-geopotential) coordinates in the upper ocean through the mixed layer, transitioning to isopycnal (referenced to 2000 dbar) in the ocean interior. Please see README for further details.
Type:
Dataset
Issue Date:
18 April 2023

29. Noise correlations in the human brain and their impact on pattern classification

Author(s):
Bejjanki, Vikranth R.; da Silveira, Rava Azeredo; Cohen, Jonathan D.; Turk-Browne, Nicholas B.
Abstract:
Multivariate decoding methods, such as multivoxel pattern analysis (MVPA), are highly effective at extracting information from brain imaging data. Yet, the precise nature of the information that MVPA draws upon remains controversial. Most current theories emphasize the enhanced sensitivity imparted by aggregating across voxels that have mixed and weak selectivity. However, beyond the selectivity of individual voxels, neural variability is correlated across voxels, and such noise correlations may contribute importantly to accurate decoding. Indeed, a recent computational theory proposed that noise correlations enhance multivariate decoding from heterogeneous neural populations. Here we extend this theory from the scale of neurons to functional magnetic resonance imaging (fMRI) and show that noise correlations between heterogeneous populations of voxels (i.e., voxels selective for different stimulus variables) contribute to the success of MVPA. Specifically, decoding performance is enhanced when voxels with high vs. low noise correlations (measured during rest or in the background of the task) are selected during classifier training. Conversely, voxels that are strongly selective for one class in a GLM or that receive high classification weights in MVPA tend to exhibit high noise correlations with voxels selective for the other class being discriminated against. Furthermore, we use simulations to show that this is a general property of fMRI data and that selectivity and noise correlations can have distinguishable influences on decoding. Taken together, our findings demonstrate that if there is signal in the data, the resulting above-chance classification accuracy is modulated by the magnitude of noise correlations.
Type:
Dataset
Issue Date:
August 2017

31. Spike Trains of Retinal Ganglion Cells Viewing a Repeated Natural Movie

Author(s):
Berry II, Michael J.
Abstract:
This archive contains spike trains simultaneously recorded from ganglion cells in the tiger salamander retina with a multi-electrode array while viewing a repeated natural movie clip. These data have been analyzed in previous papers, notably Puchalla et al. Neuron 2005 and Schneidman et al. Nature 2006.
Type:
Dataset
Issue Date:
8 March 2022

32. The Structured `Low Temperature' Phase of the Retinal Population Code

Author(s):
Ioffe, Mark Lev; Berry II, Michael J.
Abstract:
Recent advances in experimental techniques have allowed the simultaneous recordings of populations of hundreds of neurons, fostering a debate about the nature of the collective structure of population neural activity. Much of this debate has focused on the empirical findings of a phase transition in the parameter space of maximum entropy models describing the measured neural probability distributions, interpreting this phase transition to indicate a critical tuning of the neural code. Here, we instead focus on the possibility that this is a first-order phase transition which provides evidence that the real neural population is in a `structured', collective state. We show that this collective state is robust to changes in stimulus ensemble and adaptive state. We find that the pattern of pairwise correlations between neurons has a strength that is well within the strongly correlated regime and does not require fine tuning, suggesting that this state is generic for populations of 100+ neurons. We find a clear correspondence between the emergence of a phase transition, and the emergence of attractor-like structure in the inferred energy landscape. A collective state in the neural population, in which neural activity patterns naturally form clusters, provides a consistent interpretation for our results.
Type:
Dataset
Issue Date:
2017

35. Sound velocities in shock-synthesized stishovite to 72 GPa

Author(s):
Berryman, Eleanor J.; Winey, J. M.; Gupta, Yogendra M.; Duffy, Thomas S.
Abstract:
Stishovite (rutile-type SiO2) is the archetype of dense silicates and may occur in post-garnet eclogitic rocks at lower-mantle conditions. Sound velocities in stishovite are fundamental to understanding its mechanical and thermodynamic behavior at high pressure and temperature. Here, we use plate-impact experiments combined with velocity interferometry to determine the stress, density, and longitudinal sound speed in stishovite formed during shock compression of fused silica at 44 GPa and above. The measured sound speeds range from 12.3(8) km/s at 43.8(8) GPa to 9.8(4) km/s at 72.7(11) GPa. The decrease observed at 64 GPa reacts a decrease in the shear modulus of stishovite, likely due to the onset of melting. By 72 GPa, the measured sound speed agrees with the theoretical bulk sound speed indicating loss of all shear stiffness due to complete melting. Our sound velocity results provide direct evidence for shock-induced melting, in agreement with previous pyrometry data.
Type:
Dataset
Issue Date:
2019

36. Distinct cytoskeletal proteins define zones of enhanced cell wall synthesis in Helicobacter pylori

Author(s):
Taylor, Jenny A.; Bratton, Benjamin P.; Sichel, Sophie R.; Blair, Kris M.; Jacobs, Holly M.; DeMeester, Kristen E.; Kuru, Erkin; Gray, Joe; Biboy, Jacob; VanNieuwenhze, Michael S.; Vollmer, Waldemar; Grimes, Catherine L.; Shaevitz, Joshua W.; Salama, Nina R.
Abstract:
Helical cell shape is necessary for efficient stomach colonization by Helicobacter pylori, but the molecular mechanisms for generating helical shape remain unclear. We show that the helical centerline pitch and radius of wild-type H. pylori cells dictate surface curvatures of considerably higher positive and negative Gaussian curvatures than those present in straight- or curved-rod bacteria. Quantitative 3D microscopy analysis of short pulses with either N-acetylmuramic acid or D-alanine metabolic probes showed that cell wall growth is enhanced at both sidewall curvature extremes. Immunofluorescence revealed MreB is most abundant at negative Gaussian curvature, while the bactofilin CcmA is most abundant at positive Gaussian curvature. Strains expressing CcmA variants with altered polymerization properties lose helical shape and associated positive Gaussian curvatures. We thus propose a model where CcmA and MreB promote PG synthesis at positive and negative Gaussian curvatures, respectively, and that this patterning is one mechanism necessary for maintaining helical shape.
Type:
Dataset and Image
Issue Date:
April 2019

37. Attention and awareness in the dorsal attention network

Author(s):
Wilterson, Andrew; Nastase, Samuel; Bio, Branden; Guterstam, Arvid; Graziano, Michael
Abstract:
The attention schema theory (AST) posits a specific relationship between subjective awareness and attention, in which awareness is the control model that the brain uses to aid in the endogenous control of attention. We proposed that the right temporoparietal junction (TPJ) is involved in that interaction between awareness and attention. In previous experiments, we developed a behavioral paradigm in human subjects to manipulate awareness and attention. The paradigm involved a visual cue that could be used to guide a shift of attention to a target stimulus. In task 1, subjects were aware of the visual cue, and their endogenous control mechanism was able to use the cue to help control attention. In task 2, subjects were unaware of the visual cue, and their endogenous control mechanism was no longer able to use it to control attention, even though the cue still had a measurable effect on other aspects of behavior. Here we tested the two tasks while scanning brain activity in human volunteers. We predicted that the right TPJ would be active in relation to the cue in task 1, but not in task 2. This prediction was confirmed. The right TPJ was active in relation to the cue in task 1; it was not measurably active in task 2; the difference was significant. In our interpretation, the right TPJ is involved in a complex interaction in which awareness aids in the control of attention.
Type:
Dataset
Issue Date:
2020

38. Integrative Mechanisms of Social Attention

Author(s):
Bio, Branden; Graziano, Michael
Abstract:
Monitoring the attention of others is fundamental to social cognition. Most of the literature on the topic assumes that our social cognitive machinery is tuned specifically to the gaze direction of others as a proxy for attention. This standard assumption reduces attention to an externally visible parameter. Here we show that this assumption is wrong and a deeper, more meaningful representation is involved. We presented subjects with two cues about the attentional state of a face: direction of gaze and emotional expression. We tested whether people relied predominantly on one cue, the other, or both. If the traditional view is correct, then the gaze cue should dominate. Instead, people employed a variety of strategies, some relying on gaze, some on expression, and some on an integration of cues. We also assessed people’s social cognitive ability using two, independent, standard tests. If the traditional view is correct, then social cognitive ability, as assessed by the independent tests, should correlate with the degree to which people successfully use the gaze cue to judge the attention state of the face. Instead, social cognitive ability correlated best with the degree to which people successfully integrated the cues together, instead of with the use of any one specific cue. The results suggest a rethink of a fundamental component of social cognition: monitoring the attention of others involves constructing a deep model that is informed by a combination of cues. Attention is a rich process and monitoring the attention of others involves a similarly rich representation.
Type:
Dataset
Issue Date:
22 July 2021

39. Data for Coarse-grained Intrinsically Disordered Proteins

Author(s):
Webb, Michael; Patel, Roshan; Borca, Carlos
Abstract:
This distribution compiles numerous physical properties for 2,585 intrinsically disordered proteins (IDPs) obtained by coarse-grained molecular dynamics simulation. This combination comprises "Dataset A" as reported in "Featurization strategies for polymer sequence or composition design by machine learning" by Roshan A. Patel, Carlos H. Borca, and Michael A. Webb (DOI: 10.1039/D1ME00160D). The specific IDP sequences are sourced from version 9.0 of the DisProt database. The simulations were performed using the LAMMPS molecular dynamics engine. The interactions used for simulation are obtained from R. M. Regy , J. Thompson , Y. C. Kim and J. Mittal , Improved coarse-grained model for studying sequence dependent phase separation of disordered proteins, Protein Sci., 2021, 1371 —1379.
Type:
Dataset
Issue Date:
6 May 2022

40. Data on Enzyme Activity Retention in glucose oxidase, lipase, and horseradish peroxidase

Author(s):
Webb, Michael; Patel, Roshan; Gormley, Adam; Tamasi, Matt; Borca, Carlos; Kosuri, Shashank
Abstract:
This distribution contains experimentally measured data for the extent of retained enzyme activity post thermal stressing for three distinct enzymes: glucose oxidase, lipase, and horseradish peroxidase. The data is used to form conclusions and develop machine learning models as reported in the publication "Machine Learning on a Robotic Platform for the Design of Polymer-Protein Hybrids" by Matthew Tamasi, Roshan Patel, Carlos Borca, Shashank Kosuri, Heloise Mugnier, Rahul Upadhya, N. Sanjeeva Murthy, Michael Webb*, and Adam Gormley. Details regarding the experimental protocols are reported in the aforementioned paper but are briefly discussed in the README.
Type:
Dataset
Issue Date:
17 May 2022

42. Data from a flume experiment of passive scalar diffusion within vegetation canopies using laser-induced fluorescence

Author(s):
Ghannam, Khaled; Poggi, Davide; Katul, Gabriel; Bou-Zeid, Elie
Abstract:
This dataset is a sequence of laser-induced fluorescence images of a dye injected in a channel flow with canopy-like stainless steel rods simulating a vegetation canopy stand. The data is acquired close to the channel bottom at z/h=0.2, where z is the height referenced to the channel bed and h is the canopy height. The dataset provides spatial distribution of scalar concentration in a plane parallel to the channel bed. The data has been used (but the data itself has not been published or available to the public) in previous work. The references are: Ghannam, K., Poggi, D., Porporato, A., & Katul, G. (2015). The spatio-temporal statistical structure and ergodic behaviour of scalar turbulence within a rod canopy. Boundary-Layer Meteorology,157(3), 447–460. Ghannam, K, Poggi, D., Bou-Zeid, E., Katul, G. (2020). Inverse cascade evidenced by information entropy of passive scalars in submerged canopy flows. Geophysical Research Letters (accepted).
Type:
Dataset
Issue Date:
22 April 2020

43. A dual-mechanism antibiotic kills Gram-negative bacteria and avoids drug resistance

Author(s):
Martin, James K; Sheehan, Joseph P; Bratton, Benjamin P; Moore, Gabriel M; Mateus, André; Li, Sophia Hsin-Jung; Kim, Hahn; Rabinowitz, Joshua D; Typas, Athanasios; Savitski, Mikhail M; Wilson, Maxwell Z; Gitai, Zemer
Abstract:
The rise of antibiotic resistance and declining discovery of new antibiotics have created a global health crisis. Of particular concern, no new antibiotic classes have been approved for treating Gram-negative pathogens in decades. Here, we characterize a compound, SCH-79797, that kills both Gram-negative and Gram-positive bacteria through a unique dual-targeting mechanism of action (MoA) with undetectably-low resistance frequencies. To characterize its MoA, we combined quantitative imaging, proteomic, genetic, metabolomic, and cell-based assays. This pipeline demonstrates that SCH-79797 has two independent cellular targets, folate metabolism and bacterial membrane integrity, and outperforms combination treatments in killing MRSA persisters. Building on the molecular core of SCH-79797, we developed a derivative, Irresistin-16, with increased potency and showed its efficacy against Neisseria gonorrheae in a mouse vaginal infection model. This promising antibiotic lead suggests that combining multiple MoAs onto a single chemical scaffold may be an underappreciated approach to targeting challenging bacterial pathogens.
Type:
Dataset
Issue Date:
20 May 2020

44. Reductions in Retrieval Competition Predict the Benefit of Repeated Testing

Author(s):
Rafidi, Nicole S; Hulbert, Justin C; Brooks, Paula P; Norman, Kenneth A
Abstract:
Repeated testing (as opposed to repeated study) leads to improved long-term memory retention, but the mechanism underlying this improvement remains controversial. In this work, we test the hypothesis that retrieval practice benefits subsequent recall by reducing competition from related memories. This hypothesis implies that the degree of reduction in competition between retrieval practice attempts should predict subsequent memory for the practiced items. To test this prediction, we collected electroencephalography (EEG) data across two sessions. In the first session, participants practiced selectively retrieving exemplars from superordinate semantic categories (high competition), as well as retrieving the names of the superordinate categories from exemplars (low competition). In the second session, participants repeatedly studied and were then tested on Swahili-English vocabulary. One week after session two, participants were again tested on the vocabulary. We trained a within-subject classifier on the data from session one to distinguish high and low competition states. We then used this classifier to measure competition across multiple retrieval practice attempts in the second session. The degree to which competition decreased for a given vocabulary word predicted whether that item was subsequently remembered in the third session. These results are consistent with the hypothesis that repeated testing improves retention by reducing competition.
Type:
Dataset
Issue Date:
April 2018

45. Deep Potential training data for crystalline and disordered TiO2 phases

Author(s):
Calegari Andrade, Marcos; Selloni, Annabella
Abstract:
Data set used to train a Deep Potential (DP) model for crystalline and disordered TiO2 phases. Training data contain atomic forces, potential energy, atomic coordinates and cell tensor. Energy and forces were evaluated with the density functional SCAN, as implemented in Quantum-ESPRESSO. Atomic configurations of crystalline systems were generated by random perturbation of atomic positions (0-0.3 A) and cell tensor (1-10%). Amorphous TiO2 was explored by DP molecular dynamics (DPMD) at temperatures in the range 300−2500 K and pressure in the range 0−81 GPa.
Type:
Dataset
Issue Date:
9 October 2020

46. Deep Potential training data for subcritical and supercritical water

Author(s):
Calegari Andrade, Marcos; Ko, Hsin-Yu; Car, Roberto
Abstract:
Data set used to train a Deep Potential (DP) model for subcritical and supercritical water. Training data contain atomic forces, potential energy, atomic coordinates and cell tensor. Energy and forces were evaluated with the density functional SCAN. Atomic configurations were extracted from DP molecular dynamics at P = 250 bar and T = 553, 623, 663, 733 and 823 K. Input files used to train the DP model are also provided.
Type:
Dataset
Issue Date:
19 August 2020

48. Data from "Manifestations of metastable criticality in glassy water-like models detected by large-scale structural properties"

Author(s):
Gartner III, Thomas E.; Torquato, Salvatore; Car, Roberto; Debenedetti, Pablo G.
Abstract:
This dataset contains all data related to the publication "Manifestations of metastable criticality in glassy water-like models detected by large-scale structural properties" by Gartner et al., in preparation 2020. In this work, we used molecular dynamics simulations to explore the relationship between water's polyamorphism (multiple amorphous solid states) and its hypothesized liquid-liquid transition. Using the TIP4P/2005 molecular model of water, we found a surprising signature of water's liquid-liquid critical point in the long-range structure of water's amorphous solid states formed by isobaric cooling at different pressures. This structural signature was absent in two other systems that lack a critical point. This dataset contains molecular dynamics simulation trajectories, as well as processed data, analysis codes, and image files used in the publication.
Type:
Dataset
Issue Date:
3 December 2020

49. Data from "Melting curves of ice polymorphs in the vicinity of the liquid-liquid critical point"

Author(s):
Piaggi, Pablo M; Gartner, Thomas E; Car, Roberto; Debenedetti, Pablo G
Abstract:
The possible existence of a liquid-liquid critical point in deeply supercooled water has been a subject of debate in part due to the challenges associated with providing definitive experimental evidence. Pioneering work by Mishima and Stanley [Nature 392, 164 (1998) and Phys.~Rev.~Lett. 85, 334 (2000)] sought to shed light on this problem by studying the melting curves of different ice polymorphs and their metastable continuation in the vicinity of the expected location of the liquid-liquid transition and its associated critical point. Based on the continuous or discontinuous changes in slope of the melting curves, Mishima suggested that the liquid-liquid critical point lies between the melting curves of ice III and ice V. Here, we explore this conjecture using molecular dynamics simulations with a purely-predictive machine learning model based on ab initio quantum-mechanical calculations. We study the melting curves of ices III, IV, V, VI, and XIII using this model and find that the melting lines of all the studied ice polymorphs are supercritical and do not intersect the liquid-liquid transition locus. We also find a pronounced, yet continuous, change in slope of the melting lines upon crossing of the locus of maximum compressibility of the liquid. Finally, we analyze critically the literature in light of our findings, and conclude that the scenario in which melting curves are supercritical is favored by the most recent computational and experimental evidence. Thus, although the preponderance of experimental and computational evidence is consistent with the existence of a second critical point in water, the behavior of the melting lines of ice polymorphs does not provide strong evidence in support of this viewpoint, according to our calculations.
Type:
Dataset
Issue Date:
February 2023

50. Data from "Signatures of a liquid-liquid transition in an ab initio deep neural network model for water"

Author(s):
Gartner, Thomas III; Zhang, Linfeng; Piaggi, Pablo; Car, Roberto; Panagiotopoulos, Athanassios; Debenedetti, Pablo
Abstract:
This dataset contains all data related to the publication "Signatures of a liquid-liquid transition in an ab initio deep neural network model for water", by Gartner et al., 2020. In this work, we used neural networks to generate a computational model for water using high-accuracy quantum chemistry calculations. Then, we used advanced molecular simulations to demonstrate evidence that suggests this model exhibits a liquid-liquid transition, a phenomenon that can explain many of water's anomalous properties. This dataset contains links to all software used, all data generated as part of this work, as well as scripts to generate and analyze all data and generate the plots reported in the publication.
Type:
Dataset
Issue Date:
July 2020